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In Looker Studio, however, this feature has limitations that could slow down your reporting and affect the accuracy of your data. \u003C/p>","\u003Cp>With data blending in Looker Studio, you can create charts, tables, and rules that combine data from several data sources. \u003C/p>\u003Cp>For example, you can blend customer information and order details and visualize the information in a one Looker Studio table. \u003C/p>\u003Cp>You can present combined data from your Google Ads and Google Analytics accounts in a single chart to see the unified performance of your marketing campaign.\u003C/p>\u003Cp>You can also blend data from different BigQuery tables.\u003C/p>\u003Cp>But before we go in more depth, let's quickly explain the difference between a blend and a data source. \u003C/p>\u003Ch2>\u003Cstrong>Blends vs. Data Sources\u003C/strong>\u003C/h2>\u003Cp>Blending data creates a resource known as a blend. Blends are similar to data sources because they also provide data for charts and tables, but they differ from data sources in several ways: \u003C/p>\u003Cul>\u003Cli>Blends contain information from multiple data sources.\u003C/li>\u003Cli>Blends in Looker Studio are always embedded into the report in which they're created. You can’t reuse a blend across reports. \u003C/li>\u003Cli>Metrics in the underlying data source become unaggregated numeric dimensions. \u003C/li>\u003Cli>Blends don’t have data freshness and credential settings of their own. These come from the underlying data sources. \u003C/li>\u003C/ul>\u003Ch2>How to Blend Data in Looker Studio?\u003C/h2>\u003Cp>We’ll start by creating a report in which we will connect Google Analytics 4 and YouTube to present data from both platforms in one place. \u003C/p>\u003Cp>Then, we’re going to blend data from two different sources in a single chart.\u003C/p>\u003Cp>\u003Ca href=\"https://www.linkedin.com/in/benmangold/\" target=\"_blank\" rel=\"noopener noreferrer\">Benjamin Mangold\u003C/a>, Co-Founder of \u003Ca href=\"https://www.lovesdata.com/\" target=\"_blank\" rel=\"noopener noreferrer\">Loves Data\u003C/a>, explains the process in his video:\u003C/p>\u003Cfigure class=\"media\">\u003Cdiv data-oembed-url=\"https://www.youtube.com/watch?v=qQfUwClQBPk\">\u003Cdiv style=\"position: relative; padding-bottom: 100%; height: 0; padding-bottom: 56.2493%;\">\u003Ciframe src=\"https://www.youtube.com/embed/qQfUwClQBPk\" style=\"position: absolute; width: 100%; height: 100%; top: 0; left: 0;\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen=\"\">\u003C/iframe>\u003C/div>\u003C/div>\u003C/figure>\u003Cp> \u003C/p>\u003Cp>Let’s start by creating a report in Looker Studio. \u003C/p>\u003Ch3>Step 1: Create a report \u003C/h3>\u003Col style=\"list-style-type:decimal;\">\u003Cli>Open Looker Studio and click \u003Cstrong>Create\u003C/strong> > \u003Cstrong>Report\u003C/strong>.  \u003C/li>\u003Cli>Select Google Analytics 4 as a data source. Choose the account and property you want to connect. Click \u003Cstrong>Add\u003C/strong> > \u003Cstrong>Add to Report\u003C/strong>.\u003C/li>\u003Cli>To keep things simple, we’ll replace the default table widget with a scorecard. Click on the table and click on the scorecard type in the sidebar. \u003C/li>\u003Cli>Change the scorecard metric to “Total users”. Now this scorecard shows us the total number of users on the website. \u003C/li>\u003Cli>To also include data from YouTube, we need to add another data source. Click \u003Cstrong>Add data\u003C/strong> button at the bottom of the interface. Select the YouTube account you want to connect and repeat the \u003Cstrong>Add\u003C/strong> > \u003Cstrong>Add to Report\u003C/strong> steps. We can now use this new data source in our dashboard. \u003C/li>\u003Cli>Click \u003Cstrong>Add a chart\u003C/strong> and pick a \u003Cstrong>Scorecard\u003C/strong> from the drop-down menu. Make sure that the YouTube source is selected in the sidebar Data Sources field. \u003C/li>\u003C/ol>\u003Cp>Change the metrics on the scorecard to show the total number of “Views”. \u003C/p>\u003Cp>Done! We now have a dashboard that includes data from two data sources. \u003C/p>\u003Cp>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/Screenshot_2024_12_02_at_15_55_34_fb83b12d16.png\" alt=\"Looker Studio Report - Data Blending in Looker Studio\">\u003C/p>\u003Cp> \u003C/p>\u003Cp>Now, we'll add another GA4 data source and create a blend\u003C/p>\u003Ch3>\u003Cstrong>Step 2: Start blending data\u003C/strong>\u003C/h3>\u003Cp>Click \u003Cstrong>Add data\u003C/strong> in the bottom corner and select Google Analytics 4. Choose the account and property you want to connect. Click \u003Cstrong>Add\u003C/strong> > \u003Cstrong>Add to Report\u003C/strong>.\u003C/p>\u003Cp>We can now add another chart to our report. Click \u003Cstrong>Add a chart\u003C/strong> and pick a \u003Cstrong>Time series. \u003C/strong>\u003C/p>\u003Cp>Under the Setup tab, we can see that this chart uses the GA4 source we’ve just added. \u003C/p>\u003Cp>Click \u003Cstrong>Blend Data \u003C/strong>to create a new blended data source. \u003C/p>\u003Cp>To make a blended data source, you’ll need a “key” also called a “joint condition” that is available in both sources. It’s a piece of information available in both data sets. \u003C/p>\u003Cp>For example, we can use the dimension of “Date” to combine data. We can then present the number of users from both data sources by the date. \u003C/p>\u003Cp>We can see our existing data source on the left. Click \u003Cstrong>Join another table\u003C/strong>. \u003C/p>\u003Cp>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/Screenshot_2024_12_02_at_17_51_23_7256443023.png\" alt=\"Screenshot 2024-12-02 at 17.51.23.png\">\u003C/p>\u003Cp>Select the GA4 property you added first. Now, you need to choose the dimensions and metrics you want to combine. \u003C/p>\u003Cp>The “Date” is already selected as a dimension. For the metrics, we’ll add “Total users” and “Sessions” for both data sources.\u003C/p>\u003Cp>\u003Ci>\u003Cstrong>Pro tip\u003C/strong>: Name each table used in your blended data source. This will help you if you decide to create any calculated fields in your report. Calculated fields let you create custom metrics and dimensions in Looker Studio. \u003C/i>\u003C/p>\u003Cp>To name a table, select the default text at the top that says “Table Name”.\u003C/p>\u003Cp>Every blend in Looker Studio can have up to 5 tables. This is definitely a limitation, but we'll get back to it in a bit. \u003C/p>\u003Cp>For now, let's finish our blend. \u003C/p>\u003Ch3>\u003Cstrong>Step 3: Join data from two sources\u003C/strong>\u003C/h3>\u003Cp>You can see the metrics and dimensions for our blended data source on the right, together with corresponding table names. \u003C/p>\u003Cp>The next thing you need to decide is how to join data from the tables.  \u003C/p>\u003Cp>Click \u003Cstrong>Configure join\u003C/strong> in the middle. \u003C/p>\u003Cp>There are five ways you can join tables.  \u003C/p>\u003Cp>With these options, you can control what happens if data is missing from one of the tables. For example, if there is data for a particular day in one table but not the other. \u003C/p>\u003Cul>\u003Cli>\u003Cstrong>Left outer\u003C/strong>: If you use “Left Outer”, all the rows from the table on the left will be used, along with any of the matching rows from the table on the right. If there is a row missing in the table on the right, it won’t be combined in your blended source. \u003C/li>\u003Cli>\u003Cstrong>Right outer\u003C/strong>: If you use “Right Outer”, any missing rows from the table on the left won’t be used. \u003C/li>\u003Cli>\u003Cstrong>Inner\u003C/strong>: Only rows that are available in both tables will be used.  \u003C/li>\u003Cli>\u003Cstrong>Full outer\u003C/strong>: All rows from both tables will be used. \u003C/li>\u003Cli>\u003Cstrong>Cross\u003C/strong>: This option combines all available rows from both data sources. \u003C/li>\u003C/ul>\u003Cp>For our blended data source, let’s select “Full outer”. \u003C/p>\u003Cp>Now, we need to select the \u003Cstrong>Join condition\u003C/strong> or data key to join the data together. Choose the “Date” dimension. Click \u003Cstrong>Save\u003C/strong>.  \u003C/p>\u003Cp>You can now name the blended data source. For example, “GA4 Combined”. Click \u003Cstrong>Save\u003C/strong>.  \u003C/p>\u003Ch3>\u003Cstrong>Step 4: Combine metrics\u003C/strong>\u003C/h3>\u003Cp>We can see our new blended data source is applied to our chart. Each metric is still separate. \u003C/p>\u003Cp>We have two metrics for “Sessions” and two metrics for “Total users”. \u003C/p>\u003Cp>To create a combined metric, we need to create a calculated field. \u003C/p>\u003Cp>Under the \u003Cstrong>Setup\u003C/strong> tab, select \u003Cstrong>Add metric\u003C/strong> > \u003Cstrong>Create field\u003C/strong>. \u003C/p>\u003Cp>Let’s call it “Total Users Combined”. For the formula start typing “Total users” and then select the metric from the first table. Enter a “\u003Cstrong>+\u003C/strong>” sign and search for “Total users” again and select the metric from the second table. Click \u003Cstrong>Apply\u003C/strong>. \u003C/p>\u003Cp>You can now see the total number of users combined from both GA4 properties in the time series.   \u003C/p>\u003Ch2>5 Limitations of Data Blending in Looker Studio\u003C/h2>\u003Cp>There’s no doubt that \u003Ca href=\"https://whatagraph.com/blog/articles/data-blending\">data blending is a great feature\u003C/a>. Blended data can help you see the bigger picture behind your data and reveal underlying trends. However, there are several Looker Studio limitations that could slow down your report or make blended data in Looker Studio inaccurate.\u003C/p>\u003Ch3>1. A limited number of blended sources\u003C/h3>\u003Cp>Remember when we said that one blend can have up to 5 tables? That’s right. You can’t add more than five data sources and more than 10 dimensions from a single data source. Both numbers might seem reasonable, but they're not always enough. \u003C/p>\u003Cp>Agencies often need to fetch data from many different client sources using \u003Ca href=\"https://whatagraph.com/blog/articles/marketing-data-connectors\">multiple marketing data connectors\u003C/a>. For example, ad metrics from several PPC apps, sales data from an e-commerce platform, customer data from the CRM, and different information from several more spreadsheets. \u003C/p>\u003Cp>In any case, when you hit the limit, you must split the data into several reports, which makes it harder to get the full picture of your performance. \u003C/p>\u003Ch3>2. Slow loading and processing times\u003C/h3>\u003Cp>You might have noticed that the platform sometimes takes more time to load your data, even without blending. \u003C/p>\u003Cp>Indeed, \u003Ca href=\"https://whatagraph.com/blog/articles/looker-studio-slow\">Looker Studio is slow\u003C/a> for several reasons, and it tends to get even slower when you start using it with multiple data sources, especially several sources at the same time. \u003C/p>\u003Cp>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/Data_c8456a1b88.png\" alt=\"google extract data connector\">\u003C/p>\u003Cp>For each source, Looker Studio needs to connect to a different API, which demands additional computing power that isn't always available. \u003C/p>\u003Cp>Blending two sources usually works fine, but the more sources you add, the slower your dashboard gets. \u003C/p>\u003Ch3>3. Blends are not reusable\u003C/h3>\u003Cp>In Looker Studio, blends are always embedded into the report in which they're created. There’s no way to make a blend reusable across reports. If you copy the report, the blends are copied into the new report, so your charts will continue to present the blended data. \u003C/p>\u003Ch3>4. Depends on third-party connectors \u003C/h3>\u003Cp>Third-party connectors are necessary for integrating data from various sources. However, these connectors are sometimes too complex, unreliable, or have limited capabilities. \u003C/p>\u003Cp>An old adage says “A man is only as good as his tools”. In the case of Looker Studio, third-party tools can sometimes cause problems. \u003C/p>\u003Cp>Google Analytics, Google Ads, and other data sources from the Google ecosystem have one connection per property, which includes all data fields. \u003C/p>\u003Cp>On the other hand, third-party connectors often let you choose between three different menus, each connecting to a different segment of the tool’s data. This complicates your data blending processes. \u003C/p>\u003Cp>Reporting on an enterprise property or managing an agency portfolio of clients can become a nightmare. \u003C/p>\u003Cp>That's especially true if you need to connect or blend all data sources  – not to mention trying to replicate the report with blended data. \u003C/p>\u003Cp>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/thrid_party_connectors_87474828c6.png\" alt=\"Looker Studio Connectors\">\u003C/p>\u003Cp style=\"text-align:right;\">\u003Ci>Connectors from different vendors may not have the same performance\u003C/i>\u003C/p>\u003Cp>Also, third-party connectors may not be as reliable or consistent as native Looker Studio connectors. This can cause data errors, connectivity issues, or downtimes that disrupt your data analysis and reporting performance. \u003C/p>\u003Cp>Ironically, clunky spreadsheets or BigQuery-hosted datasets are often the best way to connect non-Google data sources to Looker Studio. \u003C/p>\u003Cp>This dependence on third-party connectors is not something that plagues Looker Studio alone. \u003C/p>\u003Cp>Many \u003Ca href=\"https://whatagraph.com/blog/articles/data-transformation-tools\">data transformation tools\u003C/a> use third-party connectors to connect data from scattered sources. To avoid this problem, consider using a tool with pre-built or native integrations. \u003C/p>\u003Ch3>5. Steep learning curve\u003C/h3>\u003Cp>Looker Studio is not one of those tools you can just pick up and use. Its unique modeling language requires that you have at least a basic understanding of coding — primarily languages like SQL. \u003C/p>\u003Cp>This can be challenging for agencies or companies without technical expertise or a dedicated data team. \u003C/p>\u003Cp>And if you run into a problem or unknown, you’re pretty much left to yourself, as customer support only applies to users with a Google Cloud support plan and Looker Studio Pro subscription. \u003C/p>\u003Ch2>How to Overcome Limitations of Data Blending in Looker Studio with Whatagraph\u003C/h2>\u003Cp>Whatagraph is one platform to connect, \u003Ca href=\"https://whatagraph.com/organize\">organize\u003C/a>, visualize, and share all your marketing data. \u003C/p>\u003Cp>Designed to replace multiple complex data tools with one intuitive platform, Whatagraph also gives you the easiest way to blend your data. \u003C/p>\u003Cp>How?\u003C/p>\u003Cp>Once you connect your data sources, you have the option to organize your data before analyzing it further. \u003C/p>\u003Ch3>No limits on blending data\u003C/h3>\u003Cp>With Whatagraph, there’s no limit on how many sources or dimensions you can blend. Even better, the accuracy and reliability of your blends don’t depend on third-party connectors. \u003C/p>\u003Cp>Whatagraph has fully managed integrations with 55+ marketing platforms, including web analytics, social media, paid advertising, SEO, e-commerce, email marketing, and CRM tools. \u003C/p>\u003Cp>This means that data in your blends is reliable, whether you use Google- or non-Google-based marketing apps. \u003C/p>\u003Cp>But that’s not all. Apart from these integrations, you can connect any data source via a Custom API or by using Google Sheets or BigQuery as a source. \u003C/p>\u003Ch3>Faster processing times\u003C/h3>\u003Cp>While Looker Studio can get very slow even with two data sources in a blend, Whatagraph easily handles dozens of report pages and unlimited widgets and sources. \u003C/p>\u003Cp>Thanks to a recent update to \u003Ca href=\"https://cloud.google.com/kubernetes-engine\" target=\"_blank\" rel=\"noopener noreferrer\">Google Kubernetes Engine\u003C/a>, even very heavy widgets with 180 configurations now take less than 10 seconds to load. \u003C/p>\u003Ch3>Easily review your blends to make sure the report is accurate\u003C/h3>\u003Cp>In Whatagraph, you can quickly review the dimensions and metrics for each blend you’re creating or editing. This way, you can make sure there are no duplicates and that nothing goes into the blend that shouldn’t. \u003C/p>\u003Cp>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/blend_example_3_3fba888fa1.png\" alt=\"blend_example_3.png\">\u003C/p>\u003Ch3>Save any blends to reuse and edit\u003C/h3>\u003Cp>Everything you create in Whatagraph, from metrics to whole reports, can be saved and reused as a template. This also applies to data blending. \u003C/p>\u003Cp>You can easily reuse and edit all data blends, formulas, custom dimensions, and metrics.\u003C/p>\u003Ch3>Anyone on the team can handle advanced analytics tasks \u003C/h3>\u003Cp>Whatagraph has a user-friendly UX/UI across the whole platform, making any data management task easy without technical knowledge. \u003C/p>\u003Ch2>How to Blend Data in Whatagraph\u003C/h2>\u003Cp>Now, we’ll explain how to blend data effectively using Whatagraph’s no-code process.  \u003C/p>\u003Ch3>Example 1: Blending sources\u003C/h3>\u003Cp>In this example, we’ll create a simple blend of Facebook Ads and Google Ads to get the number of impressions and clicks from both sources in one table. \u003C/p>\u003Cp>\u003Cstrong>Step 1:\u003C/strong> We start with a report with connected Facebook Ads and Google Ads. Without blended data, the report splits the performance of two sources into two tables. Now, we’ll blend these two sources.  \u003C/p>\u003Cp>\u003Cstrong>Step 2:\u003C/strong> Click the \u003Cstrong>Sources\u003C/strong> tab and then \u003Cstrong>Add new data\u003C/strong>. Choose the \u003Cstrong>Blended sources\u003C/strong> tab and click \u003Cstrong>Crete a blended source\u003C/strong> button.  \u003C/p>\u003Cp>\u003Cstrong>Step 3\u003C/strong>:  A blending window will appear. Start by selecting the channels and the specific data sources. Select Facebook Ads and Google Ads.\u003C/p>\u003Cp>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/data_blending_whatagraph_660538108a.png\" alt=\"Blending sources in Whatagraph\">\u003C/p>\u003Cp>\u003Cstrong>Step 4\u003C/strong>: Choose \u003Ci>Year, month\u003C/i> as the dimensions for both sources and \u003Ci>Impressions\u003C/i> and \u003Ci>Clicks\u003C/i>/\u003Ci>Clicks (All)\u003C/i> as metrics.\u003C/p>\u003Cp>\u003Cstrong>Step 5:\u003C/strong> Select the join. For this blend, take the full outer join. Select \u003Ci>Year, month\u003C/i> as the join key for both sources and click \u003Cstrong>Save setup\u003C/strong>.\u003C/p>\u003Cp>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/select_join_key_b6d27b0318.png\" alt=\"Select join key\">\u003C/p>\u003Cp>\u003Cstrong>Step 6:\u003C/strong> Give your blend a name and a short description. It’s a good practice to use a recognizable name so other users can easily find it. \u003C/p>\u003Cp>Click \u003Cstrong>Create a blend,\u003C/strong> and your job is done. \u003C/p>\u003Cp>You can use the blend you created as a source for any report. Select the newly created blend, drag a widget to the report, and pick the metrics from both sources. \u003C/p>\u003Cp>This example is just one of many \u003Ca href=\"https://whatagraph.com/blog/articles/data-blending\">data blending use cases\u003C/a> available in Whatagraph.     \u003C/p>\u003Ch3>Example 2: Creating formulas on a blended source level\u003C/h3>\u003Cp>In this example, we’ll create a custom formula for total Impressions from Facebook Ads and Google Ads sources we just blended.\u003C/p>\u003Cp>\u003Cstrong>Step 1\u003C/strong>: Click on the widget with a connected blended source. Click \u003Cstrong>Add new\u003C/strong> in the metrics selection, and then \u003Cstrong>Create new metric\u003C/strong>.\u003C/p>\u003Cp>\u003Cstrong>Step 2\u003C/strong>: The Create metric view will open. Set the display name and description for your new metrics e.g. \u003Cstrong>Total Impressions\u003C/strong>. Since we want to use this new metric in a formula, under the rule type, select Formula. \u003C/p>\u003Cp>\u003Cstrong>Step 3\u003C/strong>: It immediately selects the data blend we used in this report. Now, we need to pick the individual metrics from the blend. Let’s say we want total impressions, so we’ll take Impressions from both channels.\u003C/p>\u003Cp>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/blend_example_4_844a7d2f38.png\" alt=\"blend_example_4.png\">\u003C/p>\u003Cp>\u003Cstrong>Step 4\u003C/strong>: Type the formula \u003Cstrong>A+B\u003C/strong>, where A is the designated label for Facebook Ads and B for Google Ads. Click \u003Cstrong>Create metric\u003C/strong>.\u003C/p>\u003Cp>Your new formula will automatically appear under the Metrics selection for the widget. \u003C/p>\u003Cp>Update the widget and your custom table now has Impressions from both channels as well as the column for total Impressions. \u003C/p>\u003Cp>You’ll find more data transformation use cases and ways to organize your marketing data in our \u003Ca href=\"https://whatagraph.com/blog/articles/marketing-data-transformation\">marketing data transformation article\u003C/a>.  \u003C/p>\u003Ch2>More Reasons to Pick Whatagraph Over Looker Studio\u003C/h2>\u003Cp>Looker Studio is a dashboarding tool created as a supplement to Looker – a much more complex platform for data modeling and governance. Although it lets users create visual reports of available data, Looker Studio lacks the advantages of an all-in-one marketing data platform. \u003C/p>\u003Cp>Let’s consider a few more reasons to use Whatagraph to manage your marketing data.\u003C/p>\u003Ch3>Fully managed integrations\u003C/h3>\u003Cp>Consistent experience and 30-minute refresh rate across all data sources you connect. More than 55 marketing platforms are supported, plus you can connect any data through Google Sheets, BigQuery, or Custom API.\u003C/p>\u003Ch3>Cross-channel reporting\u003C/h3>\u003Cp>Add any marketing channel to your report and compare the performance of different channels in a few clicks. Easily blend data and combine metrics from multiple sources. Pick any metrics from the blended sources and use custom formulas to add, divide, multiply, and take parts of it. \u003C/p>\u003Cfigure class=\"media\">\u003Cdiv data-oembed-url=\"https://www.youtube.com/watch?v=jPBSI3My-5s\">\u003Cdiv style=\"position: relative; padding-bottom: 100%; height: 0; padding-bottom: 56.2493%;\">\u003Ciframe src=\"https://www.youtube.com/embed/jPBSI3My-5s\" style=\"position: absolute; width: 100%; height: 100%; top: 0; left: 0;\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen=\"\">\u003C/iframe>\u003C/div>\u003C/div>\u003C/figure>\u003Ch3>Pre-made widgets\u003C/h3>\u003Cp>Save time visualizing your data with pre-made report building blocks. Drag and drop them to your report page. Resize them as needed, add more metrics, apply filters, and save them as widget templates. \u003C/p>\u003Ch3>Linked templates\u003C/h3>\u003Cp>Link reports to one template to \u003Cstrong>edit them all at once \u003C/strong>by editing the template and saving hours in the process. Change sources, text widgets, images, and company logos through multiple client reports instead of doing it one by one. \u003C/p>\u003Ch3>Customization & white-labeling\u003C/h3>\u003Cp>You can effortlessly change the default design of your reports and dashboard and apply a different theme that matches your or your client’s branding. \u003C/p>\u003Cul>\u003Cli>Remove Whatagraph’s logo,\u003C/li>\u003Cli>Choose a color scheme,\u003C/li>\u003Cli>Specify the company name,\u003C/li>\u003Cli>Enter the new domain name,\u003C/li>\u003Cli>Customize the reply-to-address,\u003C/li>\u003Cli>Specify who on your team is responsible for the report.\u003C/li>\u003C/ul>\u003Cp>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/white_label_c3f037378e.png\" alt=\"white_label.png\">\u003C/p>\u003Cp>This effectively means an agency can \u003Ca href=\"https://whatagraph.com/white-label\">customize reports\u003C/a> for each of their clients.\u003C/p>\u003Ch3>Report automation & review step\u003C/h3>\u003Cp>Once you create a report you can automate the way you deliver it. Choose the frequency, delivery days, time zone, and recipients, and automate the whole process. \u003C/p>\u003Cp>When the time comes for the next report, Whatagraph automatically refreshes the widgets with new data from connected sources and sends the next report on schedule. \u003C/p>\u003Cp>However, you can add the review step to double-check the report content before it gets sent. In this step, you can manually edit numbers, delete or add new widgets, change metrics, change media images, or add text comments. \u003C/p>\u003Ch3>No code data transfers\u003C/h3>\u003Cp>Whatagraph has an intuitive, no-code workflow to transfer data from multiple marketing platforms to Google BigQuery data warehouse. \u003C/p>\u003Cp>This gives you complete ownership of the marketing data they collect. Instead of having data in different locations, use data transfer to copy it all to a managed data warehouse. This way, you protect your data from deprecation, sampling, or changing policies of individual platforms.\u003C/p>\u003Ch3>Responsive customer support\u003C/h3>\u003Cp>Unless you pay more for Looker Studio Pro, you’re left to online discussion forums and Google’s help docs that are often difficult for a non-technical person to understand.\u003C/p>\u003Cp style=\"margin-left:0px;\">All Whatagraph pricing plans come with a dedicated Customer Success Manager and live chat support who reply to your questions within 4 minutes.\u003C/p>\u003Cp style=\"margin-left:0px;\">Your dedicated CSM can help you migrate data from your current platform, connect to channels and data sources, organize your data, and everything else to ensure you have the smoothest experience with Whatagraph. \u003C/p>\u003Ch2>Wrapping up\u003C/h2>\u003Cp>Addressing common challenges in Looker Studio data blending one by one might take much of your precious time. So perhaps the best way to solve them is to use a marketing data platform that doesn’t have these issues and is much more intuitive to use. \u003C/p>\u003Cp>Whatagraph has a data blending feature that is fast, reliable, user-friendly, and 100% no-code. \u003C/p>\u003Cp>\u003Ca href=\"https://whatagraph.com/book-a-call\">Book a call\u003C/a> with us and find out how Whatagraph can help you organize and report data on a scale!\u003C/p>","2024-03-06T15:49:56.834Z","2024-12-10T11:22:44.274Z","2024-03-06T17:18:43.628Z","2024-03-06",{"id":424,"name":425,"alternativeText":426,"caption":31,"width":427,"height":428,"formats":429,"hash":437,"ext":292,"mime":293,"size":438,"url":439,"previewUrl":31,"provider":296,"provider_metadata":31,"createdAt":440,"updatedAt":441},13532,"data_blending_in_looker_studio_1.png","Data Blending in Looker Studio – Here’s a Better Way",1880,1058,{"thumbnail":430},{"ext":292,"url":431,"hash":432,"mime":293,"name":433,"path":31,"size":434,"width":435,"height":436},"https://s3.us-east-2.amazonaws.com/whatagraph.com/thumbnail_Data_Blending_in_Looker_Studio_b87588aa26.png","thumbnail_Data_Blending_in_Looker_Studio_b87588aa26","thumbnail_Data_Blending _in_Looker_Studio.png",19.08,245,138,"Data_Blending_in_Looker_Studio_b87588aa26",85.88,"https://s3.us-east-2.amazonaws.com/whatagraph.com/Data_Blending_in_Looker_Studio_b87588aa26.png","2024-12-09T09:04:56.303Z","2024-12-09T09:05:09.804Z",{"id":5,"title":443,"slug":444,"subheading":445,"createdAt":446,"updatedAt":447,"publishedAt":448},"Data analytics","data-analytics","Delve into a goldmine of marketing data","2023-05-16T15:39:58.211Z","2025-06-04T14:37:44.968Z","2023-05-18T13:29:47.959Z",[450,455],{"id":98,"tagName":451,"slug":452,"createdAt":453,"updatedAt":454},"Data transformation","data-transformation","2024-09-02T07:57:58.356Z","2024-10-14T09:27:29.370Z",{"id":62,"tagName":456,"slug":457,"createdAt":458,"updatedAt":459},"Looker Studio","looker-studio","2024-08-29T07:40:11.680Z","2025-06-27T12:13:17.690Z",{"id":461,"name":462,"about":463,"email":464,"createdAt":465,"updatedAt":466,"publishedAt":467,"slug":468,"linkedin_url":469,"avatar":470},131,"Nikola Gemes","Nikola is a content marketer at Whatagraph with extensive writing experience in SaaS and tech niches. With a background in content management apps and composable architectures, it's his job to educate readers about the latest developments in the world of marketing data, data warehousing, headless architectures, and federated content platforms.","nikola.g@whatagraph.com","2023-01-20T11:02:48.617Z","2023-08-22T15:37:25.199Z","2023-01-20T11:02:48.616Z","nikola-gemes","https://www.linkedin.com/in/nikola-gemeš-6175ba157/",{"id":471,"name":472,"alternativeText":31,"caption":31,"width":473,"height":474,"formats":475,"hash":485,"ext":477,"mime":480,"size":486,"url":487,"previewUrl":31,"provider":296,"provider_metadata":31,"createdAt":488,"updatedAt":488},9564,"Nikola Gemeš.jpg",2028,2048,{"thumbnail":476},{"ext":477,"url":478,"hash":479,"mime":480,"name":481,"path":31,"size":482,"width":483,"height":484},".jpg","https://s3.us-east-2.amazonaws.com/whatagraph.com/thumbnail_Nikola_Gemes_4ff74c0a62.jpg","thumbnail_Nikola_Gemes_4ff74c0a62","image/jpeg","thumbnail_Nikola Gemeš.jpg",4.01,154,156,"Nikola_Gemes_4ff74c0a62",463.28,"https://s3.us-east-2.amazonaws.com/whatagraph.com/Nikola_Gemes_4ff74c0a62.jpg","2023-02-14T12:19:50.923Z",{"id":490,"metaTitle":415,"metaDescription":491,"addNoIndex":15,"canonicalURL":31,"metaImage":492},1769,"Learn how data blending in Looker Studio works with our step-by-step guide + video tutorial. We also reveal a faster way that overcomes Looker limitations.",{"id":424,"name":425,"alternativeText":426,"caption":31,"width":427,"height":428,"formats":493,"hash":437,"ext":292,"mime":293,"size":438,"url":439,"previewUrl":31,"provider":296,"provider_metadata":31,"createdAt":440,"updatedAt":441},{"thumbnail":494},{"ext":292,"url":431,"hash":432,"mime":293,"name":433,"path":31,"size":434,"width":435,"height":436},"\u003Cp>With data blending in Looker Studio, you can create charts, tables, and rules that combine data from several data sources. \u003C/p>\u003Cp>For example, you can blend customer information and order details and visualize the information in a one Looker Studio table. \u003C/p>\u003Cp>You can present combined data from your Google Ads and Google Analytics accounts in a single chart to see the unified performance of your marketing campaign.\u003C/p>\u003Cp>You can also blend data from different BigQuery tables.\u003C/p>\u003Cp>But before we go in more depth, let's quickly explain the difference between a blend and a data source. \u003C/p>\u003Ch2 id=\"blends-vs-data-sources\">\u003Cstrong>Blends vs. Data Sources\u003C/strong>\u003C/h2>\u003Cp>Blending data creates a resource known as a blend. Blends are similar to data sources because they also provide data for charts and tables, but they differ from data sources in several ways: \u003C/p>\u003Cul>\u003Cli>Blends contain information from multiple data sources.\u003C/li>\u003Cli>Blends in Looker Studio are always embedded into the report in which they're created. You can’t reuse a blend across reports. \u003C/li>\u003Cli>Metrics in the underlying data source become unaggregated numeric dimensions. \u003C/li>\u003Cli>Blends don’t have data freshness and credential settings of their own. These come from the underlying data sources. \u003C/li>\u003C/ul>\u003Ch2 id=\"how-to-blend-data-in-looker-studio\">How to Blend Data in Looker Studio?\u003C/h2>\u003Cp>We’ll start by creating a report in which we will connect Google Analytics 4 and YouTube to present data from both platforms in one place. \u003C/p>\u003Cp>Then, we’re going to blend data from two different sources in a single chart.\u003C/p>\u003Cp>\u003Ca href=\"https://www.linkedin.com/in/benmangold/\" target=\"_blank\" rel=\"noopener noreferrer\">Benjamin Mangold\u003C/a>, Co-Founder of \u003Ca href=\"https://www.lovesdata.com/\" target=\"_blank\" rel=\"noopener noreferrer\">Loves Data\u003C/a>, explains the process in his video:\u003C/p>\u003Cfigure class=\"media\">\u003Cdiv data-oembed-url=\"https://www.youtube.com/watch?v=qQfUwClQBPk\">\u003Cdiv style=\"position: relative; padding-bottom: 100%; height: 0; padding-bottom: 56.2493%;\">\u003Ciframe src=\"https://www.youtube.com/embed/qQfUwClQBPk\" style=\"position: absolute; width: 100%; height: 100%; top: 0; left: 0;\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen=\"\">\u003C/iframe>\u003C/div>\u003C/div>\u003C/figure>\u003Cp> \u003C/p>\u003Cp>Let’s start by creating a report in Looker Studio. \u003C/p>\u003Ch3 id=\"step-1-create-a-report-\">Step 1: Create a report \u003C/h3>\u003Col style=\"list-style-type:decimal;\">\u003Cli>Open Looker Studio and click \u003Cstrong>Create\u003C/strong> > \u003Cstrong>Report\u003C/strong>.  \u003C/li>\u003Cli>Select Google Analytics 4 as a data source. Choose the account and property you want to connect. Click \u003Cstrong>Add\u003C/strong> > \u003Cstrong>Add to Report\u003C/strong>.\u003C/li>\u003Cli>To keep things simple, we’ll replace the default table widget with a scorecard. Click on the table and click on the scorecard type in the sidebar. \u003C/li>\u003Cli>Change the scorecard metric to “Total users”. Now this scorecard shows us the total number of users on the website. \u003C/li>\u003Cli>To also include data from YouTube, we need to add another data source. Click \u003Cstrong>Add data\u003C/strong> button at the bottom of the interface. Select the YouTube account you want to connect and repeat the \u003Cstrong>Add\u003C/strong> > \u003Cstrong>Add to Report\u003C/strong> steps. We can now use this new data source in our dashboard. \u003C/li>\u003Cli>Click \u003Cstrong>Add a chart\u003C/strong> and pick a \u003Cstrong>Scorecard\u003C/strong> from the drop-down menu. Make sure that the YouTube source is selected in the sidebar Data Sources field. \u003C/li>\u003C/ol>\u003Cp>Change the metrics on the scorecard to show the total number of “Views”. \u003C/p>\u003Cp>Done! We now have a dashboard that includes data from two data sources. \u003C/p>\u003Cp>\u003Cimg src=\"https://media.whatagraph.com/Screenshot_2024_12_02_at_15_55_34_fb83b12d16.png\" srcset=\"https://media.whatagraph.com/Screenshot_2024_12_02_at_15_55_34_fb83b12d16.png?width=350 350w, https://media.whatagraph.com/Screenshot_2024_12_02_at_15_55_34_fb83b12d16.png?width=700 700w, https://media.whatagraph.com/Screenshot_2024_12_02_at_15_55_34_fb83b12d16.png?width=420 440w, https://media.whatagraph.com/Screenshot_2024_12_02_at_15_55_34_fb83b12d16.png?width=840 880w, https://media.whatagraph.com/Screenshot_2024_12_02_at_15_55_34_fb83b12d16.png?width=768 768w, https://media.whatagraph.com/Screenshot_2024_12_02_at_15_55_34_fb83b12d16.png?width=1536 1536w, https://media.whatagraph.com/Screenshot_2024_12_02_at_15_55_34_fb83b12d16.png?width=992 992w, https://media.whatagraph.com/Screenshot_2024_12_02_at_15_55_34_fb83b12d16.png?width=1984 1984w, https://media.whatagraph.com/Screenshot_2024_12_02_at_15_55_34_fb83b12d16.png?width=600 1200w, https://media.whatagraph.com/Screenshot_2024_12_02_at_15_55_34_fb83b12d16.png?width=1200 2400w, https://media.whatagraph.com/Screenshot_2024_12_02_at_15_55_34_fb83b12d16.png?width=720 1440w, https://media.whatagraph.com/Screenshot_2024_12_02_at_15_55_34_fb83b12d16.png?width=1440 2880w\" sizes=\"(max-width: 350px) 350px, (max-width: 440px) 440px, (max-width: 768px) 768px, (max-width: 992px) 992px, (max-width: 1200px) 1200px, (min-resolution: 192dpi) and (max-width: 350px) 700px, (min-resolution: 192dpi) and (max-width: 420px) 840px, (min-resolution: 192dpi) and (max-width: 768px) 1536px, (min-resolution: 192dpi) and (max-width: 992px) 1984px, (min-resolution: 192dpi) and (max-width: 1200px) 2400px, (min-resolution: 192dpi) 2880px, 1440px\" loading=\"lazy\" fetchpriority=\"auto\" decoding=\"async\" alt=\"Looker Studio Report - Data Blending in Looker Studio\">\u003C/p>\u003Cp> \u003C/p>\u003Cp>Now, we'll add another GA4 data source and create a blend\u003C/p>\u003Ch3 id=\"step-2-start-blending-data\">\u003Cstrong>Step 2: Start blending data\u003C/strong>\u003C/h3>\u003Cp>Click \u003Cstrong>Add data\u003C/strong> in the bottom corner and select Google Analytics 4. Choose the account and property you want to connect. Click \u003Cstrong>Add\u003C/strong> > \u003Cstrong>Add to Report\u003C/strong>.\u003C/p>\u003Cp>We can now add another chart to our report. Click \u003Cstrong>Add a chart\u003C/strong> and pick a \u003Cstrong>Time series. \u003C/strong>\u003C/p>\u003Cp>Under the Setup tab, we can see that this chart uses the GA4 source we’ve just added. \u003C/p>\u003Cp>Click \u003Cstrong>Blend Data \u003C/strong>to create a new blended data source. \u003C/p>\u003Cp>To make a blended data source, you’ll need a “key” also called a “joint condition” that is available in both sources. It’s a piece of information available in both data sets. \u003C/p>\u003Cp>For example, we can use the dimension of “Date” to combine data. We can then present the number of users from both data sources by the date. \u003C/p>\u003Cp>We can see our existing data source on the left. Click \u003Cstrong>Join another table\u003C/strong>. \u003C/p>\u003Cp>\u003Cimg src=\"https://media.whatagraph.com/Screenshot_2024_12_02_at_17_51_23_7256443023.png\" srcset=\"https://media.whatagraph.com/Screenshot_2024_12_02_at_17_51_23_7256443023.png?width=350 350w, https://media.whatagraph.com/Screenshot_2024_12_02_at_17_51_23_7256443023.png?width=700 700w, https://media.whatagraph.com/Screenshot_2024_12_02_at_17_51_23_7256443023.png?width=420 440w, https://media.whatagraph.com/Screenshot_2024_12_02_at_17_51_23_7256443023.png?width=840 880w, https://media.whatagraph.com/Screenshot_2024_12_02_at_17_51_23_7256443023.png?width=768 768w, https://media.whatagraph.com/Screenshot_2024_12_02_at_17_51_23_7256443023.png?width=1536 1536w, https://media.whatagraph.com/Screenshot_2024_12_02_at_17_51_23_7256443023.png?width=992 992w, https://media.whatagraph.com/Screenshot_2024_12_02_at_17_51_23_7256443023.png?width=1984 1984w, https://media.whatagraph.com/Screenshot_2024_12_02_at_17_51_23_7256443023.png?width=600 1200w, https://media.whatagraph.com/Screenshot_2024_12_02_at_17_51_23_7256443023.png?width=1200 2400w, https://media.whatagraph.com/Screenshot_2024_12_02_at_17_51_23_7256443023.png?width=720 1440w, https://media.whatagraph.com/Screenshot_2024_12_02_at_17_51_23_7256443023.png?width=1440 2880w\" sizes=\"(max-width: 350px) 350px, (max-width: 440px) 440px, (max-width: 768px) 768px, (max-width: 992px) 992px, (max-width: 1200px) 1200px, (min-resolution: 192dpi) and (max-width: 350px) 700px, (min-resolution: 192dpi) and (max-width: 420px) 840px, (min-resolution: 192dpi) and (max-width: 768px) 1536px, (min-resolution: 192dpi) and (max-width: 992px) 1984px, (min-resolution: 192dpi) and (max-width: 1200px) 2400px, (min-resolution: 192dpi) 2880px, 1440px\" loading=\"lazy\" fetchpriority=\"auto\" decoding=\"async\" alt=\"Screenshot 2024-12-02 at 17.51.23.png\">\u003C/p>\u003Cp>Select the GA4 property you added first. Now, you need to choose the dimensions and metrics you want to combine. \u003C/p>\u003Cp>The “Date” is already selected as a dimension. For the metrics, we’ll add “Total users” and “Sessions” for both data sources.\u003C/p>\u003Cp>\u003Ci>\u003Cstrong>Pro tip\u003C/strong>: Name each table used in your blended data source. This will help you if you decide to create any calculated fields in your report. Calculated fields let you create custom metrics and dimensions in Looker Studio. \u003C/i>\u003C/p>\u003Cp>To name a table, select the default text at the top that says “Table Name”.\u003C/p>\u003Cp>Every blend in Looker Studio can have up to 5 tables. This is definitely a limitation, but we'll get back to it in a bit. \u003C/p>\u003Cp>For now, let's finish our blend. \u003C/p>\u003Ch3 id=\"step-3-join-data-from-two-sources\">\u003Cstrong>Step 3: Join data from two sources\u003C/strong>\u003C/h3>\u003Cp>You can see the metrics and dimensions for our blended data source on the right, together with corresponding table names. \u003C/p>\u003Cp>The next thing you need to decide is how to join data from the tables.  \u003C/p>\u003Cp>Click \u003Cstrong>Configure join\u003C/strong> in the middle. \u003C/p>\u003Cp>There are five ways you can join tables.  \u003C/p>\u003Cp>With these options, you can control what happens if data is missing from one of the tables. For example, if there is data for a particular day in one table but not the other. \u003C/p>\u003Cul>\u003Cli>\u003Cstrong>Left outer\u003C/strong>: If you use “Left Outer”, all the rows from the table on the left will be used, along with any of the matching rows from the table on the right. If there is a row missing in the table on the right, it won’t be combined in your blended source. \u003C/li>\u003Cli>\u003Cstrong>Right outer\u003C/strong>: If you use “Right Outer”, any missing rows from the table on the left won’t be used. \u003C/li>\u003Cli>\u003Cstrong>Inner\u003C/strong>: Only rows that are available in both tables will be used.  \u003C/li>\u003Cli>\u003Cstrong>Full outer\u003C/strong>: All rows from both tables will be used. \u003C/li>\u003Cli>\u003Cstrong>Cross\u003C/strong>: This option combines all available rows from both data sources. \u003C/li>\u003C/ul>\u003Cp>For our blended data source, let’s select “Full outer”. \u003C/p>\u003Cp>Now, we need to select the \u003Cstrong>Join condition\u003C/strong> or data key to join the data together. Choose the “Date” dimension. Click \u003Cstrong>Save\u003C/strong>.  \u003C/p>\u003Cp>You can now name the blended data source. For example, “GA4 Combined”. Click \u003Cstrong>Save\u003C/strong>.  \u003C/p>\u003Ch3 id=\"step-4-combine-metrics\">\u003Cstrong>Step 4: Combine metrics\u003C/strong>\u003C/h3>\u003Cp>We can see our new blended data source is applied to our chart. Each metric is still separate. \u003C/p>\u003Cp>We have two metrics for “Sessions” and two metrics for “Total users”. \u003C/p>\u003Cp>To create a combined metric, we need to create a calculated field. \u003C/p>\u003Cp>Under the \u003Cstrong>Setup\u003C/strong> tab, select \u003Cstrong>Add metric\u003C/strong> > \u003Cstrong>Create field\u003C/strong>. \u003C/p>\u003Cp>Let’s call it “Total Users Combined”. For the formula start typing “Total users” and then select the metric from the first table. Enter a “\u003Cstrong>+\u003C/strong>” sign and search for “Total users” again and select the metric from the second table. Click \u003Cstrong>Apply\u003C/strong>. \u003C/p>\u003Cp>You can now see the total number of users combined from both GA4 properties in the time series.   \u003C/p>\u003Ch2 id=\"limitations-of-data-blending-in-looker-studio\">5 Limitations of Data Blending in Looker Studio\u003C/h2>\u003Cp>There’s no doubt that \u003Ca href=\"/blog/articles/data-blending\">data blending is a great feature\u003C/a>. Blended data can help you see the bigger picture behind your data and reveal underlying trends. However, there are several Looker Studio limitations that could slow down your report or make blended data in Looker Studio inaccurate.\u003C/p>\u003Ch3 id=\"a-limited-number-of-blended-sources\">1. A limited number of blended sources\u003C/h3>\u003Cp>Remember when we said that one blend can have up to 5 tables? That’s right. You can’t add more than five data sources and more than 10 dimensions from a single data source. Both numbers might seem reasonable, but they're not always enough. \u003C/p>\u003Cp>Agencies often need to fetch data from many different client sources using \u003Ca href=\"/blog/articles/marketing-data-connectors\">multiple marketing data connectors\u003C/a>. For example, ad metrics from several PPC apps, sales data from an e-commerce platform, customer data from the CRM, and different information from several more spreadsheets. \u003C/p>\u003Cp>In any case, when you hit the limit, you must split the data into several reports, which makes it harder to get the full picture of your performance. \u003C/p>\u003Ch3 id=\"slow-loading-and-processing-times\">2. Slow loading and processing times\u003C/h3>\u003Cp>You might have noticed that the platform sometimes takes more time to load your data, even without blending. \u003C/p>\u003Cp>Indeed, \u003Ca href=\"/blog/articles/looker-studio-slow\">Looker Studio is slow\u003C/a> for several reasons, and it tends to get even slower when you start using it with multiple data sources, especially several sources at the same time. \u003C/p>\u003Cp>\u003Cimg src=\"https://media.whatagraph.com/Data_c8456a1b88.png\" srcset=\"https://media.whatagraph.com/Data_c8456a1b88.png?width=350 350w, https://media.whatagraph.com/Data_c8456a1b88.png?width=700 700w, https://media.whatagraph.com/Data_c8456a1b88.png?width=420 440w, https://media.whatagraph.com/Data_c8456a1b88.png?width=840 880w, https://media.whatagraph.com/Data_c8456a1b88.png?width=768 768w, https://media.whatagraph.com/Data_c8456a1b88.png?width=1536 1536w, https://media.whatagraph.com/Data_c8456a1b88.png?width=992 992w, https://media.whatagraph.com/Data_c8456a1b88.png?width=1984 1984w, https://media.whatagraph.com/Data_c8456a1b88.png?width=600 1200w, https://media.whatagraph.com/Data_c8456a1b88.png?width=1200 2400w, https://media.whatagraph.com/Data_c8456a1b88.png?width=720 1440w, https://media.whatagraph.com/Data_c8456a1b88.png?width=1440 2880w\" sizes=\"(max-width: 350px) 350px, (max-width: 440px) 440px, (max-width: 768px) 768px, (max-width: 992px) 992px, (max-width: 1200px) 1200px, (min-resolution: 192dpi) and (max-width: 350px) 700px, (min-resolution: 192dpi) and (max-width: 420px) 840px, (min-resolution: 192dpi) and (max-width: 768px) 1536px, (min-resolution: 192dpi) and (max-width: 992px) 1984px, (min-resolution: 192dpi) and (max-width: 1200px) 2400px, (min-resolution: 192dpi) 2880px, 1440px\" loading=\"lazy\" fetchpriority=\"auto\" decoding=\"async\" alt=\"google extract data connector\">\u003C/p>\u003Cp>For each source, Looker Studio needs to connect to a different API, which demands additional computing power that isn't always available. \u003C/p>\u003Cp>Blending two sources usually works fine, but the more sources you add, the slower your dashboard gets. \u003C/p>\u003Ch3 id=\"blends-are-not-reusable\">3. Blends are not reusable\u003C/h3>\u003Cp>In Looker Studio, blends are always embedded into the report in which they're created. There’s no way to make a blend reusable across reports. If you copy the report, the blends are copied into the new report, so your charts will continue to present the blended data. \u003C/p>\u003Ch3 id=\"depends-on-third-party-connectors-\">4. Depends on third-party connectors \u003C/h3>\u003Cp>Third-party connectors are necessary for integrating data from various sources. However, these connectors are sometimes too complex, unreliable, or have limited capabilities. \u003C/p>\u003Cp>An old adage says “A man is only as good as his tools”. In the case of Looker Studio, third-party tools can sometimes cause problems. \u003C/p>\u003Cp>Google Analytics, Google Ads, and other data sources from the Google ecosystem have one connection per property, which includes all data fields. \u003C/p>\u003Cp>On the other hand, third-party connectors often let you choose between three different menus, each connecting to a different segment of the tool’s data. This complicates your data blending processes. \u003C/p>\u003Cp>Reporting on an enterprise property or managing an agency portfolio of clients can become a nightmare. \u003C/p>\u003Cp>That's especially true if you need to connect or blend all data sources  – not to mention trying to replicate the report with blended data. \u003C/p>\u003Cp>\u003Cimg src=\"https://media.whatagraph.com/thrid_party_connectors_87474828c6.png\" srcset=\"https://media.whatagraph.com/thrid_party_connectors_87474828c6.png?width=350 350w, https://media.whatagraph.com/thrid_party_connectors_87474828c6.png?width=700 700w, https://media.whatagraph.com/thrid_party_connectors_87474828c6.png?width=420 440w, https://media.whatagraph.com/thrid_party_connectors_87474828c6.png?width=840 880w, https://media.whatagraph.com/thrid_party_connectors_87474828c6.png?width=768 768w, https://media.whatagraph.com/thrid_party_connectors_87474828c6.png?width=1536 1536w, https://media.whatagraph.com/thrid_party_connectors_87474828c6.png?width=992 992w, https://media.whatagraph.com/thrid_party_connectors_87474828c6.png?width=1984 1984w, https://media.whatagraph.com/thrid_party_connectors_87474828c6.png?width=600 1200w, https://media.whatagraph.com/thrid_party_connectors_87474828c6.png?width=1200 2400w, https://media.whatagraph.com/thrid_party_connectors_87474828c6.png?width=720 1440w, https://media.whatagraph.com/thrid_party_connectors_87474828c6.png?width=1440 2880w\" sizes=\"(max-width: 350px) 350px, (max-width: 440px) 440px, (max-width: 768px) 768px, (max-width: 992px) 992px, (max-width: 1200px) 1200px, (min-resolution: 192dpi) and (max-width: 350px) 700px, (min-resolution: 192dpi) and (max-width: 420px) 840px, (min-resolution: 192dpi) and (max-width: 768px) 1536px, (min-resolution: 192dpi) and (max-width: 992px) 1984px, (min-resolution: 192dpi) and (max-width: 1200px) 2400px, (min-resolution: 192dpi) 2880px, 1440px\" loading=\"lazy\" fetchpriority=\"auto\" decoding=\"async\" alt=\"Looker Studio Connectors\">\u003C/p>\u003Cp style=\"text-align:right;\">\u003Ci>Connectors from different vendors may not have the same performance\u003C/i>\u003C/p>\u003Cp>Also, third-party connectors may not be as reliable or consistent as native Looker Studio connectors. This can cause data errors, connectivity issues, or downtimes that disrupt your data analysis and reporting performance. \u003C/p>\u003Cp>Ironically, clunky spreadsheets or BigQuery-hosted datasets are often the best way to connect non-Google data sources to Looker Studio. \u003C/p>\u003Cp>This dependence on third-party connectors is not something that plagues Looker Studio alone. \u003C/p>\u003Cp>Many \u003Ca href=\"/blog/articles/data-transformation-tools\">data transformation tools\u003C/a> use third-party connectors to connect data from scattered sources. To avoid this problem, consider using a tool with pre-built or native integrations. \u003C/p>\u003Ch3 id=\"steep-learning-curve\">5. Steep learning curve\u003C/h3>\u003Cp>Looker Studio is not one of those tools you can just pick up and use. Its unique modeling language requires that you have at least a basic understanding of coding — primarily languages like SQL. \u003C/p>\u003Cp>This can be challenging for agencies or companies without technical expertise or a dedicated data team. \u003C/p>\u003Cp>And if you run into a problem or unknown, you’re pretty much left to yourself, as customer support only applies to users with a Google Cloud support plan and Looker Studio Pro subscription. \u003C/p>\u003Ch2 id=\"how-to-overcome-limitations-of-data-blending-in-looker-studio-with-whatagraph\">How to Overcome Limitations of Data Blending in Looker Studio with Whatagraph\u003C/h2>\u003Cp>Whatagraph is one platform to connect, \u003Ca href=\"/organize\">organize\u003C/a>, visualize, and share all your marketing data. \u003C/p>\u003Cp>Designed to replace multiple complex data tools with one intuitive platform, Whatagraph also gives you the easiest way to blend your data. \u003C/p>\u003Cp>How?\u003C/p>\u003Cp>Once you connect your data sources, you have the option to organize your data before analyzing it further. \u003C/p>\u003Ch3 id=\"no-limits-on-blending-data\">No limits on blending data\u003C/h3>\u003Cp>With Whatagraph, there’s no limit on how many sources or dimensions you can blend. Even better, the accuracy and reliability of your blends don’t depend on third-party connectors. \u003C/p>\u003Cp>Whatagraph has fully managed integrations with 55+ marketing platforms, including web analytics, social media, paid advertising, SEO, e-commerce, email marketing, and CRM tools. \u003C/p>\u003Cp>This means that data in your blends is reliable, whether you use Google- or non-Google-based marketing apps. \u003C/p>\u003Cp>But that’s not all. Apart from these integrations, you can connect any data source via a Custom API or by using Google Sheets or BigQuery as a source. \u003C/p>\u003Ch3 id=\"faster-processing-times\">Faster processing times\u003C/h3>\u003Cp>While Looker Studio can get very slow even with two data sources in a blend, Whatagraph easily handles dozens of report pages and unlimited widgets and sources. \u003C/p>\u003Cp>Thanks to a recent update to \u003Ca href=\"https://cloud.google.com/kubernetes-engine\" target=\"_blank\" rel=\"noopener noreferrer\">Google Kubernetes Engine\u003C/a>, even very heavy widgets with 180 configurations now take less than 10 seconds to load. \u003C/p>\u003Ch3 id=\"easily-review-your-blends-to-make-sure-the-report-is-accurate\">Easily review your blends to make sure the report is accurate\u003C/h3>\u003Cp>In Whatagraph, you can quickly review the dimensions and metrics for each blend you’re creating or editing. This way, you can make sure there are no duplicates and that nothing goes into the blend that shouldn’t. \u003C/p>\u003Cp>\u003Cimg src=\"https://media.whatagraph.com/blend_example_3_3fba888fa1.png\" srcset=\"https://media.whatagraph.com/blend_example_3_3fba888fa1.png?width=350 350w, https://media.whatagraph.com/blend_example_3_3fba888fa1.png?width=700 700w, https://media.whatagraph.com/blend_example_3_3fba888fa1.png?width=420 440w, https://media.whatagraph.com/blend_example_3_3fba888fa1.png?width=840 880w, https://media.whatagraph.com/blend_example_3_3fba888fa1.png?width=768 768w, https://media.whatagraph.com/blend_example_3_3fba888fa1.png?width=1536 1536w, https://media.whatagraph.com/blend_example_3_3fba888fa1.png?width=992 992w, https://media.whatagraph.com/blend_example_3_3fba888fa1.png?width=1984 1984w, https://media.whatagraph.com/blend_example_3_3fba888fa1.png?width=600 1200w, https://media.whatagraph.com/blend_example_3_3fba888fa1.png?width=1200 2400w, https://media.whatagraph.com/blend_example_3_3fba888fa1.png?width=720 1440w, https://media.whatagraph.com/blend_example_3_3fba888fa1.png?width=1440 2880w\" sizes=\"(max-width: 350px) 350px, (max-width: 440px) 440px, (max-width: 768px) 768px, (max-width: 992px) 992px, (max-width: 1200px) 1200px, (min-resolution: 192dpi) and (max-width: 350px) 700px, (min-resolution: 192dpi) and (max-width: 420px) 840px, (min-resolution: 192dpi) and (max-width: 768px) 1536px, (min-resolution: 192dpi) and (max-width: 992px) 1984px, (min-resolution: 192dpi) and (max-width: 1200px) 2400px, (min-resolution: 192dpi) 2880px, 1440px\" loading=\"lazy\" fetchpriority=\"auto\" decoding=\"async\" alt=\"blend_example_3.png\">\u003C/p>\u003Ch3 id=\"save-any-blends-to-reuse-and-edit\">Save any blends to reuse and edit\u003C/h3>\u003Cp>Everything you create in Whatagraph, from metrics to whole reports, can be saved and reused as a template. This also applies to data blending. \u003C/p>\u003Cp>You can easily reuse and edit all data blends, formulas, custom dimensions, and metrics.\u003C/p>\u003Ch3 id=\"anyone-on-the-team-can-handle-advanced-analytics-tasks-\">Anyone on the team can handle advanced analytics tasks \u003C/h3>\u003Cp>Whatagraph has a user-friendly UX/UI across the whole platform, making any data management task easy without technical knowledge. \u003C/p>\u003Ch2 id=\"how-to-blend-data-in-whatagraph\">How to Blend Data in Whatagraph\u003C/h2>\u003Cp>Now, we’ll explain how to blend data effectively using Whatagraph’s no-code process.  \u003C/p>\u003Ch3 id=\"example-1-blending-sources\">Example 1: Blending sources\u003C/h3>\u003Cp>In this example, we’ll create a simple blend of Facebook Ads and Google Ads to get the number of impressions and clicks from both sources in one table. \u003C/p>\u003Cp>\u003Cstrong>Step 1:\u003C/strong> We start with a report with connected Facebook Ads and Google Ads. Without blended data, the report splits the performance of two sources into two tables. Now, we’ll blend these two sources.  \u003C/p>\u003Cp>\u003Cstrong>Step 2:\u003C/strong> Click the \u003Cstrong>Sources\u003C/strong> tab and then \u003Cstrong>Add new data\u003C/strong>. Choose the \u003Cstrong>Blended sources\u003C/strong> tab and click \u003Cstrong>Crete a blended source\u003C/strong> button.  \u003C/p>\u003Cp>\u003Cstrong>Step 3\u003C/strong>:  A blending window will appear. Start by selecting the channels and the specific data sources. Select Facebook Ads and Google Ads.\u003C/p>\u003Cp>\u003Cimg src=\"https://media.whatagraph.com/data_blending_whatagraph_660538108a.png\" srcset=\"https://media.whatagraph.com/data_blending_whatagraph_660538108a.png?width=350 350w, https://media.whatagraph.com/data_blending_whatagraph_660538108a.png?width=700 700w, https://media.whatagraph.com/data_blending_whatagraph_660538108a.png?width=420 440w, https://media.whatagraph.com/data_blending_whatagraph_660538108a.png?width=840 880w, https://media.whatagraph.com/data_blending_whatagraph_660538108a.png?width=768 768w, https://media.whatagraph.com/data_blending_whatagraph_660538108a.png?width=1536 1536w, https://media.whatagraph.com/data_blending_whatagraph_660538108a.png?width=992 992w, https://media.whatagraph.com/data_blending_whatagraph_660538108a.png?width=1984 1984w, https://media.whatagraph.com/data_blending_whatagraph_660538108a.png?width=600 1200w, https://media.whatagraph.com/data_blending_whatagraph_660538108a.png?width=1200 2400w, https://media.whatagraph.com/data_blending_whatagraph_660538108a.png?width=720 1440w, https://media.whatagraph.com/data_blending_whatagraph_660538108a.png?width=1440 2880w\" sizes=\"(max-width: 350px) 350px, (max-width: 440px) 440px, (max-width: 768px) 768px, (max-width: 992px) 992px, (max-width: 1200px) 1200px, (min-resolution: 192dpi) and (max-width: 350px) 700px, (min-resolution: 192dpi) and (max-width: 420px) 840px, (min-resolution: 192dpi) and (max-width: 768px) 1536px, (min-resolution: 192dpi) and (max-width: 992px) 1984px, (min-resolution: 192dpi) and (max-width: 1200px) 2400px, (min-resolution: 192dpi) 2880px, 1440px\" loading=\"lazy\" fetchpriority=\"auto\" decoding=\"async\" alt=\"Blending sources in Whatagraph\">\u003C/p>\u003Cp>\u003Cstrong>Step 4\u003C/strong>: Choose \u003Ci>Year, month\u003C/i> as the dimensions for both sources and \u003Ci>Impressions\u003C/i> and \u003Ci>Clicks\u003C/i>/\u003Ci>Clicks (All)\u003C/i> as metrics.\u003C/p>\u003Cp>\u003Cstrong>Step 5:\u003C/strong> Select the join. For this blend, take the full outer join. Select \u003Ci>Year, month\u003C/i> as the join key for both sources and click \u003Cstrong>Save setup\u003C/strong>.\u003C/p>\u003Cp>\u003Cimg src=\"https://media.whatagraph.com/select_join_key_b6d27b0318.png\" srcset=\"https://media.whatagraph.com/select_join_key_b6d27b0318.png?width=350 350w, https://media.whatagraph.com/select_join_key_b6d27b0318.png?width=700 700w, https://media.whatagraph.com/select_join_key_b6d27b0318.png?width=420 440w, https://media.whatagraph.com/select_join_key_b6d27b0318.png?width=840 880w, https://media.whatagraph.com/select_join_key_b6d27b0318.png?width=768 768w, https://media.whatagraph.com/select_join_key_b6d27b0318.png?width=1536 1536w, https://media.whatagraph.com/select_join_key_b6d27b0318.png?width=992 992w, https://media.whatagraph.com/select_join_key_b6d27b0318.png?width=1984 1984w, https://media.whatagraph.com/select_join_key_b6d27b0318.png?width=600 1200w, https://media.whatagraph.com/select_join_key_b6d27b0318.png?width=1200 2400w, https://media.whatagraph.com/select_join_key_b6d27b0318.png?width=720 1440w, https://media.whatagraph.com/select_join_key_b6d27b0318.png?width=1440 2880w\" sizes=\"(max-width: 350px) 350px, (max-width: 440px) 440px, (max-width: 768px) 768px, (max-width: 992px) 992px, (max-width: 1200px) 1200px, (min-resolution: 192dpi) and (max-width: 350px) 700px, (min-resolution: 192dpi) and (max-width: 420px) 840px, (min-resolution: 192dpi) and (max-width: 768px) 1536px, (min-resolution: 192dpi) and (max-width: 992px) 1984px, (min-resolution: 192dpi) and (max-width: 1200px) 2400px, (min-resolution: 192dpi) 2880px, 1440px\" loading=\"lazy\" fetchpriority=\"auto\" decoding=\"async\" alt=\"Select join key\">\u003C/p>\u003Cp>\u003Cstrong>Step 6:\u003C/strong> Give your blend a name and a short description. It’s a good practice to use a recognizable name so other users can easily find it. \u003C/p>\u003Cp>Click \u003Cstrong>Create a blend,\u003C/strong> and your job is done. \u003C/p>\u003Cp>You can use the blend you created as a source for any report. Select the newly created blend, drag a widget to the report, and pick the metrics from both sources. \u003C/p>\u003Cp>This example is just one of many \u003Ca href=\"/blog/articles/data-blending\">data blending use cases\u003C/a> available in Whatagraph.     \u003C/p>\u003Ch3 id=\"example-2-creating-formulas-on-a-blended-source-level\">Example 2: Creating formulas on a blended source level\u003C/h3>\u003Cp>In this example, we’ll create a custom formula for total Impressions from Facebook Ads and Google Ads sources we just blended.\u003C/p>\u003Cp>\u003Cstrong>Step 1\u003C/strong>: Click on the widget with a connected blended source. Click \u003Cstrong>Add new\u003C/strong> in the metrics selection, and then \u003Cstrong>Create new metric\u003C/strong>.\u003C/p>\u003Cp>\u003Cstrong>Step 2\u003C/strong>: The Create metric view will open. Set the display name and description for your new metrics e.g. \u003Cstrong>Total Impressions\u003C/strong>. Since we want to use this new metric in a formula, under the rule type, select Formula. \u003C/p>\u003Cp>\u003Cstrong>Step 3\u003C/strong>: It immediately selects the data blend we used in this report. Now, we need to pick the individual metrics from the blend. Let’s say we want total impressions, so we’ll take Impressions from both channels.\u003C/p>\u003Cp>\u003Cimg src=\"https://media.whatagraph.com/blend_example_4_844a7d2f38.png\" srcset=\"https://media.whatagraph.com/blend_example_4_844a7d2f38.png?width=350 350w, https://media.whatagraph.com/blend_example_4_844a7d2f38.png?width=700 700w, https://media.whatagraph.com/blend_example_4_844a7d2f38.png?width=420 440w, https://media.whatagraph.com/blend_example_4_844a7d2f38.png?width=840 880w, https://media.whatagraph.com/blend_example_4_844a7d2f38.png?width=768 768w, https://media.whatagraph.com/blend_example_4_844a7d2f38.png?width=1536 1536w, https://media.whatagraph.com/blend_example_4_844a7d2f38.png?width=992 992w, https://media.whatagraph.com/blend_example_4_844a7d2f38.png?width=1984 1984w, https://media.whatagraph.com/blend_example_4_844a7d2f38.png?width=600 1200w, https://media.whatagraph.com/blend_example_4_844a7d2f38.png?width=1200 2400w, https://media.whatagraph.com/blend_example_4_844a7d2f38.png?width=720 1440w, https://media.whatagraph.com/blend_example_4_844a7d2f38.png?width=1440 2880w\" sizes=\"(max-width: 350px) 350px, (max-width: 440px) 440px, (max-width: 768px) 768px, (max-width: 992px) 992px, (max-width: 1200px) 1200px, (min-resolution: 192dpi) and (max-width: 350px) 700px, (min-resolution: 192dpi) and (max-width: 420px) 840px, (min-resolution: 192dpi) and (max-width: 768px) 1536px, (min-resolution: 192dpi) and (max-width: 992px) 1984px, (min-resolution: 192dpi) and (max-width: 1200px) 2400px, (min-resolution: 192dpi) 2880px, 1440px\" loading=\"lazy\" fetchpriority=\"auto\" decoding=\"async\" alt=\"blend_example_4.png\">\u003C/p>\u003Cp>\u003Cstrong>Step 4\u003C/strong>: Type the formula \u003Cstrong>A+B\u003C/strong>, where A is the designated label for Facebook Ads and B for Google Ads. Click \u003Cstrong>Create metric\u003C/strong>.\u003C/p>\u003Cp>Your new formula will automatically appear under the Metrics selection for the widget. \u003C/p>\u003Cp>Update the widget and your custom table now has Impressions from both channels as well as the column for total Impressions. \u003C/p>\u003Cp>You’ll find more data transformation use cases and ways to organize your marketing data in our \u003Ca href=\"/blog/articles/marketing-data-transformation\">marketing data transformation article\u003C/a>.  \u003C/p>\u003Ch2 id=\"more-reasons-to-pick-whatagraph-over-looker-studio\">More Reasons to Pick Whatagraph Over Looker Studio\u003C/h2>\u003Cp>Looker Studio is a dashboarding tool created as a supplement to Looker – a much more complex platform for data modeling and governance. Although it lets users create visual reports of available data, Looker Studio lacks the advantages of an all-in-one marketing data platform. \u003C/p>\u003Cp>Let’s consider a few more reasons to use Whatagraph to manage your marketing data.\u003C/p>\u003Ch3 id=\"fully-managed-integrations\">Fully managed integrations\u003C/h3>\u003Cp>Consistent experience and 30-minute refresh rate across all data sources you connect. More than 55 marketing platforms are supported, plus you can connect any data through Google Sheets, BigQuery, or Custom API.\u003C/p>\u003Ch3 id=\"cross-channel-reporting\">Cross-channel reporting\u003C/h3>\u003Cp>Add any marketing channel to your report and compare the performance of different channels in a few clicks. Easily blend data and combine metrics from multiple sources. Pick any metrics from the blended sources and use custom formulas to add, divide, multiply, and take parts of it. \u003C/p>\u003Cfigure class=\"media\">\u003Cdiv data-oembed-url=\"https://www.youtube.com/watch?v=jPBSI3My-5s\">\u003Cdiv style=\"position: relative; padding-bottom: 100%; height: 0; padding-bottom: 56.2493%;\">\u003Ciframe src=\"https://www.youtube.com/embed/jPBSI3My-5s\" style=\"position: absolute; width: 100%; height: 100%; top: 0; left: 0;\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen=\"\">\u003C/iframe>\u003C/div>\u003C/div>\u003C/figure>\u003Ch3 id=\"pre-made-widgets\">Pre-made widgets\u003C/h3>\u003Cp>Save time visualizing your data with pre-made report building blocks. Drag and drop them to your report page. Resize them as needed, add more metrics, apply filters, and save them as widget templates. \u003C/p>\u003Ch3 id=\"linked-templates\">Linked templates\u003C/h3>\u003Cp>Link reports to one template to \u003Cstrong>edit them all at once \u003C/strong>by editing the template and saving hours in the process. Change sources, text widgets, images, and company logos through multiple client reports instead of doing it one by one. \u003C/p>\u003Ch3 id=\"customization-white-labeling\">Customization & white-labeling\u003C/h3>\u003Cp>You can effortlessly change the default design of your reports and dashboard and apply a different theme that matches your or your client’s branding. \u003C/p>\u003Cul>\u003Cli>Remove Whatagraph’s logo,\u003C/li>\u003Cli>Choose a color scheme,\u003C/li>\u003Cli>Specify the company name,\u003C/li>\u003Cli>Enter the new domain name,\u003C/li>\u003Cli>Customize the reply-to-address,\u003C/li>\u003Cli>Specify who on your team is responsible for the report.\u003C/li>\u003C/ul>\u003Cp>\u003Cimg src=\"https://media.whatagraph.com/white_label_c3f037378e.png\" srcset=\"https://media.whatagraph.com/white_label_c3f037378e.png?width=350 350w, https://media.whatagraph.com/white_label_c3f037378e.png?width=700 700w, https://media.whatagraph.com/white_label_c3f037378e.png?width=420 440w, https://media.whatagraph.com/white_label_c3f037378e.png?width=840 880w, https://media.whatagraph.com/white_label_c3f037378e.png?width=768 768w, https://media.whatagraph.com/white_label_c3f037378e.png?width=1536 1536w, https://media.whatagraph.com/white_label_c3f037378e.png?width=992 992w, https://media.whatagraph.com/white_label_c3f037378e.png?width=1984 1984w, https://media.whatagraph.com/white_label_c3f037378e.png?width=600 1200w, https://media.whatagraph.com/white_label_c3f037378e.png?width=1200 2400w, https://media.whatagraph.com/white_label_c3f037378e.png?width=720 1440w, https://media.whatagraph.com/white_label_c3f037378e.png?width=1440 2880w\" sizes=\"(max-width: 350px) 350px, (max-width: 440px) 440px, (max-width: 768px) 768px, (max-width: 992px) 992px, (max-width: 1200px) 1200px, (min-resolution: 192dpi) and (max-width: 350px) 700px, (min-resolution: 192dpi) and (max-width: 420px) 840px, (min-resolution: 192dpi) and (max-width: 768px) 1536px, (min-resolution: 192dpi) and (max-width: 992px) 1984px, (min-resolution: 192dpi) and (max-width: 1200px) 2400px, (min-resolution: 192dpi) 2880px, 1440px\" loading=\"lazy\" fetchpriority=\"auto\" decoding=\"async\" alt=\"white_label.png\">\u003C/p>\u003Cp>This effectively means an agency can \u003Ca href=\"/white-label\">customize reports\u003C/a> for each of their clients.\u003C/p>\u003Ch3 id=\"report-automation-review-step\">Report automation & review step\u003C/h3>\u003Cp>Once you create a report you can automate the way you deliver it. Choose the frequency, delivery days, time zone, and recipients, and automate the whole process. \u003C/p>\u003Cp>When the time comes for the next report, Whatagraph automatically refreshes the widgets with new data from connected sources and sends the next report on schedule. \u003C/p>\u003Cp>However, you can add the review step to double-check the report content before it gets sent. In this step, you can manually edit numbers, delete or add new widgets, change metrics, change media images, or add text comments. \u003C/p>\u003Ch3 id=\"no-code-data-transfers\">No code data transfers\u003C/h3>\u003Cp>Whatagraph has an intuitive, no-code workflow to transfer data from multiple marketing platforms to Google BigQuery data warehouse. \u003C/p>\u003Cp>This gives you complete ownership of the marketing data they collect. Instead of having data in different locations, use data transfer to copy it all to a managed data warehouse. This way, you protect your data from deprecation, sampling, or changing policies of individual platforms.\u003C/p>\u003Ch3 id=\"responsive-customer-support\">Responsive customer support\u003C/h3>\u003Cp>Unless you pay more for Looker Studio Pro, you’re left to online discussion forums and Google’s help docs that are often difficult for a non-technical person to understand.\u003C/p>\u003Cp style=\"margin-left:0px;\">All Whatagraph pricing plans come with a dedicated Customer Success Manager and live chat support who reply to your questions within 4 minutes.\u003C/p>\u003Cp style=\"margin-left:0px;\">Your dedicated CSM can help you migrate data from your current platform, connect to channels and data sources, organize your data, and everything else to ensure you have the smoothest experience with Whatagraph. \u003C/p>\u003Ch2 id=\"wrapping-up\">Wrapping up\u003C/h2>\u003Cp>Addressing common challenges in Looker Studio data blending one by one might take much of your precious time. So perhaps the best way to solve them is to use a marketing data platform that doesn’t have these issues and is much more intuitive to use. \u003C/p>\u003Cp>Whatagraph has a data blending feature that is fast, reliable, user-friendly, and 100% no-code. \u003C/p>\u003Cp>\u003Ca href=\"/book-a-call\">Book a call\u003C/a> with us and find out how Whatagraph can help you organize and report data on a scale!\u003C/p>",[497,524,551,578],{"id":498,"dateReorder":499,"title":500,"slug":501,"summary":502,"body":503,"read_time":50,"createdAt":504,"updatedAt":505,"publishedAt":506,"errors":31,"table_of_contents":32,"cover_image":507,"author":522,"article_category":523},2308,"2024-03-25","Data Blending: Clear Insights for Data-Driven Marketing","data-blending","\u003Cp>Decision-makers across industries increasingly depend on data-driven marketing insights. As data volume and complexity grow, agencies and their clients struggle to get more accurate insights. The good news is that data analytics is evolving, too. Thanks to quick and versatile applications of data blending, actionable, organized data is now within your grasp, without enlisting data specialists or complicated data integration tools. \u003C/p>","\u003Cp>Let’s consider an example. You’re a strategist in a PPC agency running a multi-level campaign that has seen a drop in conversions against the campaign KPIs. To make things worse, the contract review is due soon. \u003C/p>\u003Cp>What can you do to pinpoint the problem?\u003C/p>\u003Cp>You can pull resources from your agency’s data team and crunch the numbers, or in the worst-case scenario — do it yourself. \u003C/p>\u003Cp>Luckily, you now have access to powerful data-blending solutions that let you analyze the performance of different marketing channels much more effectively. \u003C/p>\u003Cp>In this article, you’ll learn more about data blending and how it works. On top of it, we’ll explore several real-world scenarios through different data blending use cases. \u003C/p>\u003Ch2>What is data blending?\u003C/h2>\u003Cp>Data blending is an operation of bringing together two or more data sets to visualize and analyze results in a way that doesn’t physically combine or alter the original data sets. \u003C/p>\u003Cp>The blended sources use external code and formulas, provided by the data analytics platform to query data sources separately and combine the read-only data into visualizations available in that platform. \u003C/p>\u003Cp>This approach blends data with much more flexibility and speed than other methods of combining data for analysis. \u003C/p>\u003Ch2>Data blending types\u003C/h2>\u003Cp>Although there is little difference between data blending types, there are different approaches depending on how you instruct your data blending tool to join data. \u003C/p>\u003Cp>A join is an operation through which the combined data sources are positioned one against another in your blend. \u003C/p>\u003Cp>When considering join types, think of data as two separate tables.\u003C/p>\u003Cp>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/Join_types_00c9a1690a.png\" alt=\"Join types for blending\">\u003C/p>\u003Cp>For example, you will use the inner join to match rows from two tables based on the common values and visualize them side by side in a table. Let’s say you want to combine sales data from Amazon Pay with an email address and a matching row from HubSpot with the same email address. An inner join matches this data based on the same email. \u003C/p>\u003Cp>On the other hand, an outer join combines data sets even if they don’t have matching values in a common key column. For example, you can combine two data sets with web traffic data — even if many of the URLs have changed over time as you publish new blog posts, product pages, etc. \u003C/p>\u003Cp>The left join is useful when you have one primary data source and multiple secondary data sources. Your primary data source will be on the left side of your data blend. In the case of data blending in Tableau, this layout allows you to perform a data analysis with different levels of granularity. \u003C/p>\u003Ch2>How does the data blending process work?\u003C/h2>\u003Cp>If you already feel overwhelmed after reading the previous section, you’ll be happy to hear that the kind of blending described above will be done by your data blending tool. \u003C/p>\u003Cp>Once you start using data blending tools, all the data blending processes will be intuitive and straightforward. However, it’s important for you to understand what output you’re looking for.\u003C/p>\u003Cp>\u003Cstrong>Step 1\u003C/strong>: Connect the sources you want to blend. In Whatagraph, your job is easy, as you have \u003Ca href=\"https://whatagraph.com/integrations\">direct native integrations\u003C/a> to 45+ popular marketing platforms. \u003C/p>\u003Cp>\u003Cstrong>Step 2\u003C/strong>: Create a blend of two or more sources. Keep in mind that you may run into limits here, depending on the tool you choose. With Looker Studio, for example, you can't add more than five sources in one blend. Learn more about \u003Ca href=\"https://whatagraph.com/blog/articles/data-blending-looker-studio\">the limitations of data blending in Looker Studio\u003C/a> and how to overcome them. \u003C/p>\u003Cp>\u003Cstrong>Step 3\u003C/strong>: Analyze the results in multi-source tables or other data visualization types. Whatagraph, for example, also allows you to apply a custom formula on the blended source level.  \u003C/p>\u003Ch2>Data blending use cases and scenarios\u003C/h2>\u003Cp>Now we will explore the two most common use cases of how to blend data in Whatagraph followed by two real-world examples. Whatagraph has developed \u003Ca href=\"https://whatagraph.com/organize\">a user-friendly Organize feature\u003C/a> that allows you to blend, aggregate, unify, and group data in the same environment where you connect sources and visualize insights. \u003C/p>\u003Cfigure class=\"media\">\u003Cdiv data-oembed-url=\"https://www.youtube.com/watch?v=jPBSI3My-5s\">\u003Cdiv style=\"position: relative; padding-bottom: 100%; height: 0; padding-bottom: 56.2493%;\">\u003Ciframe src=\"https://www.youtube.com/embed/jPBSI3My-5s\" style=\"position: absolute; width: 100%; height: 100%; top: 0; left: 0;\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen=\"\">\u003C/iframe>\u003C/div>\u003C/div>\u003C/figure>\u003Cp>Thanks to this intuitive and scalable feature, any marketer can learn how to make a blend of sources without any specialized training or coding. \u003C/p>\u003Cp>We’ll start with a basic case first.\u003C/p>\u003Ch3>Create a cross-channel table overview\u003C/h3>\u003Cp>\u003Cstrong>Goal\u003C/strong>: To create a cross-channel overview table where each metric column belongs to a different source. \u003C/p>\u003Cul>\u003Cli>Challenge 1: This is not possible with a standard table widget as it only supports one source. \u003C/li>\u003Cli>Challenge 2: This is not possible with a multi-source breakdown table either, as the source is always used as a dimension. \u003C/li>\u003C/ul>\u003Cp>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/Table_a7234ed838.png\" alt=\"Table outcome\">\u003C/p>\u003Cp>For this table, we want each column to represent different metrics from different sources.\u003C/p>\u003Cp>\u003Cstrong>How data blending helps\u003C/strong>:\u003C/p>\u003Cp>With data blending, we can create a single source that contains all the metrics from all the sources we’d like to include in our table. In other words, we’d use a standard table widget with the blended source and select the metrics we need. \u003C/p>\u003Cp>Steps we need to take:\u003C/p>\u003Cp>\u003Cstrong>1. Create a blended source\u003C/strong>\u003C/p>\u003Cp>From the Organize environment click on \u003Cstrong>Create new\u003C/strong> > \u003Cstrong>Blended source\u003C/strong>.\u003C/p>\u003Cp>\u003Cstrong>2. Select sources\u003C/strong> \u003C/p>\u003Cp>Choose sources and their metrics and dimensions that you want to include in your blend.\u003C/p>\u003Cp>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/Blending_start_883d391658.png\" alt=\"Start with data blending\">\u003C/p>\u003Cp>\u003Cstrong>3. Join sources\u003C/strong>\u003C/p>\u003Cp>Select a join type between each source and the dimensions you want to use as a join key. \u003C/p>\u003Cp>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/Join_setup_ee050626fc.png\" alt=\"Select join key\">\u003C/p>\u003Cp>If you’re used to the blending process in Looker Studio, the concept of joins keys and blending in general are similar to Whatagraph. \u003C/p>\u003Cp>\u003Cstrong>4. Blend data\u003C/strong>\u003C/p>\u003Cp>After completing these steps, you have a single data set that contains data from multiple sources. Once you use it in a report, you can manage it as a standard source, however the metrics you selected belong to different sources.   \u003C/p>\u003Cp>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/Blend_setup_2118cfe101.png\" alt=\"Inspect blend setup \">\u003C/p>\u003Cp>When you are building a report, you need to select your blended source. Open the channel list and go to the \u003Cstrong>Blended sources\u003C/strong> tab. \u003C/p>\u003Cp>Once you select the source, add a table widget and select the metrics. The metrics list will be broken down by sources — the source we selected for our blend. \u003C/p>\u003Cp>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/Metric_drawer_05922832b9.png\" alt=\"Report metric drawer\">\u003C/p>\u003Cp>This allows us to have each column of the table representing a metric from a different source. \u003C/p>\u003Cp>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/Outcome_a5e44cb976.png\" alt=\"Widget with blended data\">\u003C/p>\u003Cp style=\"text-align:right;\">\u003Ci>(The table highlights are not a part of the actual UI)\u003C/i>\u003C/p>\u003Cp>Moving on to another useful data blending use case.\u003C/p>\u003Ch3>Use a cross-channel calculation in the table overview\u003C/h3>\u003Cp>\u003Cstrong>Goal\u003C/strong>: To create a cross-channel metric calculation in the table widget that we could display next to standard metrics. \u003C/p>\u003Cp>Additionally, we want this metric to be reusable in other widgets or dashboards without us having to recreate it each time. \u003C/p>\u003Cp>Finally, we want to be able to edit such a metric from one place, and the changes to take effect on all areas where it’s used. \u003C/p>\u003Cp>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/Table_v_formula_ad18dc7488.png\" alt=\"Table with custom formula on blended source\">\u003C/p>\u003Cul>\u003Cli>Challenge 1: This is impossible with a standard table widget, as it only allows creating custom formulas from the same channel sources. \u003C/li>\u003Cli>Challenge 2: This is not possible with a multi-source breakdown table widget either, as it wouldn’t be able to visualize such a metric as a single column. \u003C/li>\u003Cli>Challenge 3: The current custom formulas are not reusable. If we make an error in the formula, we’d have to find every instance of such a formula and edit it manually. \u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>How data blending helps\u003C/strong>:\u003C/p>\u003Cp>Data blending allows us to create a single data set that contains metrics from multiple sources. We can use those metrics and create a custom-calculated metric that is specific to that blended source. We could then use that metric everywhere where we use that blended source. \u003C/p>\u003Cp>Steps we need to take:\u003C/p>\u003Cp>\u003Cstrong>1. Create a custom metric\u003C/strong>\u003C/p>\u003Cp>Since we already have a blended source created, we need to create a transformed metric for that blend. From the Organize environment click on \u003Cstrong>Create new \u003C/strong>>\u003Cstrong> Metric\u003C/strong>. \u003C/p>\u003Cp>Create a name for your new metric and select the \u003Cstrong>Rule type \u003C/strong>>\u003Cstrong> Formula\u003C/strong>. \u003C/p>\u003Cp>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/Rule_type_e31f447dcb.png\" alt=\"Select the rule type\">\u003C/p>\u003Cp>\u003Cstrong>2. Select the transformation level\u003C/strong>\u003C/p>\u003Cp>The transformational level determines whether the metric data is taken from a specific blended source or a different source selected in the report. Select \u003Cstrong>Blended source\u003C/strong>. \u003C/p>\u003Cp>\u003Cstrong>3. Create the formula \u003C/strong>\u003C/p>\u003Cp>First, we need to select a blended source for which we want to create a metric. Then, select the metric we want to use in the calculation — these are the same metrics we used to create our blended source. \u003C/p>\u003Cp>Once we’ve selected the metrics to use in the calculation, we can see which source each metric belongs to. \u003C/p>\u003Cp>Now the only thing that remains is to type in the formula using letter labels assigned to each metric, in our case (A+B) / (C+D):\u003C/p>\u003Cp>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/Formula_3685432c9c.png\" alt=\"Data blending formula\">\u003C/p>\u003Cp>After completing these steps we have a metric that is calculated from multiple sources. If you want to make any changes to it, those changes will affect all the widgets where you use it. \u003C/p>\u003Cp>In reports, you can use the metric in any widget where you have added your blended source. \u003C/p>\u003Cp>Open the metric selection list and go to the \u003Cstrong>Custom tab\u003C/strong>. \u003C/p>\u003Cp>At the top of the list, we can see calculated metrics we created specifically for this blended source. The CPL metric is one of them.\u003C/p>\u003Cp>After selecting all the needed metrics we can add them to the table. \u003C/p>\u003Cp>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/View_formula_7e1d38ec81.png\" alt=\"View formula in the report builder\">\u003C/p>\u003Cp>From here, we can also see what formula and metrics we used inside the calculation.\u003C/p>\u003Cp>Using these two workflows, you can easily cover a range of data blending use cases in in-house or agency marketing scenarios.\u003C/p>\u003Cp>Let’s mention a few of them.\u003C/p>\u003Ch3>\u003Cstrong>Scenario 1: Present how many times your ads were shown across all channels.\u003C/strong>\u003C/h3>\u003Cp>\u003Cstrong>Goal\u003C/strong>: Show total impressions and reach your ads received on Facebook, Instagram, and Google. \u003C/p>\u003Cp>Using Whatagraph’s intuitive \u003Ca href=\"https://whatagraph.com/blog/articles/marketing-data-transformation\">marketing data transformation\u003C/a> function, we can easily get the totals:\u003C/p>\u003Col style=\"list-style-type:decimal;\">\u003Cli>From the Organize environment click on \u003Cstrong>Create new\u003C/strong> > \u003Cstrong>Blended source\u003C/strong>.\u003C/li>\u003Cli>Add your Facebook, Instagram, and Google sources. \u003C/li>\u003Cli>Select \u003Ci>\u003Cstrong>Year, month\u003C/strong>\u003C/i> as the dimension and \u003Ci>\u003Cstrong>Impressions\u003C/strong>\u003C/i> and \u003Ci>\u003Cstrong>Reach\u003C/strong>\u003C/i> as metrics for all sources. \u003C/li>\u003Cli>Select the full outer join and pick the common dimension for all sources.\u003C/li>\u003Cli>Name your blend and click \u003Cstrong>Create a blend\u003C/strong> to blend your data.   \u003C/li>\u003C/ol>\u003Cp>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/Single_value_widget_baebe82346.png\" alt=\"Single value widget\">\u003C/p>\u003Cp>You’ve created a blended source that you can use in single-value widgets or tables to show the total impressions and reach of your ads on three different marketing channels. \u003C/p>\u003Ch3>\u003Cstrong>Scenario 2: Present how much marketing budget was spent on all your campaigns combined\u003C/strong>\u003C/h3>\u003Cp>\u003Cstrong>Goal: \u003C/strong>Show the total marketing budget spend on Google Ads and Facebook Ads combined\u003C/p>\u003Cp>Again, we start from the Organize environment:\u003C/p>\u003Col style=\"list-style-type:decimal;\">\u003Cli>Click \u003Cstrong>Create new > Blended source\u003C/strong>.\u003C/li>\u003Cli>Add your Facebook Ads and Google ads as sources.\u003C/li>\u003Cli>Select \u003Ci>\u003Cstrong>Year, month\u003C/strong>\u003C/i> as the dimension, and \u003Ci>\u003Cstrong>Amount spent\u003C/strong> \u003C/i>from Facebook Ads\u003Ci> \u003C/i>and\u003Ci> \u003Cstrong>Cost\u003C/strong> \u003C/i>from Google Ads as metrics. \u003C/li>\u003Cli>Select the full outer join and pick the selected dimension for all sources\u003C/li>\u003Cli>Name your blend and click \u003Cstrong>Create a blend\u003C/strong>.   \u003C/li>\u003Cli>Create a rule that would unify the names of these two metrics into one. \u003C/li>\u003Cli>From the Organize environment click \u003Cstrong>Create new > Metric\u003C/strong>.\u003C/li>\u003Cli>Give the metrics a name — \u003Cstrong>Ad spend\u003C/strong>. \u003C/li>\u003Cli>Choose the \u003Cstrong>Rule type \u003C/strong>>\u003Cstrong> Unify names\u003C/strong> and the \u003Cstrong>Rule level \u003C/strong>>\u003Cstrong> Channel level\u003C/strong>.\u003C/li>\u003Cli>In Selected channels & fields, choose \u003Cstrong>Facebook Ads \u003C/strong>>\u003Cstrong> Amount spent\u003C/strong>, \u003Cstrong>Google Ads \u003C/strong>>\u003Cstrong> Cost\u003C/strong>. \u003C/li>\u003C/ol>\u003Cp>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/Unify_metrics_f2f1e3cfea.png\" alt=\"Create new metrics for blended source\">\u003C/p>\u003Cp>You have created a new metric that you can use in reports in widgets with Facebook Ads and Google Ads blended source to show your total spend. \u003C/p>\u003Ch2>Benefits of data blending\u003C/h2>\u003Cp>Data blending has many advantages over other types of operations to combine data, e.g. data integration. It shouldn’t be a surprise then that \u003Ci>an easy way to blend data\u003C/i> has become one of the key requirements for agencies when \u003Ca href=\"https://whatagraph.com/blog/articles/marketing-data-platform\">choosing a marketing data platform\u003C/a>. \u003C/p>\u003Cp>\u003Cstrong>Granular view of the customer journey\u003C/strong>\u003C/p>\u003Cp>Data blending processes allow marketers to connect the dots between different marketing channels and customer behavior. By blending data from various sources, including Google Analytics, social media, and \u003Ca href=\"https://www.creatio.com/glossary/crm-marketing\" target=\"_blank\" rel=\"noopener noreferrer\">CRM marketing systems\u003C/a>, you can gain a holistic view of customer interactions. This knowledge helps you create personalized marketing strategies that resonate with your target audience.  \u003C/p>\u003Cp>\u003Cstrong>Improved campaign performance\u003C/strong>\u003C/p>\u003Cp>By identifying trends and patterns in data, you can optimize your or your client’s campaigns to reach the right audience at the right time, with the right message. Such a holistic approach to marketing strategy reduces marketing waste and increases ROI.\u003C/p>\u003Cp>\u003Cstrong>Identify new market segments\u003C/strong>\u003C/p>\u003Cp>Finally, blended data allows you to identify and explore new market segments. By analyzing demographics, psychographic, and behavioral data blends, you can discover hidden customer segments and approach them accordingly. \u003C/p>\u003Cp>All in all, cross-platform data blending provides you with means to drive more effective and efficient marketing strategies. A unified view of data is a source of insights that lead to better decision-making and, consequently, improved business performance.  \u003C/p>\u003Ch2>Challenges in data blending\u003C/h2>\u003Cp>Although data blending is a popular and powerful marketing data analytics practice, there are few challenges in data blending that you should keep in mind while looking for an ideal solution.\u003C/p>\u003Cp>\u003Cstrong>Depth of insight\u003C/strong>\u003C/p>\u003Cp>Once the number of different data sources begins to increase, data blending has shown to become glitchy. This is also the case if you have a large amount of data within a single source, such as thousands of new users in a month. \u003C/p>\u003Cp>When that starts to happen, it might indicate that your data blending use case is more suited to data integration. You should also ask yourself, whether your blended data analysis has become too complex, and whether it justifies the additional time and money to make it more advanced. \u003C/p>\u003Cp>\u003Cstrong>Control\u003C/strong>\u003C/p>\u003Cp>Depending on \u003Ca href=\"https://whatagraph.com/blog/articles/data-transformation-tools\">the data transformation tool you use\u003C/a>, you may not control all your data sources. Data from Google or Meta might be accurate, however, any inconsistencies from an outside data source may creep into your blend and need special attention. Also, if a source doesn’t easily aggregate key data you want to blend, you’ll need to solve that before creating the blend. \u003C/p>\u003Cp>When blending data sources in Looker Studio, for example, these inaccuracies are hard to notice. They usually result from how Looker Studio creates tables for your blend — querying data for each table before joining them into the final blend. Any data ranges, filters, or calculated fields on your tables before the join can affect the accuracy of your blend. \u003C/p>\u003Cp>\u003Cstrong>Processing blended sets\u003C/strong>\u003C/p>\u003Cp>Data blending allows you to analyze cross-platform data, however, exporting the new data set to a business intelligence tool such as Microsoft Power BI is more complicated. You may need to use additional, specialized software or employ a data analyst. But at that point, you’re moving away from the agency-minded usage of data blending. In Whatagraph, you can \u003Ca href=\"https://whatagraph.com/data-export\">export any table or other widget\u003C/a> with a blended data source in a spreadsheet format, as Excel or CSV files. \u003C/p>\u003Ch2>FAQs\u003C/h2>\u003Cp>\u003Cstrong>Why is data blending important for data analysis and reporting?\u003C/strong>\u003C/p>\u003Cp>Data blending allows data analysts to include any data type or any source into their analysis for faster, deeper, business insights. Combining data from two or more sources can reveal valuable information that might otherwise remain hidden even when data is visualized in reports. When used in reports, blended data often provides a new perspective that might lead to better business decisions. \u003C/p>\u003Cp>\u003Cstrong>What is the difference between data blending and data joining?\u003C/strong>\u003C/p>\u003Cp>Data blending allows you to combine data from multiple sources. Data joining, however, only lets you combine data from two sources, e.g. two SQL databases, and is restricted by the size of the dataset. Blending is better than joining if you want to determine if data needs to be cleansed or adjusted — for example, having null values and errors that need to be fixed.\u003C/p>\u003Cp>\u003Cstrong>What is better: data blending vs data integration?\u003C/strong>\u003C/p>\u003Cp>Data blending is a more versatile solution that doesn’t require creating a new data set when original data sources change. If there’s a change in any of the original sources, your data visualization solution will pick up the change and update the blended source automatically.\u003C/p>\u003Cp>On the other hand, \u003Ca href=\"https://whatagraph.com/blog/articles/marketing-data-connectors\">data integration\u003C/a> is an operation that physically combines multiple data into a single database or file. This can be done through data pipeline processes, such as ETL, which extracts data from multiple sources, transforms it into a common format, and loads it into a data warehouse. Data integration, it’s a much slower and process-intensive technique used for larger, more complex data combinations and is usually performed by a data scientist. \u003C/p>\u003Cp>\u003Cstrong>What considerations should be made when blending structured and unstructured data?\u003C/strong>\u003C/p>\u003Cp>When blending structured and unstructured data, the first consideration is to prioritize data from apps that directly impact your revenue, such as CRMs. \u003C/p>\u003Cp>The master data from these systems helps you orient key structured data and lays the framework for blending unstructured data through metadata.  \u003C/p>\u003Cp>Another consideration is to make sure that your unstructured data which comes in the form of documents, photos, videos, etc. has specific native metadata that allows you to link unstructured to structured data. \u003C/p>\u003Ch2>Streamline your data blending process with Whatagraph\u003C/h2>\u003Cp>Fast-moving agencies require accuracy and speed in both data organization and reporting. This is why data blending in Whatagraph is handled within the same environment where you visualize your data. \u003C/p>\u003Cp>No going back and forth between tools or modules. \u003C/p>\u003Cp>Blend any data source you have and create custom metrics for it to get the most out of your data and make right decisions fast. \u003C/p>\u003Cp>Sign up for a \u003Ca href=\"https://live.whatagraph.com/auth/register\">free trial\u003C/a> and start blending your data with minimal effort and zero room for error. \u003C/p>","2024-03-25T18:25:09.860Z","2025-05-16T13:09:18.326Z","2024-03-25T18:46:19.607Z",{"id":508,"name":509,"alternativeText":510,"caption":31,"width":427,"height":428,"formats":511,"hash":517,"ext":292,"mime":293,"size":518,"url":519,"previewUrl":31,"provider":296,"provider_metadata":31,"createdAt":520,"updatedAt":521},12139,"data_blending.png","Data Blending: Combine Data for Clear Insights",{"thumbnail":512},{"ext":292,"url":513,"hash":514,"mime":293,"name":515,"path":31,"size":516,"width":435,"height":436},"https://s3.us-east-2.amazonaws.com/whatagraph.com/thumbnail_data_blending_0ed75567f3.png","thumbnail_data_blending_0ed75567f3","thumbnail_data_blending.png",10.79,"data_blending_0ed75567f3",78.17,"https://s3.us-east-2.amazonaws.com/whatagraph.com/data_blending_0ed75567f3.png","2024-03-25T18:20:10.075Z","2024-03-25T18:20:25.136Z",{"id":461,"name":462,"about":463,"email":464,"createdAt":465,"updatedAt":466,"publishedAt":467,"slug":468,"linkedin_url":469},{"id":5,"title":443,"slug":444,"subheading":445,"createdAt":446,"updatedAt":447,"publishedAt":448},{"id":525,"dateReorder":526,"title":527,"slug":528,"summary":529,"body":530,"read_time":50,"createdAt":531,"updatedAt":532,"publishedAt":533,"errors":31,"table_of_contents":32,"cover_image":534,"author":549,"article_category":550},2306,"2024-02-05","Marketing Data Transformation: How to Organize Unstructured Marketing Data?  ","marketing-data-transformation","\u003Cp>You’re creating a quarterly marketing report and pulling data from several marketing data sources. But the data comes in unorganized. Do you really need sessions from 60 countries? Why not show just tier-1 countries where most conversions come from? Why scrolling through a long list of landing pages when grouping them by sections can reveal a trend faster? \u003C/p>","\u003Cp>What you need are clean and consistent reports and dashboards that actually help you discover insights quickly. This is where marketing data transformation enters the game. \u003C/p>\u003Ch2>What is marketing data transformation?\u003C/h2>\u003Cp>Marketers collect massive amounts of data, which is often scattered or unstructured. Data transformation techniques help organize and clean this data to make it more suitable for analysis and decision-making. Marketing data transformation includes techniques like data blending, mapping, and aggregation.  \u003C/p>\u003Cp>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/Data_transformation_e1e585c016.png\" alt=\"What is marketing data transformation\">\u003C/p>\u003Cp>By transforming unstructured marketing data, marketers can identify hidden patterns and improve data quality. Organized data, in return, helps marketers uncover insights and track campaign performance more easily. These insights can be used to optimize campaigns and demonstrate ROI to clients and, in turn, improve their retention.\u003C/p>\u003Ch2>What problems do marketers face working with unstructured data? (And how marketing data transformation helps)\u003C/h2>\u003Cp>Marketing data points gathered from different sources are often incompatible, preventing marketers from identifying trends and grasping the whole picture of their performance. \u003C/p>\u003Cp>Let’s take a look at some of the challenges marketers face:\u003C/p>\u003Cul>\u003Cli>\u003Cstrong>Different naming conventions make reporting difficult \u003C/strong>\u003C/li>\u003C/ul>\u003Cp>Different advertising platforms name dimensions and metrics like age, date, gender, etc., differently. So, for example, you can have “Country” on Google Ads and “Location” on Facebook Ads for the same dimension. Or have the date 2024-01 or 202401, depending on the channel. These differences prevent marketers from blending cross-channel metrics into a unified view, as each dimension is displayed separately.\u003C/p>\u003Cul>\u003Cli>\u003Cstrong>Clients get confused by missing value fields\u003C/strong>\u003C/li>\u003C/ul>\u003Cp>The data from connected marketing platforms might contain missing values or inconsistencies. Something like “not set” under the page path from GA4 could be considered a missing value. Such fields confuse readers, which can be solved by replacing those with more reader-friendly wording. Transformation allows us to filter out irrelevant or inconsistent values. \u003C/p>\u003Cul>\u003Cli>\u003Cstrong>It’s difficult to compare cross-channel data in multiple tables\u003C/strong>\u003C/li>\u003C/ul>\u003Cp>When you have Facebook Ads, Google Ads, and Amazon Ads, it’s much more reader-friendly to display the PPC metrics from all three channels in one table. Transformation can help marketers visualize data from various sources in one widget by blending several data sources into one data source.  \u003C/p>\u003Cul>\u003Cli>\u003Cstrong>Large or complex datasets are hard to read\u003C/strong>\u003C/li>\u003C/ul>\u003Cp>Some datasets are just too large or complex to comprehend, which slows down data analysis. Take, for example, GA4 country dimensions. You can easily have 200+ countries listed in a table. You need a way to split the countries into custom dimension groups by tiers, regions, etc. Creating blends or custom dimensions simplifies your data and makes it more manageable for visualization and decision-making.  \u003C/p>\u003Cul>\u003Cli>\u003Cstrong>The API access token runs out before the end of the month \u003C/strong>\u003C/li>\u003C/ul>\u003Cp>Let’s say you are creating a 20-page Google Analytics report. Using a single \u003Ca href=\"https://whatagraph.com/integrations/ga4\">GA4 source\u003C/a> will time out your API access token. However, if you simplify the customer data and group it through blends or custom dimensions, you won’t need so many API requests. Instead of 5 tables, you’ll be querying one.  \u003C/p>\u003Ch2>Marketing data transformation use cases \u003C/h2>\u003Cp>Now, we will explore different use cases for data transformation in marketing that help teams manage unstructured data and draw actionable insights that help shape data-driven business decisions. \u003C/p>\u003Cp>We’ll start our list of marketing data transformation examples from simpler ones, such as:\u003C/p>\u003Ch2>Unify names of metrics and dimensions\u003C/h2>\u003Cp>Data mapping on the source or channel level allows you to give standardized names to cross-channel dimensions and metrics. This marketing data transformation is useful when different integrations have dimensions or metrics that give the same value but are named differently. \u003C/p>\u003Cp>Apart from unifying the names of metrics and dimensions, you can use the transformation to:\u003C/p>\u003Cul>\u003Cli>Categorize campaigns into the specific part of the funnel, e.g., \u003Ci>engagement\u003C/i>\u003C/li>\u003Cli>Aggregate results from clients' campaigns, ads, or ad sets by a specific keyword\u003C/li>\u003Cli>Make tables and widgets easier to read\u003C/li>\u003Cli>Create categories for specific countries and see results for them\u003C/li>\u003Cli>Globally translate metrics for your entire team\u003C/li>\u003Cli>Help users in agencies with less report-creating experience find the metrics they need quickly, as these will be named consistently\u003C/li>\u003C/ul>\u003Cp>Here are a few examples of marketing data mapping:\u003C/p>\u003Ch3>\u003Cstrong>Example 1: Map dimensions by unifying names \u003C/strong>\u003C/h3>\u003Cp>Your client is running Facebook Ads and Google Ads, and you want to present the impressions by device. \u003C/p>\u003Cp>Whatagraph allows you to easily unify different naming conventions like “Platform/Device/Device Type” into a single dimension e.g., “Device” or “Mobile/Mobile App/MOBILE” to a more convenient “Mobile” across different sources or channels.\u003C/p>\u003Cp>As a result, you have more consistent information in your reports.\u003C/p>\u003Ch3>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/unify_example_1_a665740ee2.png\" alt=\"unify_example_1.png\">\u003Cbr>\u003Cstrong>Example 2: Simplify long campaign names \u003C/strong>\u003C/h3>\u003Cp>Situation: You’re tracking campaigns for your client, but tables within your report have long dimension names or are overwhelmed by many values.\u003C/p>\u003Cp>With Whatagraph, you can simply create new dimension names for your campaigns on a source or channel level using conditions. \u003C/p>\u003Cp>Again, your reports become much neater and easier to understand to the end-reader. \u003Cbr>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/unifu_example_2_82b09e74dc.png\" alt=\"Simplify long campaign names\">\u003C/p>\u003Ch3>\u003Cstrong>Example 3: Shorten and unify field names that are confusing to the end reader\u003C/strong>\u003C/h3>\u003Cp>API endpoints are often difficult to understand. Whatagraph report tables may sometimes display [blank] or [empty] outputs as those values come directly from the API. \u003C/p>\u003Cp>However, Whatagraph allows you to set up a rule and easily change those fields into something that would make more sense to clients as end-readers of the report. \u003C/p>\u003Cp>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/unify_example_3_c5f4d747df.png\" alt=\"Shorten and unify field names that are confusing to the end reader\">\u003C/p>\u003Ch3>\u003Cstrong>Example 4: Translate your displayed metrics and dimensions globally\u003C/strong>\u003C/h3>\u003Cp>An agency regularly provides reports in their native language and struggles to translate every data point coming from the APIs. A large number of saved, pre-made widgets can solve the problem, but whenever clients add a source or have a different reporting need, they need to start from scratch. \u003C/p>\u003Cp>Whatagraph has a quicker and more scalable solution. Create a condition rule on a channel or source level to replace standard English metric and dimension names with their native language counterparts. This way, you can translate a bunch of metrics and dimensions at once, which also makes it easier for team members to use them in the future. \u003C/p>\u003Cp>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/unify_example_4_86f8dcaea4.png\" alt=\"Translate your displayed metrics\">\u003C/p>\u003Ch2>Blending cross-channel data\u003C/h2>\u003Cp>Blending is a data transformation process of combining or merging data from sources to create a unified and comprehensive dataset. \u003C/p>\u003Cp>The process of blending combines datasets that have related but distinct dimensions or metrics, helping marketers compare information from various sources seamlessly. \u003C/p>\u003Cp>Whatagraph allows non-technical users to blend marketing data in a user-friendly, no-coding way. You can effortlessly execute blends on large volumes of data to match and consolidate relevant data points. \u003C/p>\u003Cp>This allows you to create comprehensive visualizations that can reveal correlations, patterns, or trends across disparate datasets. As a result, you can gain a complete understanding of a certain segment of your marketing data. \u003C/p>\u003Cp>Here are a few examples of marketing data blending:\u003C/p>\u003Ch3>\u003Cstrong>Example 1: Blend marketing data sources in one group for quick access\u003C/strong>\u003C/h3>\u003Cp>Let’s say that one of your clients uses Facebook Page, Instagram, and LinkedIn. \u003C/p>\u003Cp>You can \u003Ca href=\"https://whatagraph.com/blog/articles/data-blending\">create a data blend\u003C/a> with all three sources. Selecting “Date” as the join key, and include only those metrics in the blend that are relevant for the client.\u003C/p>\u003Cp>When you connect the blend to a report, the client will easily spot only the selected metrics from the three sources. \u003Cbr>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/blend_example_1_4d0d19932b.png\" alt=\"Blend marketing data sources in one group\">\u003C/p>\u003Ch3>\u003Cstrong>Example 2: Aggregate campaign data inside one table\u003C/strong>\u003C/h3>\u003Cp>In this scenario, your client uses Facebook Ads and Google Ads, with the same naming conventions for both sources. \u003C/p>\u003Cp>You can create a data blend with both data sources and select “Week” as the join key. \u003C/p>\u003Cp>When you connect the blend to the report, your clients can see the aggregated campaign results for each week. \u003Cbr>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/blend_example_2_3a31e3d42a.png\" alt=\"Aggregate campaign data inside one table\">\u003C/p>\u003Ch3>\u003Cstrong>Example 3: Aggregate results from sources that belong to the same channel\u003C/strong>\u003C/h3>\u003Cp>Your client runs three Facebook Pages, and they want to aggregate their results. \u003C/p>\u003Cp>Whatagraph allows you to easily create a data blend with all three sources with Full Outer join and choose “Date” as the join key. \u003C/p>\u003Cp>The only thing that remains is to connect the blend to a report and select the “Sum of [metric]” for any repeating metrics in the blend.  \u003C/p>\u003Cp>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/blend_example_3_208492a6a9.png\" alt=\"Aggregate results from sources that belong to the same channel\">\u003C/p>\u003Ch3>\u003Cstrong>Example 4: Create a custom cross-source calculation\u003C/strong>\u003C/h3>\u003Cp>Let’s go back to the client from Example 2 who uses Facebook Ads and Google Ads, and you want to calculate the cost of individual impressions for both sources + add an agency markup.\u003C/p>\u003Cp>You can easily create a new metric, “Total cost with markup”. \u003C/p>\u003Cp>Select “Formula” as a rule type and select the source data blend you want to use. You’ll use the same blend you created in Example 2 (Facebook Ads + Google Ads).\u003C/p>\u003Cp>Select the metric A — “Amount Spent” from Facebook Ads and the metric B — “Cost” from Google Ads and enter the formula A+B*1.2, where 1.2 is the agency markup. \u003C/p>\u003Cp>Set the value type to “Currency”, the increase logic to “Negative”, and create the metric. \u003C/p>\u003Cp>Finally, you can visualize the data in a table by selecting “Date” as the dimension and picking all three metrics.\u003C/p>\u003Cp>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/blend_example_4_6410e9b148.png\" alt=\"Create a custom cross-source calculation\">\u003C/p>\u003Ch2>Aggregate metrics and dimensions from blended data sources\u003C/h2>\u003Cp>Data blends that have metrics that match multiple sources will automatically suggest potential aggregations that are available, e.g. “Spend“ from Google Ads and “Spend” from Facebook Ads.\u003C/p>\u003Cp>These are available from Whatagraphs’ transformation metric creation interface and directly when creating a new widget with blended sources.\u003C/p>\u003Cp>But how does this actually help?\u003C/p>\u003Cp>This type of data transformation saves a lot of time for users who are adding the same metric a lot of times and reusing it inside the report. Whatagraph suggests possible aggregate options. \u003C/p>\u003Cp>And if you update the formula or blend in any way, the aggregate will automatically adjust inside any report using it. For example, if you have to delete one source from the blend. \u003C/p>\u003Cp>Let’s consider a few examples of marketing data aggregation:\u003C/p>\u003Ch3>\u003Cstrong>Example 1: Group multiple countries into regional dimensions or tiers\u003C/strong>\u003C/h3>\u003Cp>Your client is interested in tracking conversions or other user actions by regions or country tiers specific to their business. \u003C/p>\u003Cp>With Whatagraph, your job is easy. Just set up a condition rule on the channel or source level and include the countries into a regional dimension you create. \u003C/p>\u003Ch3>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/aggregate_example_1_5cb8ca900a.png\" alt=\"Group multiple countries into regional dimensions or tiers\">\u003Cbr>\u003Cstrong>Example 2: Classify and group conversion events\u003C/strong>\u003C/h3>\u003Cp>An agency serves a portfolio of global clients, and what they see as a conversion or goal often varies, depending on the channel. So, the account managers must standardize metrics names for individual clients based on integration. \u003C/p>\u003Cp>Using Whatagraph’s Organize feature, account managers can easily create rules on the source level for each individual client, depending on their definition of a conversion. They can effortlessly replicate the process for each individual channel and change the metric names to be consistent. \u003C/p>\u003Cp>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/aggregate_example_2_6cdc10a0d0.png\" alt=\"Classify and group conversion events\">\u003C/p>\u003Ch3>\u003Cstrong>Example 3: Group multiple campaigns data as a new dimension\u003C/strong>\u003C/h3>\u003Cp>A freelance data analyst is regularly hired by businesses and agencies to help with visualizing client marketing data from complex datasets. One common request is to attribute marketing efforts to specific funnel stages. \u003C/p>\u003Cp>The agency has the naming convention for their campaigns, ad sets, and ads, and they want to streamline the information within the reports, not to overload the end reader. \u003C/p>\u003Cp>The analyst can spend time creating custom columns in Google Sheets or CSV, attributing specific keywords to the correct part of the funnel, OR they can use Whatagraph’s elegant solution.\u003C/p>\u003Cp>Using the Organize feature, the analyst can create a set of rules to attribute keywords in campaign names to different stages in the funnel, such as Awareness, Conversion, etc.\u003C/p>\u003Cp>The newly created dimension shows what part of the funnel each campaign or ad set belongs to, making the report easier to read. In addition, the simple transformation saves time and allows account managers to rely less on data analysts for data maintenance tasks. \u003C/p>\u003Cp>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/aggregate_example_3_c45342caee.png\" alt=\"Group multiple campaigns data as a new dimension\">\u003C/p>\u003Ch3>\u003Cstrong>Example 4: Categorize and group landing page names\u003C/strong>\u003C/h3>\u003Cp>In a similar way, account managers can use Whatagraph to transform data by creating a new dimension that would group all pages with a specific keyword in the URL. \u003C/p>\u003Cp>Simply set the rule on a source or channel level that ensures that each landing page containing e.g., “blog” is automatically attributed to a new dimension “Blog” that you create on the same spot. \u003C/p>\u003Ch2>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/aggregate_example_4_7e5cba08b5.png\" alt=\"Categorize and group landing page names\">\u003Cbr>The easiest way to organize data — Whatagraph \u003C/h2>\u003Cp>Whatagraph is a marketing data platform to connect, \u003Cstrong>organize\u003C/strong>, visualize, and share all your data. It can replace multiple complex tools with one easy-to-use platform that covers the entire data journey.\u003C/p>\u003Cp>The \u003Ca href=\"https://whatagraph.com/organize\">Organize step\u003C/a> is essential in this process, as it allows non-technical staff like account executives to run different data transformation tasks in an intuitive, no-code way. \u003C/p>\u003Cfigure class=\"media\">\u003Cdiv data-oembed-url=\"https://www.youtube.com/watch?v=jPBSI3My-5s\">\u003Cdiv style=\"position: relative; padding-bottom: 100%; height: 0; padding-bottom: 56.2493%;\">\u003Ciframe src=\"https://www.youtube.com/embed/jPBSI3My-5s\" style=\"position: absolute; width: 100%; height: 100%; top: 0; left: 0;\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen=\"\">\u003C/iframe>\u003C/div>\u003C/div>\u003C/figure>\u003Cp>Using Whatagraph, marketers can easily:\u003C/p>\u003Cul>\u003Cli>Standardize outputs from different sources across reports and dashboards\u003C/li>\u003Cli>Unify internal dimension and metric naming conventions \u003C/li>\u003Cli>Group campaigns together or combine data from multiple countries in one tier\u003C/li>\u003Cli>Display the total performance of multiple landing pages\u003C/li>\u003Cli>Blend cross-channel data and create new cross-channel metrics\u003C/li>\u003Cli>Summarize data from multiple pages \u003C/li>\u003Cli>Aggregate campaign data and add agency markup\u003C/li>\u003Cli>Replace confusing outputs from marketing sources into something more understandable\u003C/li>\u003C/ul>\u003Cp>— Together with many more benefits of data transformation that make working with marketing data fast and manageable for teams who don’t want to hire a data specialist. \u003C/p>\u003Cp>Don’t splurge on a complex data transformation tool that you need a Python or SQL data expert to use and an equally complex business intelligence tool to visualize the transformed data.\u003C/p>\u003Cp>Empower your marketing team to transform marketing data with no hassle. \u003C/p>\u003Cp>\u003Ca href=\"https://whatagraph.com/book-a-call\">Book a demo\u003C/a> of Whatagraph today.\u003C/p>\u003Ch3>One marketing data platform to manage all your data\u003C/h3>\u003Cp>It's not a rare case that marketers use multiple tools to manage their data. This means having a data pipeline to gather data from disparate sources, \u003Ca href=\"https://whatagraph.com/blog/articles/data-transformation-tools\">a data transformation tool\u003C/a> to organize the data, and another data visualization tool to present the cleansed data to the end-readers. \u003C/p>\u003Cp>\u003Cstrong>However, the “fragmented data stack” approach has several pitfalls. \u003C/strong>\u003C/p>\u003Cp>The demand for technical expertise may pose a barrier, especially for smaller agencies. The integration challenges can result in long implementation times that delay realizing actionable insights. \u003C/p>\u003Cp>Maintaining a fragmented marketing data stack may also require training, integration, and ongoing maintenance costs. These hidden costs work against the very benefits the tools claim to provide. Also, the cost of using multiple specialized tools can easily increase, straining the budgets of smaller marketing teams. \u003C/p>\u003Cp>\u003Cstrong>On the other hand, \u003C/strong>\u003C/p>\u003Cp>As an all-in-one platform, Whatagraph is an excellent solution for marketing agencies and in-house teams that want to turn siloed and unstructured data into presentation-ready reports and dashboards at scale without technical expertise. \u003C/p>\u003Cp>Fully managed connectors allow users to quickly and reliably integrate \u003Ca href=\"https://whatagraph.com/integrations\">more than 45 digital marketing sources\u003C/a> like social media, PPC, website analytics, SEO, email marketing, e-commerce, and CRM platforms.  \u003C/p>\u003Cp>To connect any other source, you can use a Custom API, Google Sheets, or Google BigQuery data warehouse. \u003C/p>\u003Cp>Any data blend, calculation, new dimension, or metrics you create through the Organize feature you can readily use in visualizations.\u003C/p>\u003Cp>Whatagraph allows you to create stunning and insightful data visualizations in a few clicks and tell the story in a way stakeholders and clients can easily understand. We have 50+ report and dashboard templates, and you can save almost anything you create yourself as a template.\u003C/p>\u003Cp>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/Intuitive_report_builder_582d023abf.png\" alt=\"Whatagraph report builder\">\u003C/p>\u003Cp>In addition to effortless connecting, organizing, and visualizing your marketing data, sharing is another step where Whatagraph can save you many hours to focus on what matters. \u003C/p>\u003Cp>Whatagraph lets you automate insight sharing through scheduled emails with reports attached or live links for near real-time access. \u003C/p>\u003Ch2>Conclusion\u003C/h2>\u003Cp>Marketing agencies and in-house teams increasingly deal with big data that comes unorganized from scattered marketing sources. \u003C/p>\u003Cul>\u003Cli>To present the insights and digestible form for end users, \u003C/li>\u003Cli>To extract valuable insights from large or complex data sets and \u003C/li>\u003Cli>To conveniently present cross-channel data, you need marketing data transformation.\u003C/li>\u003C/ul>\u003Cp>Until recently, this meant buying a complex data transformation tool and hiring an external data engineer to sort things out. \u003C/p>\u003Cp>Now, marketers have a no-code solution that makes blending, aggregating, and renaming connected data much faster, easier, and ready for visualization and sharing. \u003C/p>","2024-02-05T09:35:13.574Z","2024-04-15T17:54:32.701Z","2024-02-05T09:35:29.276Z",{"id":535,"name":536,"alternativeText":537,"caption":31,"width":427,"height":428,"formats":538,"hash":544,"ext":292,"mime":293,"size":545,"url":546,"previewUrl":31,"provider":296,"provider_metadata":31,"createdAt":547,"updatedAt":548},11912,"mdata_transformation.png","Marketing Data Transformation - Guide & Examples",{"thumbnail":539},{"ext":292,"url":540,"hash":541,"mime":293,"name":542,"path":31,"size":543,"width":435,"height":436},"https://s3.us-east-2.amazonaws.com/whatagraph.com/thumbnail_mdata_transformation_5d68957eb7.png","thumbnail_mdata_transformation_5d68957eb7","thumbnail_mdata_transformation.png",14.3,"mdata_transformation_5d68957eb7",325.16,"https://s3.us-east-2.amazonaws.com/whatagraph.com/mdata_transformation_5d68957eb7.png","2024-02-05T08:56:33.305Z","2024-02-05T08:56:48.644Z",{"id":461,"name":462,"about":463,"email":464,"createdAt":465,"updatedAt":466,"publishedAt":467,"slug":468,"linkedin_url":469},{"id":5,"title":443,"slug":444,"subheading":445,"createdAt":446,"updatedAt":447,"publishedAt":448},{"id":552,"dateReorder":553,"title":554,"slug":555,"summary":556,"body":557,"read_time":50,"createdAt":558,"updatedAt":559,"publishedAt":560,"errors":31,"table_of_contents":32,"cover_image":561,"author":576,"article_category":577},2305,"2024-01-28","Top 15 Data Transformation Tools for Marketers in 2025","data-transformation-tools","\u003Cp>Data transformation is an essential part of data management, and in the data-rich marketing environment of today, a step you can’t afford to miss. In this article, we review some of the most capable data transformation tools available to marketers.\u003C/p>","\u003Ch2>What is data transformation in marketing?\u003C/h2>\u003Cp>Data transformation is the process of converting, cleansing, and structuring marketing data into a usable format that you can analyze to gain actionable insights and support decision-making. Data transformation is often used when integrating data from diverse marketing sources or when marketing analytics requires grouping or blending certain data points before the analysis.   \u003C/p>\u003Cp>In our recent article on \u003Ca href=\"https://whatagraph.com/blog/articles/marketing-data-transformation\">marketing data transformation\u003C/a>, we talked in more depth about its benefits and capabilities. But which tools give you the most options and smoothest journey when transforming your marketing data?\u003C/p>\u003Ch2>How to choose a marketing data transformation tool?\u003C/h2>\u003Cp>When it comes to choosing the best data transformation software for your marketing agency or company, the answer becomes much easier if you know what you’re looking for. These four questions can help you filter out these 15 tools and base the decision on your needs. \u003C/p>\u003Cul>\u003Cli>What are your marketing data sources?\u003C/li>\u003Cli>What kind of data transformations do you need to run?\u003C/li>\u003Cli>What is the destination for your transformed marketing data?\u003C/li>\u003Cli>What kind of professional support are you looking to get?\u003C/li>\u003C/ul>\u003Cp>When you answer these questions, you can better understand your needs and choose the best marketing data transformation tool for your outfit. \u003C/p>\u003Ch2>15 best marketing data transformation tools\u003C/h2>\u003Cp>Now, let’s take a look at the most significant marketing data transformation tools available on the market. The pros and cons of each tool come from our experience testing different tools, as well as verified user reviews on websites like Capterra, TrustRadius, and G2. \u003C/p>\u003Ch3>1. Whatagraph\u003C/h3>\u003Cp>Whatagraph is an all-in-one marketing data platform to connect, organize, visualize, and share all your marketing data. With Whatagraph, you can replace multiple slow and complex tools with one platform. Unlike many competitors, Whatagraph is easy to use and allows anyone in a team to manage and organize unstructured, scattered cross-channel data. Using no-code transformations, you can easily blend, unify, and group data points and create custom metrics and dimensions. \u003C/p>\u003Cp>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/Cross_channel_results_267af89bfe.png\" alt=\"Cross-channel results.png\">\u003C/p>\u003Cp>At the same time, Whatagraph allows you to visualize the organized and prepared cross-channel data. You can quickly turn it into analysis- or presentation-ready reports, dashboards, or standalone graphs, charts, funnels, or tables.\u003C/p>\u003Cp>Finally, automate how you share or deliver that data to any destination: clients, stakeholders, teammates, or other tools and platforms.\u003C/p>\u003Cp>\u003Cstrong>Best for\u003C/strong>: Marketing agencies and businesses that need to manage marketing data on scale. This includes connecting marketing data from scattered sources, code-less data transformations, quick and engaging visualizations, and automated insight sharing. \u003C/p>\u003Cp>\u003Cstrong>Pros\u003C/strong>:\u003C/p>\u003Cul>\u003Cli>One platform\u003C/li>\u003Cli>Intuitive to use\u003C/li>\u003Cli>Personal onboarding\u003C/li>\u003Cli>Fully managed integrations to popular marketing tools\u003C/li>\u003Cli>Connect any source via a custom API, Google Sheets, or Google BigQuery\u003C/li>\u003Cli>No-code data transformations\u003C/li>\u003Cli>AI insights\u003C/li>\u003Cli>Visualize transformed data in the same platform\u003C/li>\u003Cli>Automated report sharing via scheduled emails or live links\u003C/li>\u003Cli>Live chat customer support\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Cons\u003C/strong>:\u003C/p>\u003Cul>\u003Cli>No freemium plan\u003C/li>\u003Cli>Currently only BigQuery available as a destination\u003C/li>\u003C/ul>\u003Cfigure class=\"media\">\u003Cdiv data-oembed-url=\"https://www.youtube.com/watch?v=jPBSI3My-5s\">\u003Cdiv style=\"position: relative; padding-bottom: 100%; height: 0; padding-bottom: 56.2493%;\">\u003Ciframe src=\"https://www.youtube.com/embed/jPBSI3My-5s\" style=\"position: absolute; width: 100%; height: 100%; top: 0; left: 0;\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen=\"\">\u003C/iframe>\u003C/div>\u003C/div>\u003C/figure>\u003Ch3>2. Matillion\u003C/h3>\u003Cp>Matillion is an ETL tool for Amazon Redshift, Azure, Google BigQuery, Snowflake, and Synapse. It’s a missing piece between your raw data sources and your business intelligence (BI) and analytics tools. Matillion eliminates the manual-intensive activity of extracting, transforming, and loading marketing data from your on-premise server to one of your destinations. You can use the tool to set up automated data transformation as and when required by your BI tools or to set up complex business rules. \u003C/p>\u003Cp>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/matillion_70af5e5b53.png\" alt=\"Matillion data transformation tool\">\u003C/p>\u003Cp>\u003Cstrong>Best for\u003C/strong>: Medium-sized businesses and enterprises that import their marketing data into data warehouses and data lakes before manipulating and cleaning it. \u003C/p>\u003Cp>\u003Cstrong>Pros\u003C/strong>:\u003C/p>\u003Cul>\u003Cli>Quick and easy to set up\u003C/li>\u003Cli>No-code ELT and data pipelines\u003C/li>\u003Cli>Move data easily from any source or cloud platform \u003C/li>\u003Cli>AI-generated documentation for pipeline management\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Cons\u003C/strong>:\u003C/p>\u003Cul>\u003Cli>Needs another tool to visualize data\u003C/li>\u003Cli>Lacks built-in monitoring and error reporting\u003C/li>\u003Cli>No event-based task scheduling \u003C/li>\u003C/ul>\u003Ch3>3. Hevo Data\u003C/h3>\u003Cp>Hevo Data is a bi-directional no-code data pipeline that helps users replicate marketing data from any source with zero maintenance. You can easily load the data to your desired data warehouse while enriching it and transforming it into an analysis-ready form. Hevo offers a range of pre-built transformations you can run using a Python or intuitive drag-and-drop interface. \u003C/p>\u003Cp>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/Hevo_89cfdca923.png\" alt=\"Hevo data transformation tool\">\u003C/p>\u003Cp>\u003Cstrong>Best for\u003C/strong>: Data scientists and data engineers who need to replicate data from several different sources.\u003C/p>\u003Cp>\u003Cstrong>Pros\u003C/strong>:\u003C/p>\u003Cul>\u003Cli>Near-real-time data replication\u003C/li>\u003Cli>Intuitive dashboards for pipeline monitoring\u003C/li>\u003Cli>Architecture scalability with zero data loss\u003C/li>\u003Cli>Pre-built connectors for 150 SaaS apps and databases\u003C/li>\u003Cli>24x7 customer support\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Cons\u003C/strong>:\u003C/p>\u003Cul>\u003Cli>Not all schema are auto-mapped, some need manual mapping\u003C/li>\u003Cli>Pipelines sometimes take time to load\u003C/li>\u003Cli>Customer support is complex\u003C/li>\u003C/ul>\u003Ch3>4. Alteryx Designer Cloud\u003C/h3>\u003Cp>Alteryx (ex-Trifacta) is an easy-to-use visual data engineering and data wrangling cloud platform. This data transformation tool is built for more technical users who prepare, clean, and transform raw marketing data and visualize the insights on a regular basis. Alteryx uses AI service and support, so users should already have some working experience with similar solutions. If your marketing team lacks a dedicated technical data-guy, this data transformation software might not be the best fit. \u003C/p>\u003Cp>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/Alteryx_d590613530.png\" alt=\"Alteryx data transformation tool\">\u003C/p>\u003Cp>\u003Cstrong>Best for\u003C/strong>: Data analysts, data scientists, and data engineers with experience with similar tools. \u003C/p>\u003Cp>\u003Cstrong>Pros\u003C/strong>:\u003C/p>\u003Cul>\u003Cli>Intuitive UI\u003C/li>\u003Cli>Amazing GitHub stars and forks\u003C/li>\u003Cli>Great ELT capabilities\u003C/li>\u003Cli>Automated machine learning\u003C/li>\u003Cli>Generative AI insights\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Cons\u003C/strong>:\u003C/p>\u003Cul>\u003Cli>Only 100MB files supported in the free version\u003C/li>\u003Cli>The web interface can be buggy — use the desktop app\u003C/li>\u003Cli>Reports available as PowerPoint, textual emails, or messages\u003C/li>\u003Cli>No option to customize the visuals\u003C/li>\u003C/ul>\u003Ch3>5. Rivery\u003C/h3>\u003Cp>As a fully managed DataOps platform, Rivery is suitable for all organizational data tasks, allowing users to seamlessly manage, transform, and automate data models. If you are looking for a scalable solution for SQL and Python transformations, data ingestion, orchestration, and workflow automation, Rivery has it. Thanks to the native Python support, Rivery makes transforming marketing data easy from any system. \u003C/p>\u003Cp>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/Rivery_5e2eec70bd.png\" alt=\"Rivery data transformation tool\">\u003C/p>\u003Cp>\u003Cstrong>Best for\u003C/strong>: Data analyst and data scientists transforming marketing data from a variety of sources, including databases.\u003C/p>\u003Cp>\u003Cstrong>Pros\u003C/strong>: \u003C/p>\u003Cul>\u003Cli>200+ native connectors\u003C/li>\u003Cli>Freemium plan included\u003C/li>\u003Cli>User-friendly interface for building dependencies\u003C/li>\u003Cli>No setup fee\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Cons\u003C/strong>:\u003C/p>\u003Cul>\u003Cli>Understanding error prompts requires adequate data knowledge\u003C/li>\u003Cli>Training required to use all features properly\u003C/li>\u003C/ul>\u003Ch3>6. Keboola\u003C/h3>\u003Cp>Keboola is a self-service data operations platform used to automate much of your marketing data operations. To connect your data, you can use one of 400+ connectors or the low-code component framework that allows you to connect any source. Business users can choose no-code transformations to do the basic stuff, and tech-savvy teams can transform data using Python, SQL, or R.  \u003C/p>\u003Cp>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/Keboola_87d13d4b71.png\" alt=\"Keboola data transformation tool\">\u003C/p>\u003Cp>\u003Cstrong>Best for\u003C/strong>: Data engineers, data analysts, and analytics engineers to collaborate on analytics and automation and execute marketing data operations from extraction, transformation, data management, and pipeline orchestration to reverse ETL. \u003C/p>\u003Cp>\u003Cstrong>Pros\u003C/strong>:\u003C/p>\u003Cul>\u003Cli>400+ connectors out of the box\u003C/li>\u003Cli>Customized data sourcing\u003C/li>\u003Cli>1:1 pipeline development environment\u003C/li>\u003Cli>No-code and low-code data transformations\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Cons\u003C/strong>:\u003C/p>\u003Cul>\u003Cli>No mobile phone app\u003C/li>\u003Cli>Lacks compatibility with some OS\u003C/li>\u003C/ul>\u003Ch3>7. RudderStack\u003C/h3>\u003Cp>RudderStack is positioned as a data pipeline platform that makes data integration painless with real-time event delivery. You can integrate this data transformation tool into your existing data warehouse or infrastructure and use it to set up transformations in a sandbox environment. This way, you can review and debug every transformation before it goes live. \u003C/p>\u003Cp>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/Rudderstack_6278dcee2c.png\" alt=\"RudderStack data transformation tool\">\u003C/p>\u003Cp>\u003Cstrong>Best for\u003C/strong>: More experienced data engineers and analysts comfortable working with JSONs and manipulating APIs. \u003C/p>\u003Cp>\u003Cstrong>Pros\u003C/strong>:\u003C/p>\u003Cul>\u003Cli>One source to rule many other destinations\u003C/li>\u003Cli>Custom data integrations\u003C/li>\u003Cli>Slack team channel \u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Cons\u003C/strong>:\u003C/p>\u003Cul>\u003Cli>Users need some technical background\u003C/li>\u003Cli>Not all destination connectors are full-featured\u003C/li>\u003C/ul>\u003Ch3>8. Talend\u003C/h3>\u003Cp>Talend is a big data and cloud data integration tool leading in open-source integration applications. To support your marketing data transformation process, Talend partners with leading cloud service providers, including Amazon Web Services (AWS), Google Cloud Platform, and Snowflake. Marketers can use it to perform advanced data analytics and ETL operations with over 900 connectors. \u003C/p>\u003Cp>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/Talend_d9473639a5.png\" alt=\"Talend data transformation tool\">\u003C/p>\u003Cp>\u003Cstrong>Best for\u003C/strong>: Data teams and engineers to move massive amounts of data in real time. \u003C/p>\u003Cp>\u003Cstrong>Pros\u003C/strong>:\u003C/p>\u003Cul>\u003Cli>One platform for \u003Ca href=\"https://www.docsumo.com/blogs/data-extraction/techniques\" target=\"_blank\" rel=\"noopener noreferrer\">data extraction\u003C/a>, transformation, and loading\u003C/li>\u003Cli>Scalable, open-source solution\u003C/li>\u003Cli>Specialized ETL process \u003C/li>\u003Cli>Low error rates\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Cons\u003C/strong>:\u003C/p>\u003Cul>\u003Cli>No free version\u003C/li>\u003Cli>Unresponsive technical support\u003C/li>\u003Cli>Confusing user interface (e.g., when setting up an artifact repository)\u003C/li>\u003C/ul>\u003Ch3>9. EasyMorph\u003C/h3>\u003Cp>EasyMorph is a data management platform that allows users to perform complex transformations in marketing data with no code or specialized programming. This is made possible by numerous preset actions and functions that marketers can use straight away. Although developed with the analytics teams in mind, EasyMorph can be a good choice for less technical-savvy users thanks to the code-free integration process. \u003C/p>\u003Cp>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/Easy_Morph_9a79f4712f.png\" alt=\"EasyMorph data transformation tool\">\u003C/p>\u003Cp>\u003Cstrong>Best for\u003C/strong>: Technical and non-technical users who need out-box transformation capabilities. \u003C/p>\u003Cp>\u003Cstrong>Pros\u003C/strong>:\u003C/p>\u003Cul>\u003Cli>Simple to install\u003C/li>\u003Cli>No administration required\u003C/li>\u003Cli>Free version available\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Cons\u003C/strong>:\u003C/p>\u003Cul>\u003Cli>For MS Windows only, no MacOS or Linux\u003C/li>\u003Cli>Documentation is not always up to date with new features\u003C/li>\u003Cli>Not many destinations available \u003C/li>\u003C/ul>\u003Ch3>10. Qlik\u003C/h3>\u003Cp>Qlik is one of the major data transformation software firms offering different data integration, data quality, and analytics solutions that support your AI marketing data strategy. Providing a single platform for active intelligence, Qlik allows marketers to streamline data warehouse tasks, as well as develop, test, deploy, and update anything data-related. \u003C/p>\u003Cp>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/Qlik_a88229c257.png\" alt=\"Qlik data transformation tool\">\u003C/p>\u003Cp>\u003Cstrong>Best for\u003C/strong>: Data engineers and data analysts looking to accelerate marketing data analytics tasks by data warehousing, data management, and ETL processes. \u003C/p>\u003Cp>\u003Cstrong>Pros\u003C/strong>: \u003C/p>\u003Cul>\u003Cli>No-code ETL automation\u003C/li>\u003Cli>User-centered interface\u003C/li>\u003Cli>Effortless scalable replications\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Cons\u003C/strong>:\u003C/p>\u003Cul>\u003Cli>Occasional data latency issues\u003C/li>\u003Cli>Works best with other Qlik products\u003C/li>\u003Cli>Needs more documentation \u003C/li>\u003C/ul>\u003Ch3>11. Informatica\u003C/h3>\u003Cp>Informatica Data Management Cloud is a perfect solution for extracting, transforming, and loading your marketing data into a data warehouse. It offers pre-built connectors and actions between applications and programs, as well as use-case-specific service. You can transform any type of data format and size into usable data. Teams can map any transformation they need and then deploy the command to execute it whenever needed without writing code.\u003C/p>\u003Cp>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/Informatica_ab12b74a86.png\" alt=\"Informatica data transformation tool\">\u003C/p>\u003Cp>The platform is available as a PowerCenter, which is an ETL solution for large enterprises, and Cloud Data Integration, which is positioned as an IPaaS (Integration Platform as a Service).\u003C/p>\u003Cp>\u003Cstrong>Best for\u003C/strong>: Enterprises looking to unlock and democratize their data across the organization, turning it into a trusted asset. \u003C/p>\u003Cp>\u003Cstrong>Pros\u003C/strong>:\u003C/p>\u003Cul>\u003Cli>User-interface easy to understand for non-developers\u003C/li>\u003Cli>Pre-built transformations library\u003C/li>\u003Cli>Seamless scaling\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Cons\u003C/strong>:\u003C/p>\u003Cul>\u003Cli>No documentation for new features\u003C/li>\u003Cli>Complex to implement a new API\u003C/li>\u003C/ul>\u003Ch3>12. IBM InfoSphere DataStage\u003C/h3>\u003Cp>IBM InfoSphere DataStage is a cloud-ready data integration platform that can clean, modify, and transform different types of data marketers use to optimize their campaigns. Built on containers and microservices, DataStage provides easy real-time analytics and features like built-in search, automatic metadata propagation, and simultaneous highlighting of compilation errors. \u003C/p>\u003Cp>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/Info_Sphere_30e468b0b4.png\" alt=\"InfoSphere DataStage data transformation tool\">\u003C/p>\u003Cp>\u003Cstrong>Best for\u003C/strong>: Marketers dealing with various data sources, formats, and structures that need scalable solutions for aggregation and unified view. \u003C/p>\u003Cp>\u003Cstrong>Pros\u003C/strong>:\u003C/p>\u003Cul>\u003Cli>Reporting function \u003C/li>\u003Cli>Excellent mapping tools\u003C/li>\u003Cli>Data collaboration\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Cons\u003C/strong>:\u003C/p>\u003Cul>\u003Cli>Tools not easy to manipulate through Cloud services\u003C/li>\u003Cli>Hierarchical stages to build XMLs and JSONs need work\u003C/li>\u003C/ul>\u003Ch3>13. Datameer\u003C/h3>\u003Cp>Datameer is a SaaS data transformation solution designed explicitly for Snowflake Cloud. It comes with end-to-end data lifecycle management able to discover data, transform, deploy and document marketing data operations. The tool is ideal for both non-tech-savvy and SQL-savvy teams, as it allows you to manipulate datasets using SQL, No-code, or both. \u003C/p>\u003Cp>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/Datameer_da117aa38e.png\" alt=\"Datameer data transformation tool\">\u003C/p>\u003Cp>\u003Cstrong>Best for\u003C/strong>: Building use-case-driven pipelines to ETL on-premise marketing data to a cloud data warehouse without need to hire a data ops expert. \u003C/p>\u003Cp>\u003Cstrong>Pros\u003C/strong>:\u003C/p>\u003Cul>\u003Cli>Built-in data catalog for quick access to metadata and documentation \u003C/li>\u003Cli>Advanced version control and pipeline monitoring \u003C/li>\u003Cli>Complete data lineage and audit trails\u003C/li>\u003Cli>Efficient data governance and minimal duplication thanks to Snowflake integration\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Cons\u003C/strong>:\u003C/p>\u003Cul>\u003Cli>Could benefit from more condensed designs\u003C/li>\u003Cli>Multiple tabs tend to dilute focus \u003C/li>\u003Cli>Tutorial videos too long in most cases\u003C/li>\u003C/ul>\u003Ch3>14. Dbt Labs\u003C/h3>\u003Cp>Dbt is a command-line data transformation software aimed at SQL and Python-savvy data analysts and engineers to transform data in their warehouses more effectively. You can use SQL code to build and manage data models, test data quality, and document work. Thanks to the auto-generated dependency graphs, you can track data flow through the pipeline and resolve each step in the correct order.\u003C/p>\u003Cp>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/dbt_d84b012e17.png\" alt=\"Dbt data transformation tool\">\u003C/p>\u003Cp>\u003Cstrong>Best for\u003C/strong>: More technical analysts and BI teams already using SQL programming to transform their marketing data. \u003C/p>\u003Cp>\u003Cstrong>Pros\u003C/strong>:\u003C/p>\u003Cul>\u003Cli>Easy-to-deploy tests\u003C/li>\u003Cli>Data lineage and documentation \u003C/li>\u003Cli>Libraries with helpful code packages\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Cons\u003C/strong>:\u003C/p>\u003Cul>\u003Cli>Too complex for non-tech-savvy digital marketers\u003C/li>\u003Cli>Documentation inheritance issues \u003C/li>\u003C/ul>\u003Ch3>15. Nexla\u003C/h3>\u003Cp>Nexla is a data transformation tool that simplifies marketing data preparation thanks to its no-code interface. It helps non-tech users run data transformation with its extensive library of transformation operations. Using automated versioning and logging, Nexla helps you understand how data sets have changed and who made those changes. Features like schema validation, data type validation, and data profiling simplify data compliance and improve the reliability of transformed data. \u003C/p>\u003Cp>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/Nexla_cf3558ba9f.png\" alt=\"Nexla data transformation tool\">\u003C/p>\u003Cp>\u003Cstrong>Best for\u003C/strong>: Non-technical marketing teams looking for a robust solution for data operations, data flow management, and data analytics. \u003C/p>\u003Cp>\u003Cstrong>Pros\u003C/strong>:\u003C/p>\u003Cul>\u003Cli>Powerful no-code platform\u003C/li>\u003Cli>Extensive failed pipeline recovery mechanism\u003C/li>\u003Cli>Responsive support team\u003C/li>\u003Cli>Easy to transform large data sets\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Cons\u003C/strong>:\u003C/p>\u003Cul>\u003Cli>UI/UX could be better\u003C/li>\u003Cli>Site loading time varies between devices\u003C/li>\u003C/ul>\u003Ch2>Benefits of using one marketing data platform vs. data stack\u003C/h2>\u003Cp>You might be tempted to spring for a modern data stack with various specialized tools. However, these often come with a complex web of dependencies and a demand for technical expertise. For marketing agencies and smaller businesses, this can become a barrier that prevents them from scaling. \u003C/p>\u003Cp>The integration challenges that follow a modern data stack can slow the workflow, delaying sourcing actionable insights. \u003C/p>\u003Cp>With Whatagraph, you don’t have such problems. The whole data journey takes place within one platform that has been optimized for maximum speed and user experience. Whatagraph can replace an entire marketing data stack with one reliable platform. \u003C/p>\u003Ch3>Anyone in a marketing team can execute advanced data management\u003C/h3>\u003Cp>With Whatagraph, there’s nothing to manage, integrate, or maintain. Your data flows in automatically and directly from your marketing sources into a single hub via \u003Ca href=\"https://whatagraph.com/integrations\">fully-managed integrations\u003C/a>, Custom API, Google Sheets, or BigQuery. \u003C/p>\u003Cp>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/connect_features_274c1253d7.png\" alt=\"Connect marketing sources easily\">\u003C/p>\u003Cp>Unlike other complex tools, you can easily blend, unify, and group data with transformations instantly available for visualization. \u003C/p>\u003Ch3>All-in-one intuitive data platform \u003C/h3>\u003Cp>Once you connect your marketing data sources to Whatagraph, every data point is in one easy-to-navigate solution. You are free to \u003Ca href=\"https://whatagraph.com/organize\">seamlessly transform unstructured data\u003C/a>. Get to analysis- or presentation-ready reports and dashboards faster.\u003C/p>\u003Cp>Whatagraph is a marketing data platform that grows with you. There’s no restriction on adding any number of new clients or campaigns. No limits on the length and depth of displayed data and no limits to fast and reliable experience. \u003C/p>\u003Cp>Most importantly, no matter how many clients or data sources you have, you can rest assured your marketing data is accurate, reliable, and automatically updated regularly. \u003C/p>\u003Ch3>Save hours in the process \u003C/h3>\u003Cp>Whatagraph reduces the repetitive manual work by introducing report and dashboard templates out of the box. Just connect your sources, and your presentation is ready. You can save any report, widget, or calculation you create as a template for future use. This includes any blend or transformation, as well. \u003C/p>\u003Cp>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/save_time_374eec0e53.png\" alt=\"Save time with marketing reporting\">\u003C/p>\u003Cp>Link multiple reports to a template and edit them in bulk instead of changing them one by one. Save time on report distribution by scheduling emails with links or attachments or sharing live links for on-demand access. \u003C/p>\u003Ch3>Visualize instantly\u003C/h3>\u003Cp>Create \u003Ca href=\"https://whatagraph.com/visualize\">an engaging visual story\u003C/a> so stakeholders can understand in the same platform where you connect and organize your marketing data. Create a visual report from scratch using the intuitive drag-and-drop builder, a library of pre-made widgets, including the goal widget, media, funnel, multi-source tables, different charts, etc. Build your own widgets, applying custom formulas and filters.  \u003C/p>\u003Ch3>No hidden costs\u003C/h3>\u003Cp>Using several specialized tools can seem like a cost-effective solution. However, it can easily spiral into a budgetary nightmare. The fragmented nature of a modern data stack often requires additional expenses in training, integration, and ongoing maintenance. Such hidden costs can encumber your budget and work against the very agility that the stack promises to provide. \u003C/p>\u003Cp>Not to mention that the cost of licensing several specialized tools can easily escalate, straining the resources of smaller agencies looking for sustainable, data-driven excellence. \u003C/p>\u003Cp>When it comes to Whatagraph pricing, everything is transparent. You choose a plan and pay based on the number of source credits - for connecting new sources, executing blends, or setting up a data transfer to BigQuery.\u003C/p>\u003Ch2>Wrapping up\u003C/h2>\u003Cp>When it comes to organizing unstructured marketing data, there are many highly capable data transformation tools to choose from. However, capability doesn’t always go hand in hand with speed, convenience, and user experience. \u003C/p>\u003Cp>If you have a data engineer or data scientist onboard, there’s no question they’ll enjoy using tools like RudderStack, Alteryx, or Dbt command prompts to build complex transformations in code. \u003C/p>\u003Cp>But what about marketing agencies and businesses that don’t necessarily have a tech-savvy team? \u003C/p>\u003Cp>Whatagraph’s one-platform concept allows you to transform marketing data without any code or breaking a sweat in the same environment where you connect the sources and create stunning visualizations. \u003C/p>\u003Cp>\u003Ca href=\"https://whatagraph.com/book-a-call\">Book a demo\u003C/a> today and see Whatagraph in action. \u003C/p>","2024-01-28T20:38:41.668Z","2025-02-02T21:33:34.913Z","2024-01-28T21:18:22.465Z",{"id":562,"name":563,"alternativeText":564,"caption":31,"width":427,"height":428,"formats":565,"hash":571,"ext":292,"mime":293,"size":572,"url":573,"previewUrl":31,"provider":296,"provider_metadata":31,"createdAt":574,"updatedAt":575},11831,"dt_tools.png","Top 15 Data Transformation Tools for Marketers",{"thumbnail":566},{"ext":292,"url":567,"hash":568,"mime":293,"name":569,"path":31,"size":570,"width":435,"height":436},"https://s3.us-east-2.amazonaws.com/whatagraph.com/thumbnail_dt_tools_604f90e0ce.png","thumbnail_dt_tools_604f90e0ce","thumbnail_dt_tools.png",11.82,"dt_tools_604f90e0ce",64.21,"https://s3.us-east-2.amazonaws.com/whatagraph.com/dt_tools_604f90e0ce.png","2024-01-28T20:46:06.809Z","2024-01-28T20:59:00.557Z",{"id":461,"name":462,"about":463,"email":464,"createdAt":465,"updatedAt":466,"publishedAt":467,"slug":468,"linkedin_url":469},{"id":5,"title":443,"slug":444,"subheading":445,"createdAt":446,"updatedAt":447,"publishedAt":448},{"id":579,"dateReorder":580,"title":581,"slug":582,"summary":583,"body":584,"read_time":34,"createdAt":585,"updatedAt":586,"publishedAt":587,"errors":31,"table_of_contents":15,"cover_image":588,"author":602,"article_category":603},2277,"2023-04-13","Whatagraph Introduces Easy-To-Use Data Transfer Functionality to Get Your Marketing Data to Google Bigquery. No Coding Required.","data-transfer-release","\u003Cp>Whatagraph is trusted by thousands of marketing professionals around the world for connecting, visualizing, and sharing marketing data. As a response to the growing need for marketers to have more control over their data, we are happy to introduce a new solution that moves data to a BigQuery data warehouse in a few simple steps. \u003C/p>","\u003Cp>With marketing advertising and analytics platforms’ APIs becoming increasingly unreliable due to changing rules, limitations, and platforms going away for good, data warehouses are emerging as a more stable and secure solution for storing valuable marketing data.\u003C/p>\u003Cp>BigQuery is a cloud-based data warehouse, fully managed by Google Cloud Platform, so no additional maintenance is needed. Unlike on-premises data warehouses, BigQuery is scalable to petabytes of data with no loss in speed.\u003C/p>\u003Cp>But transferring data to a warehouse can be challenging, as it often requires coding skills or data experts’ help. Many data transfer tools on the market are complicated to use and their pricing could be confusing or quote-based.\u003C/p>\u003Cp>Whatagraph simplifies this task with a user-friendly interface that connects your sources and destination, while automating the data flow. The platform comes with a transparent and easy-to-understand pricing model. You pay a fixed amount per each transfer, that is every data flow you set up between a marketing source and the warehouse.\u003C/p>\u003Cp>\u003Cimg src=\"https://s3.us-east-2.amazonaws.com/whatagraph.com/image1_37db9e9e43.png\" alt=\"Data transfer in Whatagraph\">Tailored for marketing professionals, Whatagraph is an all-in-one platform that includes valuable features such as seamless data visualization directly from the warehouse, cross-channel reports and dashboards, customization, sharing automation, on-demand live dashboards, and many more.\u003C/p>\u003Cp>If you're looking to gain more control over your marketing data, \u003Ca href=\"https://whatagraph.com/data-transfer/google-bigquery/\">request a free trial of Whatagraph's new data transfer solution\u003C/a>. \u003C/p>","2023-04-13T07:27:42.758Z","2023-12-19T19:16:28.553Z","2023-04-13T07:51:34.770Z",{"id":589,"name":590,"alternativeText":582,"caption":31,"width":427,"height":428,"formats":591,"hash":597,"ext":292,"mime":293,"size":598,"url":599,"previewUrl":31,"provider":296,"provider_metadata":31,"createdAt":600,"updatedAt":601},10540,"datatran.png",{"thumbnail":592},{"ext":292,"url":593,"hash":594,"mime":293,"name":595,"path":31,"size":596,"width":435,"height":436},"https://s3.us-east-2.amazonaws.com/whatagraph.com/thumbnail_datatran_8e30ed68fe.png","thumbnail_datatran_8e30ed68fe","thumbnail_datatran.png",13.39,"datatran_8e30ed68fe",62.77,"https://s3.us-east-2.amazonaws.com/whatagraph.com/datatran_8e30ed68fe.png","2023-04-13T07:22:51.538Z","2023-04-13T07:23:21.389Z",{"id":461,"name":462,"about":463,"email":464,"createdAt":465,"updatedAt":466,"publishedAt":467,"slug":468,"linkedin_url":469},{"id":270,"title":604,"slug":605,"subheading":606,"createdAt":607,"updatedAt":608,"publishedAt":609},"Product news","product-news","The latest news and product updates","2023-05-16T15:42:50.435Z","2025-06-04T14:38:03.945Z","2023-05-18T13:29:38.364Z",{"id":5,"title":344,"subtitle":611,"createdAt":612,"updatedAt":613,"publishedAt":614,"text_banner":31,"bottom_banner":31},"\u003Cp>Dive into Whatagraph product updates, how-tos, company news, product reviews, and educational content to get the most out of your marketing data.\u003C/p>","2022-11-18T08:43:50.870Z","2025-07-03T08:13:56.278Z","2022-11-18T08:43:52.413Z",{"data":616,"meta":623},{"id":5,"title":443,"slug":444,"subheading":445,"createdAt":446,"updatedAt":447,"publishedAt":448,"toc_banner":617},{"id":319,"content":618,"cta":619,"cta_collection":31},"Learn how to implement AI successfully at your agency",{"id":620,"label":621,"url":367,"subtext":31,"style":622,"element_id":31,"color":31,"icon":31,"width":31},1609,"Get free playbook","button",{},1759916842118]