Some concepts of branching and version control, long adopted by developers is seeping into the solutions and workflow of marketing professionals. One of those is the latest Google Data Studio feature called data control support.

Data control support acts as a type of version control that lets you select a data set for a report without adjusting the data source through programming syntax. The benefit is that it eases report sharing to users and streamlines accounts by removing the need to create separate reports.

Currently each report connects to a specific data set in Google Data Studio. So, for example when it is connected, Google Analytics will send data from one account to a specific report in Data Studio report. This may be acceptable for simple websites or apps, but it can get complicated as more stakeholders are involved. Channels and campaigns are not always clearly analyzed with teams siloed to those channels and campaigns. 

Google Data Studio's Data Control Acts as a Switch

Data Control acts as a switch allowing you to adjust the data without changing reports. You can have data from two accounts that can be shared in multiple reports rather than a one-account-to-one-account access. The original data set associated with the Data Studio account remains as the default, while anyone viewing the report can use the data control to change to a different data set to which they have access. That can be useful when you want to see different dimensions and metrics for a set visualization, like pie charts for different ad campaigns. Data controls only show the accounts to which users have access: Access is governed by Data Studio source credentials. 

The granularity Data Control offers is being introduced alongside an improved GA4 report integration in Data Studio. In August, Data Studio added subproperties and rollup properties within its GA4 connection. Subproperties are account properties that receive data from a source property. The data may or may not be a subset of the source property. Since the source properties for Data Studio are often Google platforms — Google Ads and Google Search Console, for example — subproperties can provide more flexibility in report creation. Combined with Data Control, each stakeholder can see the right amount of campaign and channel data.

Related Article: Leveraging Google Data Studio as the GA4 Transition Looms

Fostering Innovative Insights on Data Studio Dashboards

The result is allowing data access to foster innovative insights from Data Studio dashboards, but in a manner better suited to the immediate reporting needs of agencies and stakeholder teams. People are more interested in quickly crafting data models or reports that speak to the business risk they are considering. These kind of dashboard features speak to that interest.

Learning Opportunities

Data Control in Google Data Studio is an outcome from an emerging data storage trend being introduced in many analyses, from digital advertising to machine learning. Martech solution providers are realizing that the data that doesn't fit neatly within a database or as a one-on-one connection. Making changes can often involve architectural changes and programming skills that teams do not have. Creating low code data options allows the platforms to aid customers who need to share data from a reliable single source of truth, but not necessarily have it come through a database or with an API syntax to work with. Pins, a new dependency feature introduced to R programming, is built with the same concept in mind.

Related Article: Report Publishing Brings Version Control to Google Data Studio

Benefitting From Low-Code Convenience

Features like Data Control shift workflow task so that analysts can focus on what is being produced by data while benefiting from a low-code convenience in maintaining a source of truth for their data. A source of truth means that the data is arriving from a source that is considered reliable and accurate. Many times, in accessing data users must have a technical verification like an API token, to access trusted sources.

Because API authorization tokens are not accessed — the data source is trusted already — Data Control in Google Data Studio minimizes the need for syntax knowledge. Yet the user maintains a simplified workflow integrity for instances where data access must be controlled — like for privacy, for example. That capacity lets analysts focus on the real task — setting up a dashboard to quickly serve their measurement needs.

The Takeaway: Good Data Studio Meeting Analysts' Demands

All of this supports a direction of making Google Data Studio more of a default dashboard for analysts who manage measurement based on different data sources. These days analysts and stakeholders are demanding dashboards offer more insightful features, giving analysts an opportunity to make better decisions. Data Control in Google Data Studio is among those platforms meeting the demand.