With Universal Analytics (UA) in its final year of collecting data, companies that haven’t already done so should begin to develop Google Analytics 4 (GA4) in parallel with existing systems to carry over event configurations as well as preserve any year-over-year views.
Google Data Studio (GDS) can be used to create analytics and reports and help harness the power of GA4, the new analytics property type from the search giant.
GA4 offers more privacy protection using data-driven attribution to analyze the impact of marketing efforts across the entire customer journey.
Brendan Pescatore, integrated marketing analytics manager at Matter Communications, explained Google Data Studio can provide powerful side-by-side visual comparisons of these data sources to gauge the level of variance between analytics models and set expectations for the future state.
"Data Studio is highly advantageous when establishing data streams within the G Suite, for example when connecting Analytics or BigQuery," he said.
One common path is to pipe website data directly into GDS via the native connector, offering at-scale customization for unlimited Google Analytics views.
Alternatively, connecting GDS to the data warehouse and leveraging GA4’s out-of-the box integration with BigQuery allows for storage, transformation and faster dashboard performance, which Pescatore points out is also handy when accessing data older than 14 months.
Using Data Studio to Simplify the Mining Process
Mikael Ekelund, senior commerce adviser at Avensia, explained the reporting UI of GA4 is still in its early stages and for nontechnical users and those not familiar with the GA4 interface, it is still difficult to get the data that is important for your business.
"Because of this, it makes sense to look at external tools and Data Studio can simplify the process of getting the data needed for reporting by allowing you to build custom reports and dashboards," he said.
He noted Google Data Studio is an excellent option if users are looking to take their GA4 reporting to the next level and it will make it easier to find insights.
"With just a little background knowledge, Data Studio offers an intuitive approach for getting data and displaying it in a user-friendly manner," he said. "Data Studio canvas, along with configuration settings, is a much more flexible solution compared to using the Explore module in GA4."
Allan Rogers, lead customer success architect at Tealium, said GDS, combined with GA4 and BigQuery, will allow smaller and medium-sized companies to quickly combine the most voluminous data source (web analytics data) with other data sources and allow data scientists to query the data.
"Of course, Google heavily supports the purchasing of ads through their platform as the main activation path," he added.
From his perspective, being able to bring together disparate, high-volume data sources into one visualization tool could enable great insights for customers who don't want to invest in their own data lakes and expensive visualization tools.
Related Article: Google Analytics 4 and Making the Most of the Customer Lifecycle
Analytics Pros Should Partner With Other Stakeholders
Pescatore said analytics professionals would be wise to partner with and assess the measurement needs of their internal/external stakeholders.
“Fostering collaboration allows digital teams to identify solutions and leverage the tools available that provide the most value to my team and clients,” he said. "However, it is up to the analytics teams to set the tone for how stakeholders should work together and to develop the end goal."
Rogers noted the chief information officer team and data scientists will also be responsible for leveraging Data Studio, pointing out a “deep understanding” of how data is defined and how it relates to other data is required for meaningful insights.
“Since there is no universal schema or definition of how users/visitors/visits are defined, it can be quite complex and some organizations will come to flawed conclusions due to data disparity,” he explained.
Pescatore said using a staging environment, such as a data warehouse that stores or a third-party connection that caches GA4 data, will result in a faster, more stable dashboard performance in Google Data Studio.
Ekelund added that in Google Data Studio users can include both GA4 as well as Universal Analytics data.
“Use this possibility to learn how GA4 metrics and dimensions differ from UA," he advised. "This also makes it easier to plan your dashboard transition.”
Related Article: The Switch to Google Analytics 4 Is Fast Approaching: Here's What to Do
GA4 Familiarization Presents Challenges
From Pescatore's perspective, the highest hurdle for most businesses in adopting GA4 will be adapting to the new model and becoming familiarized with adjustments Google has made to prepare for the “cookie-less” future.
“As Google forces everyone’s hand by moving from hit-based events to user-focused conversion metrics in GA4, marketers leveraging Data Studio for Google Analytics should educate their businesses on the differences in data availability when migrating data sources,” he explained.
Ekelund points out that because GA4 properties are very different from the earlier Universal Analytics properties, the GA4 Data Studio connector is not a simple copy.
“Google is actively working to introduce new features all the time, but there are still some important limitations you should know about, such as currently not being able to apply segments to your GA4 data,” he added.
Focus on Well-Designed Data Flows
Rogers said making sure the data flowing into GA4 is thoughtfully defined is key to success when bringing data into Data Studio.
He explained many organizations are doing a lift and shift from UA to GA4, which is limiting.
"Data strategy and definition at the tag level is more important than ever to make sure that GIGO [garbage in, garbage out] doesn't occur in Data Studio, which could lead to incorrect conclusions," he said.
He added creating accurate insights is more difficult with fewer default metrics.
"A strong data dictionary for both online and offline data needs to be prioritized to make sure that, as data is mashed together, it is able to be understood and actioned on properly," Rogers said. "Consent around data usage could also complicate matters heavily."