3 Reasons Your Google Analytics 360 Reporting UI Is Undermining You
One of the main attractions to Google Analytics is that it is approachable and accessible. This is especially true for the reporting interface. The interface, while expansive, is generally easy for most users to consume and comprehend.
However, when moving beyond standard Google Analytics 360 reports, things can get more complicated. This is especially true for teams that are undertaking more complex analysis of the underlying Google Analytics 360 data outside the reporting UI with tools like Tableau, PowerBI, Periscope Data and Mode Analytics.
Why The Numbers Are Wrong
Here is a typical situation that arises when teams get more sophisticated with data analysis:
- Your analytics team (data analysts and scientists) are using self-service tools like Tableau, PowerBI, Qlik… you name them. What’s powering those tools is the raw Google Analytics 360 data. It’s candy to this team! The data analysts and scientists are poking, probing and exploring the data, coming up with their visualizations, reports, insights and recommendations. They are excited to have the freedom to analyze with their preferred tools. The ability to push the boundaries beyond standard Google reports and come up with compelling insights and reports is the work they love to undertake!
- While the analytics team is working with their lovely tools, executives being in a rush or too impatient to wait for their team, log into Google Analytics 360 reporting UI and notice some of the numbers are “off” from what the analytics team had shared with them. “Why is the Google reporting UI different from what I see in Tableau?” they wonder. “My analysts’ numbers are wrong!”. Trusting Google more than his team, the executive wonders what they are doing wrong!?
- The analytics team, experts with their tools and the data, are puzzled (and frustrated!). They are using the same data that powers the Google 360 reporting UI, so what can be wrong? “We have double checked our queries, the analysis and the data, everything checks out!” they exclaim. “We are losing credibility and we can’t figure out why. Why is the reporting UI showing something so different from the data we are using?”
Not a pleasant situation for the executive or the analytics team! After digging a little further, the analysts found the problem.
What is the cause of the disconnect? The culprit is often custom reports!
Why Custom Reports in Google Analytics 360 Can Become A Problem
The Google Analytic reporting UI does a great job and almost always aligns with the underlying data for standard reports. However, this is not true for custom reports. Why? There are a few reasons:
- Reason 1: Poor setup of custom reports. Google provides tremendous flexibility and freedom for creating those reports. However, creating and publishing custom reports does not mean they are valid or accurate. They are custom and they can give a false sense of security that they are setup correctly like the standard reports. Just because Google allows you to create them, this does not mean they are accurate! Understanding how to create those reports properly requires training and skill.
- Reason 2: No testing or validation of the resulting custom report output. The flexibility and freedom to create custom reports can be utilized by those who are not able (or willing) to test the resulting outcome. They might be mixing and matching dimensions that are not compatible with each other. The more complex the GA tagging is, the greater the risk. This is due to the intrinsic complexity of validating the results for complex event models. People often trust that the outputs of a custom report, regardless of who created it, are accurate.
- Reason 3: No ongoing management or ownership of custom reports. Neither custom reports, nor the data underlying them, are static. Ownership needs to be assigned to an individual or a team to assess, test, validate and publish reports. They need to ensure that quality and consistency are preserved, rooting out any unverifiable reports/data points from consumption by the executive/business team. They also need to flag potential problems to upstream teams who may be responsible for tagging the site.
Mike Sullivan, Founder at Analytics Edge, wrote an article about the challenges of using custom reports.
As Mike states, “People run into problems when they start including other dimensions (we all have unique websites with unique reporting needs). Not all dimensions work together, and Google won’t stop you from mixing incompatible ones.”
Mike recommends that people start with minimal reports in the reporting UI. This means focusing on the standard event dimensions and metrics. He also suggests incrementally adding new reports only after testing the outputs to verify accuracy. Good advice!
Summary
Our suggestion is for teams to make a choice on which system is authoritative for certain types of information. If you have a team that employs tools like Microsoft PowerBI or Tableau use them rather than GA custom reports! This ensures consistency and confidence in the analysis. It also avoids unnecessary testing, validation and ongoing oversight for custom reports in Google Analytics 360. The team has already performed those tasks with Tableau or PowerBI so there is no need to undertake the same work again in Google.
This approach will ensure that the Google Analytics 360 reporting UI is not presenting problematic or incorrect information. It will also reduce conflicts with those teams who are employing different analytics tools to crunch the data.
Did we miss anything? If you have any questions about Google, BigQuery, or data in general, feel free to leave a comment or contact us at support@openbridge.com. You can also visit us at https://www.openbridge.com to learn how we are helping other companies with their data efforts.
