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Why Now Is a Good Time to Adjust Your Analytics Workflow

7 minute read
Pierre DeBois avatar
With the impending transition to GA4, marketers must adjust for martech now.

When I was a young boy, I looked forward to autumn to see all the new changes being introduced, ranging from the new car models (because I was a car enthusiast) to the new TV shows.

I think fall is also the best time for businesses to see all the new changes in marketing media and analytics, adjusting analytics workflow to accommodate new solutions.

For many marketers, however, 2022’s “Fall TV Lineup” of analytics planning can feel like an extended season of surreal reality shows. One important hit show — the full migration from Google's Universal Analytics to GA4 — has become top priority for digital marketers and analysts in every industry, creating high anxiety that will likely last until the forthcoming July 23 deadline. 

If you rank yourself among those marketers, there is a silver lining to the GA4 transition cloud: An examining period of techniques against platform updates that requires time and adjustments to your overall measurement stack. It is your opportunity to rethink your entire digital analytics data collection. It also comes at a time when businesses struggle to manage their overall martech stack.

Why Marketers Must Adjust With Martech in Mind Now

Widespread marketer attention on the GA4 changeover has overshadowed current martech environment trends. Martech sprawl, the widespread use of a variety of software marketers use to manage their marketing campaigns, has been on the rise. As a result, marketers are overwhelmed in making strategic decisions on what tools they have, especially with content.

Making a good martech solution choice now replies upon a variety of dependencies to support a stack. Those dependencies range from Application Protocol Interfaces (APIs), a set of programming syntax that controls what data is shared among solutions, to distinct purpose software. The more variation of dependency sources within a MarTech ecosystem means accounting for more training steps in an analytics training plan. This means every vendor of those dependencies influences the volume of tasks involved to maintain a MarTech ecosystem. This can challenge a wide number of teams, including the IT department, which is usually charged with managing APIs and addressing the cascade of usages from those APIs. 

Another aspect is the growing attention to data privacy. Marketers are prioritizing their organization’s understanding of how data is construed as personal data within their analytics solutions. Decentralization of data sourcing and storage has introduced permission and privacy risks with data. The variety of territorial regulations, especially with the five new US state regulations (Virginia, California, Colorado, Utah and Connecticut) has introduced compliance complexity.

Marketers must now consider how data lineage management within an organization reveals those risks while crafting customer experiences. Data lineage is simply an overview of where data begins, what happens to the data over time, where it moves over time and where it ends up. The organizational choices to support data lineage impacts compliance and the level of responsibility, be it as a data controller, a data processor or as a dual controller/processor role.

Related Article: Data Control Support Makes Dashboard Workflow in Google Data Studio Better

Does Data Collection Highlight Dataflow?

So as a marketer, where should you best start examining adjustments? 

A key question regarding all these considerations is “How proficient is the data collection with respect to highlighting dataflow?” These days dataflow represents workflow, so your dataflow answers should highlight ideas for you to best address any workflow and martech shortcomings.

One aspect marketers can inspect is their capacity for historical data. Some managers switching to GA4 became concerned that their historical data was at risk because of the July 23 deadline. More realistically, marketers should first determine what they need to learn from historical data. What operational history is of note during the period? How far back should the history go? Six months? A year? Even longer?

Related Article: How to Get Attribution in Analytics Right 

Refresh Aspects of Measurement, Address Technical Debt

When considering technology lifecycles, proactive marketers always expect to refresh aspects of a measurement plan over time. Thus, your historical data becomes useful as a benchmark of past performance. You can do a year-over-year comparison, but you will likely find only a handful of metrics will make sense, not the entire historical data. Selecting a limited historical dataset can help budget resources and direct your team focus to what is valuable.

Learning Opportunities

Conducting measurement plan reviews naturally introduces an opportunity to better understand what people want from their dashboards. Dashboards help people understand activity within a business unit, but it is not meant to be the final product. People want an outcome from a model or a report — ideas and observations that identify the risk in pursuing an action that was meant for either more revenue or lower costs. So, you must find the reason behind the outcome from the data.

The discussion on dashboards leads into another aspect, identifying the time needed to address a technical debt in a martech stack. You should outline what is required to learn new dashboard and data collection technologies. You can use blameless retrospectives to help encourage your team to understand what issues exist, what can be corrected and what corrections are possible.

Related Article: Hit an Analytic Bottleneck? Here's How to Get Unstuck 

Identify Data Lineage

Finally, teams should visualize the work needed to identify data lineage. Data lineage is a map of a data journey, identifying the origin of data, each step and explanation of how and why the data moved. Tools for advanced analytics have a variety of connections between dashboards and data sources. Many are simple like the connectors in Looker Data Studio. 

Other introduced concerns are centered on APIs. APIs act as doorways that permit the right data to pass between data sources and among dashboards. APIs are also essential to data modeling — many models created on R and Python rely on APIs to import data. So, if you have APIs, you should take time to map the ecosystem of data allowed among systems, for dashboard-supporting tasks such as data streaming, batching and authentication maintenance. You can look at support documentation and put together a short specification sheet to know what is in your measurement ecosystem. You can then decide how you want it to impact your marketing team and strategy plans. 

Managing data lineage is also the path to meeting privacy milestones, outlining what is at risk when data is processed and what tasks are required to avoid benign data from becoming sensitive data. The pressure to update analytics life cycles with respect to privacy requires recognizing where confusion exists in some processes related to data collector or processor roles that a company takes. Mapping data lineage can assist teams in refining their data collection practices and establish a fundamental ability to respond to data subject requests concerning the collected data. Start with what you know.

Don’t Be Tempted to Recreate the Old With the New

There can be a temptation to just recreate the old implementation with a new analytics platform — like using Universal Analytics on GA4. But this is not a realistic option with most analytics solutions. With Google Analytics, GA4 has many new reporting features, creating nuanced yet significant differences in workflow decisions to implement and measure online data.

The real options are adjusting the new features revealed in the workflow. You can encounter bottlenecks in advancing productivity with analytics — I explain how to get past those issues in this post. If you find your team or organization in those bottlenecks, do not lose sight of acting on feedback regarding technical or workplace data issues. It can be easy to do, especially during holiday marketing campaigns. Advertising budget cuts, tech layoffs and platform disruptions like the Twitter acquisition can overshadow any sentiment toward this year’s campaigns.

Final Word on Adjusting Your Analytics Workflow

Yet, there is a silver lining. Marketplace uncertainty reveals an opportunity to consider the entire analytic cycle. Use the time to audit the current capabilities of your marketing tools the right way against any new ones you are considering.

About the author

Pierre DeBois

Pierre DeBois is the founder of Zimana, a small business digital analytics consultancy. He reviews data from web analytics and social media dashboard solutions, then provides recommendations and web development action that improves marketing strategy and business profitability.

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