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As we're still reconfiguring in the wake of the pandemic, face-to-face events in digital analytics are back on the agenda.
We attended this year's MeasureCamp in Copenhagen, the fifth edition of the (un)conference in Denmark on Saturday, June 11th. Here are the top takeaways from the international data leaders that joined:
With the sunsetting of Universal Analytics in 2023, GA4 has become a dominant topic on the digital analytics agenda.
"Companies are more aware of GA4 because of the sunset date and people are realizing what kind of features they could get out of GA4 and how they can boost their analysis by moving to GA4. So I feel like GA4 has kind of been the biggest highlight of MeasureCamp so far." - Ashit Kumar, Growth Lead at Spotify.
An increased and obvious focus is the implementation of Google Analytics 4 - whether it's a migration from Universal Analytics or another analytics platform that does not have all the capabilities that organizations need.
"Just like most people in analytics, I’m pretty sure that we’re gonna spend this year and the next year reimplementing GA4 analytics for our companies. Because everyone is going to lose Universal Analytics in little over one year, which means that everyone is busy migrating to GA4, Adobe Analytics or some other tool like that." - Mikko Pippo.
At the same time, the migration to a new analytics platform brings up an important opportunity: improving data governance. Leaving legacy systems behind and adopting a new platform makes many organizations rethink their data collection strategy and find ways for improving data collection from the early stages.
“This is a good opportunity for clients out there to actually evaluate what analytics capabilities they require from it and maybe reassess the choices that were made previously", adds Marta Florentyna Saratowicz - Product Manager, Tracking & Martech at Pandora.
With the rising volume and velocity of enterprise data, we are witnessing a shift from the well-established centralized data lake to a data mesh architecture.
Compared to the data lake model where the data is collected, processed and managed from a common warehouse, a data mesh model enables access and ownership of data by single, autonomous teams.
An argument for the data mesh framework is that it makes data easier to consume and distribute, while increasing stakeholder accountability. Stakeholders have complete ownership of data processes, and data distribution is democratized across the organization.
“I think we are moving into an era of breaking silos. So there are many concepts that are speaking for this trend like the data mesh. Where every function in the company would own their own data and provide their own data as a product to other teams. I think this will be an interesting trend that will dominate in the next couple of years.” - Ibrahim Elawadi, Senior Insights Manager at Greenpeace.
Now more than ever, the vast amount of data we are collecting is seen both as an opportunity and a challenge.
Changes in analytics setups, privacy concerns, increasing data volumes are only a few of the elements that factor in when we tackle questions like: What are we tracking? How are collecting data? And, more importantly, what value do we drive from the data we collect?
"Since there are more questions being asked about what data we are collecting, I feel like we are being more aware about why we need tracking. If you're tracking people, you should first and foremost tell them that you're tracking them and what you are planning to do with their data. Secondly, we're also becoming more aware about why we're tracking data and what business value we get out of it." says Ashit Kumar, Growth Lead at Spotify.
“Peter Drucker is often quoted as saying 'You can't manage what you can't measure.' And what we’re noticing now is a new tendency. Because you can measure and you can improve data, it is not necessarily important to do it. With the wrong incentives, it can backfire.” adds Alban Gérôme - Senior Data Analyst at Legal & General.
