I love things to grow

When I started creating analytics setups, they were usually Google Analytics, Amplitude, or Mixpanel projects. They worked well and could give us some insights into some aspects of the application or shop.

Small insights are better than nothing was the theme of the days then.

The missing pieces finally drove me to the data warehouses. I was working with some ecommerce teams and they needed to get insights beyond what analytics platforms like Google Analytics could offer. They wanted to blend analytics data, with backend and ad platform data to create campaign reports that gave them more details about the profitability of campaigns and how customer cohorts were developing.

So we brought it all together in data warehouses, added classic dimensional data models and created plenty of reporting dashboards.

It solved one problem, but many kept difficult.

One problem was the static data model. It was good to answer obvious questions about sales and marketing campaigns but become complex to handle when we want to include more touchpoints and analyse more details about the natural complex user journeys (because a buying funnel is just a last step of a longer journey).

And it all fell apart when I was trying to solve product analytics cases with subscription data. 

So, I started to dream about a setup where I could combine the power of event analytics that product analytics made possible, with the extended data sets that the data warehouse could provide.

It took me some years to collect all the puzzle pieces I needed for it. Part one was to find a data model that works specialized for event data. I found this in activity schema.

Next part were the first tools released that could do event analyics on top of data warehouse data. Luckily 2 years ago the first startups appeared and they pushed the legacy tools like Amplitude or Mixpanel to follow.

The final part was building and developing to bring it all together. The result is now an analytics stack, that I always wanted. One that brings marketing, product and subscription data together to allow the construction and analyzis of unlimited customer segments to find the ones that help businesses to scale.

I came from university with a background in economics and software development. I could not decide between them. 

Luckily, I found product management, which sits perfectly in between both disciplines. New love was found.

Not long after that, I discovered analytics. I wanted to move faster and was scared of driving blind, so I started to experiment with Google Analytics (the classic edition). This gave me more confidence in the backlog decisions and surprising and sometimes heartbreaking feedback (no one is actually using it).

Data just pulled me in 100%, and at some point, I took the jump to work 100% on data projects.

I wanted to see the data world beyond product analytics, so I spent time with app analytics, marketing analytics and, at some point, ended in the fascinating world of data warehouse. Here I designed architectures, created data models and these custom python data pipelines almost every setup needs.

After a long journey in data engineering, I missed the analytics space. Building great setups is one thing, and having this skill is important. However, the real value only comes when you combine it with the business and the product. To build really great customer and product experiences.