I always counted myself lucky to have had the opportunity of starting my career in data & analytics. In short, my years as a data analyst, consultant and senior consultant can be summarized as follows: I developed hundreds of reports, helped implement and carefully document a number of state-of-the-art data warehouses and supported a number of companies in rolling out self-service reporting, training and coaching business users to build their own reports based on the available data.

During these various assignments, I saw the value of data. I witnessed organizations relying more and more on data to make strategic decisions. I shared joy with a financial controller who went from having to spend 4 hours every Monday morning wrestling with Excel to prepare his weekly sales report, to doing the same with a refresh button in under 3 minutes. And I was amazed by some of the self-service dashboards, built by users I coached.

However, I also saw the struggle. When I looked at the usage of all those reports I developed, it turned out only a fraction of them had been opened in the previous months. Also, nobody ever read the documentation I spent so much time on. And I saw those self-service analytics rollouts lead to confusion, lots of duplicate reports and quite a few incorrect interpretations of the data.

So what was to be done? We had great capabilities to work with data, but how could we make sure business users knew which reports existed and how to easily access them? How could we make sure the data we unlocked was understood and correctly interpreted, not just by the experts, but by all users throughout the organization?

It turns out this is a challenge that metadata catalogs aim to solve. So, with a number of expert colleagues, we assessed the tools available on the market, in order to identify the right one for our clients. Our search however did not lead us to the right solution. Each option we reviewed, suffered from one of these elements:

· They were too technical, focusing mainly on crawling and labeling technical metadata, interesting for IT experts but not for less technical data users.

· They were too complex, including a steep learning curve and simply adding another data tool to the pile users already needed to be able to work with.

With the support of 2 customers who had joined us in our search for the right solution, we decided to start developing our own product, building further on a comprehensive KPI framework I had started to work on with a few colleagues in 2017 (KPIs have always been a hobbyhorse of mine). Three principles guided us in our efforts.

The result of this work is dScribe's Collaborative Data Compass, the most intuitive and easy-to-use data discovery and knowledge solution on the market.

Today, I am proud to say that thanks to its early adopters, dScribe has become an official product company, fully focused on making our solution available to a broader audience. At dScribe, we believe the greatest opportunity related to data remains largely untapped: the people working with it. We are here to change this, by making data easily accessible and fully understood.

If this topic resonates with you in any way, please don’t hesitate to send me an email. We have helped several organizations in multiplying the value of data for their users. We’d love to do the same for you.

Until then, keep striving for data clarity, and leave no (data) stone unturned!