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When we began our journey, it was our VP of Engineering who assessed potential fits to start working with the data that we have. It was all done in between the tasks of our Operations department and our CEO himself to create reports that were crucial for communicating the status-quo within and outside of the company.

We had no dedicated person or department to take care of the volume of data that we had collected and wanted to analyze. With this list in our hands, we started our journey to become data-driven.Īt the beginning of our journey, everything started without a Data Scientist. Noteworthy is that we wanted to automate our reporting process so that at each point in time, one would know exactly our current state. However, if the business grows further, there is a point in time where it gets almost impossible to know everything by fact. I don’t think this is a bad thing to do in general, especially in the early stages of a business. Those data silos can cause action steps to be drawn not based on data but rather on one’s experience and knowledge. The data was also mainly accessible by only one department and isolated from the rest of the company. The biggest pain point for us to resolve was the lack of transparency and the incomplete view of our business performance each month. The inspiration to become data-drivenīefore we started researching our technical setup, we had identified our biggest challenges and made up a clear list of needs that we wanted to cover. This was the turning point to evolve our data communication. We added metrics that would report our Marketing efforts, which included new data sources that needed to be maintained as well.

Suddenly, we had to maintain a lot more data in the spreadsheet to keep the report up-to-date. The complexity grew, even more, when the numbers of business metrics to keep track of were also growing with us. At that time, usual KPIs would include the development of our Annual Recurring Revenue (ARR), Monthly Recurring Revenue (MRR), and other financial key indicators as well. That spreadsheet was then used to inform stakeholders about the success and growth.

#METABASE LINKEDIN MANUAL#

The manual collection process required to report our business performance through Key Performance Indicators (KPIs) involved many hours of manual data curation, filtering, aggregation, and copying and pasting to calculate and visualize them on a spreadsheet. It became almost impossible to maintain every piece of data manually since we hadn’t decided on a proper data infrastructure yet.īack then, we had enough data sources to create meaningful dashboards, but the amount of manual work required to preprocess, aggregate, and visualize the data was not scalable for us. The journey began over a year ago when we started to face difficulties in keeping up with monitoring our business performances due to the growing amount of data.

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