Publisher Malmberg collects a lot of offline and online data. More and more educational institutions are using online licenses in addition to (or instead of) printed teaching materials. To properly make use of this, Malmberg uses monthly reports. The in-house data team compiles these as input for specific departments. Malmberg asked us to strengthen this team and make the internal processes around data more efficient.
During conversations with the internal stakeholders, we discovered all kinds of data and analysis needs that existed within the organisation. With this information we were able to quickly start improving internal processes and the expansion of the current analysis spectrum.
We quickly discovered that the data team was spending a lot of time on manual actions. External reports were manually linked and uploaded to the CRM. In addition, all data was collected, analysed and reported using Excel. We mapped out the current processes and automated them where possible.
For this, we wrote a Python script which largely makes manual data manipulations superfluous. Using this, Malmberg's internal data system can automatically read in various offline and online data streams. We built a PowerBI dashboard based on the results of the script. This dashboard shows all Key Performance Indicators (KPIs) of the various departments within the organisation.
With the PowerBI dashboard, all teams within Malmberg have access to relevant information. They have more insight into what other teams are doing and how they are performing on their KPIs regarding their goals. The various departments now know from each other what analysis issues and information needs there are. This has greatly improved internal cooperation.
Because many manual processes are now automated, Malmberg's data team saves a lot of time. This time can then be spent digging deeper into the data to answer even more analytics needs. The monthly report that was previously made with Excel, is now refreshed with the click of a button using the Python script and Power BI. As a result, almost immediately after the data flows in, the most up-to-date information is available for the various departments.
The script and dashboard we have created ensure that a month and a half of work can now be done in 10 minutes: a major efficiency improvement.
An additional advantage is that the Malmberg teams now have up-to-date information. So they no longer have to make decisions based on information from the more distant past.
A data-driven future
In the future we will build predictive models at Malmberg. For example, we will identify signals to predict when the publisher is in danger of losing a customer. We are also expanding the PowerBI dashboard with additional filters so that everyone can create their own viewwith relevant data. In this way, Malmberg is increasingly able to make data-driven decisions.
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