Publisher Malmberg collects a lot of off- and online data. More and more educational institutions are using online licences to supplement (or replace) printed teaching material. To respond to this, Malmberg makes use of 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 surrounding data more efficient.
During discussions with the internal stakeholders, we found out about 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 expanding the current analysis spectrum.
We soon discovered that the data team spent 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.
We wrote a Python script for this, making manual data manipulation largely unnecessary. Using this, Malmberg’s internal data system can automatically read various off- and online data streams. Based on the results of the script, we built a PowerBI dashboard. This dashboard shows all Key Performance Indicators (KPIs) of the various departments within the organisation.
The PowerBI dashboard provides all teams within Malmberg with relevant information. They have more insight into what other teams are doing and how they are performing on their KPIs in relation to the goals. Also, the different departments now know from each other which analysis issues and information needs there are. As a result, internal cooperation has improved considerably.
Because many manual processes are now automated, the Malmberg data team saves a lot of time. This time can, for example, be spent digging deeper into the data in order to be able to answer even more analysis needs. The monthly report that was previously created with Excel is now refreshed with a single click of a button using the Python script and Power BI. In this way, the most up-to-date information is available to the various departments almost immediately after the data flows in.
The script and dashboard we have created ensure that a month and a half's work can now be done in 10 minutes: a considerable efficiency leap.
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 threatens to lose a customer. We are also expanding the PowerBI dashboard with extra filters so that everyone can create their own view with relevant data. In this way, Malmberg is increasingly able to make data-driven decisions.