Digital transformation and better internal collaboration thanks to insight into offline and online data.
- Customer case
- Data Engineering
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.
Business Manager Joachim gaat graag met je in gesprek over wat we als datapartner voor jou en je organisatie kunnen betekenen.
Business Manager+31(0)20 308 43 90+31(0)6 23 59 83 email@example.com
Receive data insights, use cases and behind-the-scenes peeks once a month?
Sign up for our email list and stay 'up to data':
You might find this interesting too:
The COVID-19 Violence Tracker
The outbreak of the corona pandemic in early 2020 has turned the world upside down. In addition to countless infections, hospitalisations and deaths, we also saw an outbreak of violence in many countries. Citizens took to the streets, sometimes violently, to protest against the measures taken, but domestic violence also increased in many places and fear and frustration played a role in racism.
Deliver reliable and meaningful data to everyone from a solid, scalable infrastructure.
5 reasons to use Infrastructure as Code (IaC)
Infrastructure as Code has proven itself as a reliable technique for setting up platforms in the cloud. However, it does require an additional investment of time from the developers involved. In which cases does the extra effort pay off? Find out in this article.
A well-organised data infrastructure
FysioHolland is an umbrella organisation for physiotherapists in the Netherlands. A central service team relieves therapists of additional work, so that they can mainly focus on providing the best care. In addition to organic growth, FysioHolland is connecting new practices to the organisation. Each of these has its own systems, work processes and treatment codes. This has made FysioHolland's data management large and complex.
Implementing a Data Platform
Based on our know-how, the purpose of this blog is to transmit our knowledge and experience to the community by describing guidelines for implementing a data platform in an organization. We understand that the specific needs of every organization are different, that they will have an impact on the technologies used and that a single architecture satisfying all of them makes no sense. So, in this blog we will keep it as general as we can.
Improved data quality thanks to a new data pipeline
At Royal HaskoningDHV, the number of requests from customers with Data Engineering issues continue to climb. The new department they have set up for this, is growing. So they asked us to temporarily offer their Data Engineering team more capacity. One of the issues we offered help with involved the Aa en Maas Water Authority.
A scalable machine-learning platform for predicting billboard impressions
The Neuron provides a programmatic bidding platform to plan, buy and manage digital Out-Of-Home ads in real-time. They asked us to predict the number of expected impressions for digital advertising on billboards in a scalable and efficient way.
Why do I need Data Engineers when I have Data Scientists?
It is now clear to most companies: data-driven decisions by Data Science add concrete value to business operations. Whether your goal is to build better marketing campaigns, perform preventive maintenance on your machines or fight fraud more effectively, there are applications for Data Science in every industry.