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Enabling fast, easy, and secure data analysis


With data as a key ingredient, any company can invest time and resources to explore how to improve their products and services. Data exploration can help find information on how to reduce downtime, increase efficiency, or enhance quality. And if such an exploration turns out promising, one would want to industrialize it and incorporate it into daily practice as soon as possible.

However, the bigger the company, the more complex it becomes to provide for these data explorations. With large amounts of sensitive data, we are dealing with serious challenges; think of data availability, access management, data trust-ability, scalability, security; in addition, specific domain knowledge and skills are required to correctly explore the data.

How to prevent employees from locally storing and processing confidential data, from sharing results via insecure channels, and from using different data formats and tools so that collaboration becomes nearly impossible? And how to ensure data exploration does not take ages to run on a local laptop but can be shared rapidly? For that, a solid data analytics platform is essential.

That’s why one of our enterprise customers asked us to collaborate on a solution to enable their employees to securely and efficiently analyze data and share results.


Working shoulder to shoulder, we’ve built a secure and easy self-service platform for data exploration. A cloud platform, with an intuitive front end, to easily select your preferred data exploration toolset, the trusted dataset you want to explore, and the tool you like to use to present your results.

Below, we’ve highlighted the most important elements of this platform, and included a video that demonstrates its functionality:

Trusted data sets to ensure data quality
To effectively analyze data, you first want to make sure that the data you offer is correct. If the user can’t trust the data, the analysis is useless. That’s why we work with ‘trusted data’: data sets that are pre-processed, include metadata on their meaning and lineage, and that are monitored for completeness and quality.

Trusted tool sets to ensure ease of usage
There are multiple types of users in the organization, each having their own level of data skills and preferred analysis tools. To meet this demand, we offer various validated tools on the platform – from low code tools such as KNIME, to more specialized tools such as Databricks and MATLAB. And since the outcome of a data exploration is often a visualization, we’ve also included reporting tools (such as Power BI and Spotfire). After all, you want to share insights with colleagues in a way they directly understand it.

An intuitive environment to ensure self-service and collaboration
A very important part of data explorations is collaboration. To effectively collaborate, users need to be able to easily and securely share their results with others in a uniform way. We provide for this functionality in our user portal.

The portal ensures our platform is fully self-service. Users can manage their projects and store data in a secure way. The user portal, built in Mendix, allows them to define which project members can get access to the data or analysis results. They can also see the costs of their data processing, can select their preferred tooling, and more.

User experience
In an enterprise setting, solutions are not easily adopted. It requires internal marketing and above all: an excellent user experience. That’s why we’re continuously gathering feedback from our users to improve the platform, and offer clear documentation, multi-level training tracks, and support. After all, the most effective method to grow is having happy users recommending your solutions to colleagues.



With hundreds of active users, our platform contains many success stories. From teams that reduced data processing time from weeks to minutes, to teams that prevented unscheduled downtime of their machines in the factory by predictive data exploration, to teams collaborating and speeding up their root cause analysis.


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