What a data platform should do

A data management platform is not an end in itself, nor is it an IT prestige project. It makes sense if it helps to solve specific business problems and create measurable value - for example through more efficient processes, better decisions or new business models.

Many companies fall into a vendor lock-in without realizing it. They decide on a platform early on, sign contracts and build their first solutions. It later becomes apparent that certain requirements can only be implemented with considerable effort, individual workloads become too expensive or important functions only work via detours. 

In addition, there are often skill mismatches: the chosen technology does not match the existing skills in the company. The introduction then quickly turns into a major training and organizational project - while the business is already expecting results.

Fragmented reality

In theory, there is a central platform with clear data models and uniform key figures. In practice, the situation is usually different: Companies work with many operational systems such as ERP, CRM or specialized applications as well as external data sources or IoT data. 

On top of this lies a mature analytics landscape consisting of BI tools, Excel evaluations, embedded analytics and initial AI projects. Many of these solutions have grown historically and are interconnected at certain points. This leads to data silos, different KPI definitions and governance that is difficult to manage consistently. Some companies also try to use their ERP system as a central analysis platform. 

However, this contradicts the clean-core concept: ERP systems are optimized for transactions, not for complex analytics scenarios across many data sources - especially not for modern AI applications.

Beyond the technology, the task of a data platform is clear: it integrates data from different sources, harmonizes and curates it and makes it available for reuse in reporting, planning, advanced analytics, AI or automation. This creates a common database for different roles in the company - from data engineers and analysts to controllers and specialist departments with self-service analytics. The central principle is: once properly modeled, used many times over. If each department maintains its own data models and KPIs, the effort and sources of error multiply. A data platform creates common models and governance structures that can be used for many applications.

Feature lists rarely help

In selection projects, platforms are often compared using extensive feature matrices. These appear structured, but are only meaningful to a limited extent. Functions are implemented differently and can rarely be compared directly. Above all, the business value hardly depends on a single feature. Rather, the decisive question is: which platform can we use to implement our specific use cases in an economical, sustainable and scalable way - with the people who work in the company?

Topics such as data governance or scalability are often considered too late. As long as nothing goes wrong, governance rules seem like an obstacle. However, when it comes to audits, security incidents or data protection issues, it becomes clear just how important clear roles, rights and traceability are.

The situation is similar with scaling: pilot projects usually work without any problems. Only when more data sources, specialist areas and users are added does it become clear whether a platform is really viable from a business, technical and economic perspective.

Conclusion

Data platforms are not an end in themselves. They are successful when they help to make better decisions and tap into new potential. Those who select platforms not according to feature lists, but according to specific business needs, realistic capabilities and a clear value logic, avoid unnecessary vendor lock-in - and make the data platform a real building block of the corporate strategy.