Many companies store business-critical data in relational databases. At the same time, there is a growing need to enable operating departments – without in-depth database knowledge – to process this data securely and in a structured manner.
In practice, companies often opt for an in-house solution. This allows for the precise implementation of specific requirements — from integration into existing business processes to direct data maintenance by operating departments via custom interfaces.
However, as usage increases, so do the demands. Issues such as governance, data validation, and audit-proof documentation often have to be addressed retrospectively. At the same time, unclear requirements or subsequent changes can lead to inconsistent data models. The effort required for integration is often underestimated, resulting in fragmented solutions that are difficult to maintain.
The actual effort required over the entire lifecycle — from development to production — is difficult to estimate in advance. This is precisely where the fourth part of our comparison series comes in, highlighting when a standardized approach like BOI FreeDa can be more efficient. Where this transition occurs depends heavily on the specific use case.
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