Platform Data Knowledge & Governance

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The Data platform is governed by autonomous Data Engineers. In this context, they code all the flows crossing the platform in its entire width. The coherence of the work carried out by all the Data Engineers is not clearly defined. The rotation of Data Engineers increases the entropy pressure on the platform. This results in effects such as:

• Unclear knowledge of the data available in the platform

• Consistency of data names not always ensured

• Data documentation is not clearly defined

• Consistency of indicators at the single and consolidated level not always aligned

• Productivity of Data Engineers not aligned with the increase of the Platform volumetry

What effective actions can be taken to regain control over all of these parameters for driving the Data Platform? And beyond this, upscale Data governance at the Company level?

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The Medallion architecture adds a central "Silver" layer in the Data Platform.

This is exactly the place to plug in the governance of the Knowledge of the Data Platform.

In a Model-Centric approach, it is possible to govern the Enterprise Data Model associated with an automated deployment in the Data Platform.

This governance has the following virtues:

• Knowledge of the Data available in the Platform mastered

• Streamlining of the activities of Data Engineers

• Productivity of Data Engineers and multiplication by increasing the volume