The weak maturity of data governance projects necessitate the implementation of good practices and feedback loops to constantly monitor and verify the validity of management rules on your data asset.

The following articles explain the characteristics of a data governance labeled as agile in order to:

1. Be as close as possible to your enterprise’s operational reality.
2. Adapt to your enterprise’s context and not the other way around.
3. Accurately reflect your data assets.
4. Unify and involve your collaborators.
5. Respond to changes quickly.

Enterprises consider that the classic approaches to Enterprise Data Management (EDM) require all parties involved to adopt a certain number of tools and procedures that can burden the processes of data discovery, thus becoming an obstacle to innovation. In a world where the variety as well as the volume of data is exploding, where new tools for data storage and processing ceaselessly pop up, a much more reasonable approach exists.

It is a means to give freedom to your collaborators to use tools more adaptable to their uses, whether it is to generate or to access datasets, of course, according to their authorized level!

This approach aims to centralize the knowledge that your collaborators have acquired from their datasets in a “data catalog.” The objective is to collect and to aggregate the metadata of your created or updated datasets from your tools and storage systems. It is from these platforms, unrelated to operations, that data governance can be executed without interfering with the daily work of your collaborators.

This non-intrusive method of addressing data governance calls for the enterprise move forward little by little. Experiment with and adjust your management rules on data and its metadata gradually so that you can establish a curation of your data assets.

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