Collaborative data governance

By Matthieu Gauthey, Manager of the Data Governance offer, Micropole Lyon

Data is now the main lever for companies to improve their competitiveness, even in areas where it was only an IT resource a few years ago. With the volume of data doubling every year, it is becoming difficult to manage, and the resulting changes are increasingly at the expense of users.

In terms of data governance, two approaches have long been opposed: letting business users enrich the data (e.g., product catalog) or automating the aggregation of data from several sources (e.g., customer database) by an IT system. The common objective, which was to obtain clean, unduplicated data of good quality, can no longer be achieved with these models.


The first to understand that data is not just the preserve of a multitude of supercomputers are the GAFA giants. Other new models (Uber, Airbnb) have emerged with the same postulate: the truth must be captured and harvested at the source. For data, the source is the user himself.

Unfortunately, users do not easily give out information about themselves, so it is necessary to provide a service in return.

For example, a user who agrees to provide accurate geographic coordinates receives an efficient, fast and free location-based service in return. Similarly, product reviews completed by consumers are becoming as important as the product features themselves in the purchase decision.

Once in the office, the user expects the same thing from enterprise tools. Contribute yes, but with meaning and efficiency. This is what is at stake for them to accept to share their information.


Many software packages remain in a logic where the process takes precedence over the user's interest. ERPs and CRMs rightly structure user input through well-established business processes. However, do users really see the benefits of their contributions? Do they feel they are taking part in data governance or do they see themselves as a forced performer?

The implementation of a collaborative Master Data allows to structure the roles and expectations of each actor in the data life cycle. The notion of contextualization is increasingly important for the user to see the value in his daily work.


The other lesson learned from the GAFAs is that many data governance initiatives fail because of a lack of user buy-in.

Often, the implementation of a master data system can be summarized as a technical project focused on data modeling or management rules.

For several years now, when we approach the design of a Master Data, we have systematically met with the various businesses involved in the data life cycle. This step is very important in order to understand the context in which users understand this new tool. The interviews allow us to bring out the expected benefits in their daily work. We also bring in user experience specialists to make interactions more fluid and accelerate user acceptance.


Often, governance projects focus on the target to be reached without taking into account the quality of existing data. However, our experience of more than 15 years on these subjects shows that the level of quality is regularly underestimated at the launch of projects.

In this context, the collaboration of users is often long and tedious, even with the help of high-performance tools. This is due to the volume of data and the knowledge required of the data to be corrected.

Micropole has recently developed a "Data Quality Factory" offer for business users. Based on predictive algorithms, it facilitates user contribution by suggesting corrections to be implemented.

It is therefore important to overcome preconceptions and move towards a truly collaborative approach in order to not only transform data, but above all to improve it in order to derive key benefits in supporting your projects.

With over 15 years' experience in the field of Master Data platform integration, Micropole can advise and support you in the choices you make regarding the reflection and implementation of solutions that will adapt to the specificities of your market, but also of your organization. Even if data is even less tangible than the oil to which it is often compared, the human being must remain at the center of all reflection, in order to take advantage of it in the best conditions.

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