Master data management has never been more strategic than it is in today’s digital era . It represents the company’s core: it ensures the transfer of master data, which is vital for the company’s activity, from and to all parts of the organization, and doing so, it guarantees the company’s sustainability and growth. Unfortunately, unlike the human body, proper regulation of this circulatory system is far from being innate.

Data Governance, what does it mean?

Leading a master data governance project means defining the management rules for master data, like a civil code for master data. Governance aims to clarify how data is used, who owns it and who is responsible for its quality, according to the processes, solutions, norms or standards.

It involves looking at the semantics of what an item, a client, or a supplier within the database is, to define their life cycles, to consider everyone’s roles in the organization regarding the creation and maintenance of this data, and to rethink the applicative architecture where the data belongs.

It addresses several challenges such as:

  • Reliability: ensuring accuracy and access at all firm’s levels.
  • Consistency/quality: securing cre- ation/maintenance processes.
  • Security: ensuring the security of data and exchanges.
  • Agility: quickly adapting optimizing to operational needs.
  • Productivity: optimize the workload needed to manage master data.

First, rally the Executive Committee: why, how?

Master Data’s governance is a complex and transversal subject, which requires important cultural, organizational and technical transformations. Although operational committees are more and more aware of the importance of data quality, challenges regarding the core business are generally underestimated. In this context, the risk that these initiatives might not be valued or prioritized enough, is real.

Therefore, the alignment of the executive committee with the challenges and the expected results of the project is crucial before moving on to more operational phases of implementation of the project. A master data governance project does indeed create value, which ideally needs to be proven.

A master data project, source of profit rather than center of cost

Today, ambitions about data management are high.

However, in reality, many companies are far from their objectives on this topic: 91% have not yet reached their “transformational” level of maturity regarding data management and analysis, although it has been a number 1 investment priority for ISDs over the last years (Gartner).


of companies consider that the quality of Master Data is not satisfactory

Several root causes have been identified by companies:

  • Lack of data culture within the company (lack of awareness, lack of competences…), mentioned by 58% of companies.
  • Inexistent or poorly shared rules/ definitions, mentioned by 48% of companies.
  • Difficulties related to organization (unclear governance, poorly defined roles…), mentioned by 37% of companies.
  • Inefficient Master Data Management (MDM) processes (lack of transversality, existence of several parallel and poorly coordinated processes regarding the creation/modification of data etc.), mentioned by 34% of companies.
  • Inappropriate informatic applications, mentioned by 20% of companies.

To facilitate the support of leaders, be smart with the timing

Even if the intrinsic value created for the firm by the project is sometimes difficult to estimate, reminding of its utility in the scope of a larger initiative, like a small brick in a large wall to build, is particularly convincing. This requires knowing how to take advantage of the transformation opportunities of the firm, to launch the project at the right time.

Therefore, one essential question needs to be asked: in which context is it relevant to raise the question of Master Data governance?

Drive the operational and manage the change

The executive committee’s adherence is necessary but is not enough. Even if it seems to be obvious, a successful master data project is 80% effort on the operational side, by frontline employees, and 20% by IT. The difficulty is to have frontline employees feel involved in the project, as such topics are sometimes far from their day to day activities. Involving some operational profiles who have competences in data and systems is often a profitable option for the firm.

In this environment, your approach to support changes at all levels is a key to success:

Top management to encourage initiatives and sponsor them

Middle management to build the data governance of tomorrow

Frontline employees to contribute to data quality

We know who to involve and how to involve them...Now let's see what to do ?

To learn more about the strategic priorities and limits of the transformation, download the publication.

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