The Importance of Governance in Digital Transformation Projects

Many are unaware, but the key to success in numerous digital transformation projects lies in data mastery. Today, businesses invest substantial sums expecting immediate concrete results rather than the systematic control of data through adapted governance. It is clear that many advanced projects eventually face data quality issues and difficulties in identifying key stakeholders (the data owners capable of validating that information is reliable and usable). The overall finding is that a lack of data mastery impacts not only IT teams—who exhaust themselves trying to interpret and fix data—but also the business, which sees projects slip or qualitative objectives drift away. What, then, are the keys to ensuring data mastery and, consequently, the success of a digital transformation project?

First, let us examine several common pitfalls observed in these types of projects, using the implementation of Hadoop platforms as an example:

Lack of traceability of information sources injected into a Data Lake.
Lack of clarity regarding the governance to be implemented around the Data Lake. This point is often addressed too late. In all cases, it is essential to define the stakeholders and processes required to enrich, manipulate, and access Data Lake information.
The rush to deliver MVPs (Minimum Viable Products): Companies want to see products quickly to launch industrialization. However, issues related to reliability, knowledge, and data traceability mean that moving from POC (Proof of Concept) to full-scale production often fails.

How can these pitfalls be addressed?

1- CONSIDERING THE BENEFITS OF GOVERNANCE FROM THE PROJECT'S INCEPTION

Faced with the difficulty of mastering available data sources, identifying one or more data owners within the company is a frequent challenge. This necessitates asking the right questions upfront:

How do we identify these individuals?
How do we raise awareness among the right people so they understand the value of data governance and instill it within their teams?
What governance strategy should be implemented to benefit projects, such as a Data Lake?

Indeed, in a Data Lake environment, vast amounts of raw data (text, video, images, Excel) are stored. While this "massive" reservoir is at the heart of the project, only data governance will effectively:

Accelerate all digital initiatives: Every digital project requires production-ready data to move from "prototype" to "industrialization" as quickly as possible. Data Governance ensures that data is reliable, comes from the right sources, and is easily interpreted by project and business teams.
Master data knowledge: Sharing a common data language creates a foundation of knowledge and expertise (technical and functional), allowing for defined service commitments between data and stakeholders. This also helps master data propagation, from collection and calculation to final usage (traceability).
Manage risks related to regulated data: The goal here is to guarantee that data usage complies with corporate obligations (personal data, regulatory reporting, etc.). This requires qualifying data sensitivity levels, classifying them, and defining security requirements.
Manage data quality and utilize the right tools: The challenge is twofold: first, identifying the people capable of defining quality rules to establish reliability; second, identifying the most relevant point in the IT system to implement tools for monitoring and industrializing governance. This ensures consistency, relevance, and reliability while allowing for quality steering.

By covering these benefits, all necessary means will be in place to successfully execute the digital transformation project, benefiting all stakeholders across the IT landscape.

2- BEST PRACTICES FOR SUSTAINING GOVERNANCE

Another observation in digital transformation is that data governance is far too often confused with project governance. This is a critical error, as data governance concerns all of a company’s data, extending far beyond the specific projects using that data.

Furthermore, when data governance committees are established, companies often only consider data through the lens of specific projects. These committees set operational and strategic goals, whereas the primary purpose of governance is to make data reliable wherever it resides. Governance cannot be reduced to a strictly "project-based" vision. If a company intends to become Data-Centric, data governance must be placed at the center of the corporate strategy, motivating teams to adopt a cross-functional view of their data.

Finally, for this strategy to succeed, do not forget Change Management! It is essential to identify the key stakeholders who will trigger this data governance and whose objective will be to instill a mindset shift within the organization. Moving from a product-centric or client-centric strategy to a data-centric one requires a true paradigm shift in mentalities. In conclusion, digital transformation means new offerings, better data knowledge, and evolving mindsets. To guarantee project success, data governance must now be an integral part of the corporate strategy.