Key challenges of data governance

Data governance is a key part of ensuring the success of digital transformation initiatives. The formal definition, provided by the Data Governance Institute, speaks of “a system of decision rights and responsibilities for information-related processes, executed according to agreed-upon models that describe who is in charge of making decisions derived from data analytics.”

This means that if a data governance strategy is not implemented in an organization, investments made in innovations such as artificial intelligence, analytical tools, business intelligence, big data or machine learning, may be underutilized or may not bring any value to the business.

Planning and monitoring

On the other hand, carrying out a data governance vision requires enormous planning and oversight efforts. It is not just a matter of stating: it is necessary to document how each process uses data and evaluate how this is aligned with business objectives. This is a complex task, which must be done with extreme care, but if done correctly, the results will far outweigh the effort.

On the way to the data governance strategy, organizations will encounter some barriers. The first, of a cultural nature, has to do with the “ownership” of data. There are still companies with siloed data structures, where each area considers that the information collected belongs to it. In other cases, the IT area is presented as the owner of the data. This generates friction as to who should be in charge of the governance project. For this reason, the sponsorship of top management is essential to unblock the situation and give the initiative the importance it deserves.

The figure of the CDO

An emerging figure in recent times is the CDO (chief data officer), a c-level executive who is specifically in charge of data leadership. If we talk about “governance” this function would be the “Governor”. The CDO is responsible for continuously monitoring the governance strategy, data quality, the evaluation of new business cases, establishing metrics to verify that everything is working as expected, incorporating the necessary technological tools to support data-related activities, identifying and prioritizing data assets and ensuring that they are always aligned with the organizational strategy.  

On the other hand, new roles are emerging, such as data owners, responsible for defining data in business terms; or data stewards, in charge of more detailed characterizations.

Beyond leadership and ownership, a good governance model must ensure that data reaches all levels of the organization and that it is available and accessible to each employee at the exact moment they need it to make a business decision.

A matter of quality

The second step, often underestimated by organizations, is to assess the quality of the data. An organization needs a catalog detailing everything from usage and type of access to storage point or general characteristics, as well as having detected the presence of erroneous or unnecessary data. Another benefit of the catalog is that it facilitates control and avoids the misuse of data.

One of the big challenges has to do with aligning the data strategy not only with business objectives, but also with contextual issues: the most visible case is that of regulated industries. Indeed, governance is also a key ally for compliance.

And, of course, a change of mindset is essential to guide the organization towards a true data-driven model. 

The company must be convinced that data is the source of its ability to make more and better decisions in order to turn it into a source of current and future success.