In a world where companies aspire to become data-driven, data management and data governance are concepts that must be mastered at all costs. Too often perceived as related or even interchangeable disciplines, the differences are important.
A company wanting to become data-driven must master the disciplines, concepts, and methodologies that govern the collection and use of data. Among those that are most often misunderstood are data governance and data management.
On the one hand, data governance consists of defining the organizational structures of data – who owns it, who manages it, who exploits it, etc. On the other hand, data governance is about policies, rules, processes, and monitoring of indicators that allow for a sound administration of data throughout its life cycle (from collection to deletion).
Data management can therefore be defined as the technical application of the recommendations and measures defined by data governance.
Data governance vs. data management: their different missions
The main difference between data governance and data management is that the former has a strategic dimension, while the latter is rather operational.
Without data governance, data management cannot be efficient, rational, or sustainable. Indeed, data governance that is not restated into appropriate data management will remain a theoretical document or a letter of intent that will not allow you to actively and effectively engage in data-driven decision-making.
In order to understand what is at stake, it is important to understand that all the disciplines related to data are permanently overlapping and interdependent. Data governance is a conductor who orchestrates the entire system. It is based on a certain number of questions such as:
- What can we do with our data?
- How do we ensure data quality?
- Who is responsible for the processes, standards, and policies defined to exploit the data?
Data management is the pragmatic way to answer these questions and make the data strategy a reality. Data management and data governance can and should work in tandem. However, data governance is mainly concerned with the monitoring and processing of all the company’s data, while data management is mainly concerned with the storage and retrieval of certain types of information.
Who are the actors of data governance and management?
At the top management level, the CEO is naturally the main actor in data governance, as they are its legal guarantor. But they are not the only one who must get involved.
The CIO (Chief Information Officer) plays a key role in securing and ensuring the availability of the infrastructure. However, constant access to data is crucial for the business (marketing teams, field salespeople) but also for all the data teams who are in charge of the daily reality of data management.
It is then up to the Chief Data Officer (CDO) to create the bridge between these two entities and break down the data silos in order to build agile data governance. He or she facilitates access to data and ensures its quality in order to add value to it.
And while the Data Architect will be more involved in data governance, the Data Engineer will be more involved in data management. As for the Data Steward, he or she is at the confluence of the two disciplines.
How combining the two roles helps companies become data-driven
Despite their differences in scope and means, the concepts of data governance and data management should not be opposed. In order for a company to adopt a data-driven strategy, it is imperative to reconcile these two axes within a common action. To achieve this, an organization’s director/CEO must be the first sponsor of data governance and the first actor in data management.
It is by communicating internally with all the teams and by continuously developing the data culture among all employees that data governance serves the business challenges while preserving a relationship of trust that unites the company with its customers.