Data Governance was a trending topic in 2019! Enterprises dealing with their data are realizing how important it is to implement this discipline in order to effectively and efficiently manage their data assets.
To fully understand what Data Governance is, many definitions exist:
“Data governance is a quality control discipline for adding new rigor and discipline to the process of managing, using, improving and protecting organizational information.”
IBM Data Governance Council
“Data governance is the exercise of authority and control (planning, monitoring, and enforcement) over the management of data assets.”
Dama DMBok
“Data Governance is a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods.”
The Data Governance Institute
“Data governance is the formal orchestration of people, processes, and technology to enable an organization to leverage their data as an enterprise asset.”
MDM Institute
What is certain is that these definitions are far from being fun! But before we get into defining what data governance is, let’s see the reasons why it has become a strategic subject for enterprises.
What drives enterprises to implement Data Governance?
In our experience, we’ve learned that enterprises tend have one or several of these issues within their organizations:
Tribal knowledge: usually enterprises have a person or select group of people who produce, work with and understand their data assets. However, the rest of the organization has no knowledge on their data (where it comes from, its value, its quality, etc.). This results in enterprises having siloed information that is difficult to use and share.
Big mess: In the last decade, many complicated information systems have appeared resulting in enterprises having massive amounts of unorganized data. Data users are therefore subjected to data where their quality, uses or even location is unknown.
Compliance: All enterprises are subjected to some form of compliance may it be data privacy, general data usage or ethics. Those that do not have governance in their organizations will suffer from these rules and regulations.
Implementing Data Governance therefore helps enterprises resolve these problems!
With Data governance, enterprises are able to create a data fluent organization, organize their data and comply to the increasing regulatory demands.
Why Data Governance fails in enterprises
However, enterprises often look past implementing Data Governance. These organizations believe that:
- Governance implies control and not value
- It slows down business
- It is more of an IT concern than a business concern
- Governance projects implemented in the past have failed too many times before
- It is too big, there are no resources available
- It’s nice to have, but not a priority
What strategy for your Data Governance?
With all of that said, it is essential for enterprises to build the right sized Data Governance. There is not a unique way of implementing data governance: enterprises must know what kind of governance they need, the right style, and where they stand in the governance landscape.
We’ve identified two types of strategies when to comes to implementing Data Governance.
Defensive Data Governance
This dimension of Data Governance is more focused on risk control and risk management. Here, enterprises are making sure they respect data compliance (such as GDPR), privacy, and security. This framework goes hand in hand with some of the definitions seen above where it is more about “control”.
Offensive Data Governance
This dimension is more focused on producing value with data. With an offensive approach to Data Governance, enterprises are prioritizing value production and innovation.
