The difference between data management and data governance should be easily understood by any professional, especially one working in the data field. Unfortunately, even though there's a lot of information out there about these topics, I feel that they create more confusion that clarification.
So let's understand what the difference is between data management and data governance.
I want to preference that within the data community there are no clear aligned definitions of data management and data governance, their roles, and relationship. The definitions, roles, and relationships between these two concepts change in different contexts. So if you have a different view of what is the difference between data management and data governance than how I’m going to explain it in this article or video, please let me know in the comments below.
Data to value journey
"Data is valuable!", "Data is an asset!" We hear this a lot.
We need to get value out of our data, because this value is wort a lot, it’s what gives data-driven organizations a competitive advantage. There's no argument there.
But, in order for our data to turn into value, it doesn't take a simple path. A LOT of things need to happen to it along the way. Just to name a few, our data needs to get:
Our data also needs to be:
- Made consistent
- Accessible to the right people and systems
- And so on
In other words, our data needs to be managed.
What is data management?
All of the above, all that was listed in the bullet points and more, represents data management. Data management is a business function of planning for, controlling and delivering data and information assets.
"A business function of planning for, controlling and delivering data and information assets."
We need to manage our data if we're going to get value out of it and have things like:
- Consistency and accuracy in our reports
- A single view of our customer
- Actionable information that informs our business decisions
- A data-driven organization
- Competitive advantage
What is data governance?
But how can we get all of that? In particular:
- How can we get clean data, documented metadata, categorized and classified data and so on? We need POLICIES.
- What are the steps to clean our data, to make it consistent, to provide access, to secure it, define it? We need PROCESSES.
- How do we ensure consistency in our cleanliness, definitions, and so on. Well we need STANDARDS.
- Who's going to create all of these policies, processes, standards and rules and definition? who will approve them, who will maintain them, who will enforce them. We need ROLES & RESPONSIBILITIES.
This is what data governance provides.
"A discipline which provides the necessary policies, processes, standards, roles and responsibilities needed to ensure that data is managed as an asset."
(George Firican - LightsOnData)
Data management vs. data governance
The overall data management function is made out of these 11 components, or knowledge areas as identified by the Data Management Association International (in no particular order):
- Data Governance
- Data Quality
- Data Architecture
- Data Modeling & Design
- Data Storage & Operations
- Data Security
- Data Integration & Interoperability
- Document & Content Management
- Reference & Master Data
- Data Warehousing & BI
- Metadata Management
Data governance is one of these 11 data management knowledge areas. As you can see it sits here in the middle because it has a relationship with all these areas. There is certain overlap between data governance and data quality, data security, metadata, reference data and so on which can create some confusion, I know, but in the end data governance can help us tie it all together.
Where to learn more
Practical Data Governance: Implementation
Learn the practical steps and best practices, and use the provided templates to put together and implement your data governance program from scratch or improve the one you have.