Data governance is the set of processes, procedures, policies, standards and metrics that organizations implement to understand what their data is, where their data is and how best to use it. It also dictates who gets access to what data, in what situations, and using what methods.
Data governance typically requires a dedicated governance team, including a steering committee to act as the governing body and a group of data stewards to enforce everything. Ideally, data governance also involves executives and other stakeholders, along with the IT and data management teams.
“Data governance is an enterprise-wide undertaking. It’s something that is designed to be supporting all data across the entire company,” Malcolm Hawker, head of data strategy at data management company Profisee, told Built In. “It’s the rules of the road when it comes to data.”
What Is Data Governance?
Data governance is the set of internal standards an organization follows to ensure its data is compliant, accurate, usable, secure and available to the right people and under the right circumstances.
Data Governance vs. Data Management
Data management refers to the process of creating, collecting, storing, maintaining, securing and using all the data in an entire company; it covers the entire data lifecycle. Data governance is a component of data management, along with data quality, data security, data warehousing and so on.
“Data governance is policies and procedures,” Hawker said. “Management is doing it. It’s putting a shovel in the ground and actually implementing the policies and procedures.”
Why Is Data Governance Important?
Meeting Data Regulations and Compliance
To ensure compliance with government regulations and data privacy legislation, companies have to know what kind of data they’re collecting, where it is being stored, how it is being used, and by whom.
And as governments continue to tighten regulations on data usage, data privacy and even companies’ environmental, social and governance metrics, data governance is becoming even more important in making sure businesses’ data collection, usage and security stay within the bounds of the law.
The European Union’s strict data privacy regulations, for example, can result in tens of millions of euros in fines for companies that violate these laws. And while California’s Consumer Privacy Act isn’t as harsh in some respects, it punishes businesses that blatantly violate this act with fines up to $7,500 per record.
“If you’re a large company, and you fall under one of the global privacy protection acts, and you don’t have data governance, then you’re going to be in trouble,” Peggy Tsai, chief data officer at data security company BigID, told Built In. “Or, if you’re any company, and you don’t want to be hacked or have any kind of cybersecurity breach, you better have a data governance program.”
Boosting Business Efficiency
Data governance has also shown to be more than just a means of securing one’s data and ensuring it is compliant with the law. It can also be a business enabler — “rocket fuel,” as Hawker put it — that allows an organization to run more efficiently and try new things with its data. Particularly when it comes to the implementation of artificial intelligence, which is something most companies are looking to do more of.
Supporting New Technologies
Companies are using AI — especially generative AI — to streamline a variety of business processes, from marketing to people management. Executing all of this well requires an immense amount of data. And to mitigate any bias, ethical or legal issues, data governance should be an important piece of any company’s AI execution strategy.
“A good data governance program really enables you to take advantage of that kind of technology rapidly, safely, legally, ethically and in a commercially valuable way,” Jay Militscher, VP of data analytics at data intelligence company Collibra, told Built In. “Not having a good data governance program means you can do really bad things without even intending to.”
So whether a company is trying to reduce its carbon footprint, or experiment with things like chatbots and AI content generators, data governance is a must-have. “No organization can be data-driven, or can do great analytics, or can do any type of AI or predictive analytics, without having a basic data governance framework in place,” Tsai said.
Data Governance Principles
When it comes to implementing a data governance program, there is no one-size-fits-all method. All companies are different. Still, all data governance programs have some guiding principles.
Break Down Data Silos
Companies are often structured into isolated departments — each with very different ways that they create, manage and define their data. High-quality data for the marketing department isn’t the same as what it would be for the finance department. And people working on the sales side might quantify customers differently than those working on the product side. This inevitably makes it difficult for a company to have a consistent understanding of the data it owns, where it is stored and what to do with it.
A well-executed data governance framework ensures that all of this disparate data is consistently defined, organized and protected so that it can serve as a shared resource. It covers all the strategic, tactical and operational aspects of managing data so that everyone — no matter what department they’re in — is on the same page.
“A great data governance program is going to break down those silos and get the whole organization connected through data,” Militscher said. “Everybody can have a common understanding of the organization’s information.”
Ensure Data Accuracy
Without an effective data governance program, any data inconsistencies across an organization might not get resolved or even noticed because companies are so often siloed.
For example, customer names may be listed differently depending on the department, which could complicate data integration efforts and create data integrity issues, ultimately affecting the accuracy of business intelligence, analytics and more if it is not detected and resolved.
Focus on Value
Perhaps the most important guiding principle of all is to focus on value — not just how data affects the value of the business, but the value of the data itself. It’s important for companies to have a firm understanding of how much it costs to create a new piece of data, or how much revenue they generate from having good data
Valuing is vital because it gives data leaders a quantifiable justification for placing more controls on the way different teams use and access their data. It looks beyond the vague idea of data governance as a means of achieving “better data,” and instead offers commensurate proof. After all, it’s a lot easier to tell people what to do if you have data showing it will save them money, or increase their revenue.
“It’s like world peace. Everybody knows it makes sense. ‘Better data’ — intuitively, we know it makes sense,” Hawker said. “And that’s not just kind of a feeling. It’s actually true if you take the time to quantify it, if you take the time to model it.”
Data Governance Best Practices
No two data governance programs are created equal. But there are some general best practices organizations are encouraged to follow when creating their own data governance framework.
Define Specific Business Terms and Concepts
A big part of data governance is about building a glossary of definitions in order to establish what specific business terms and concepts mean. For example, what constitutes an active customer. By establishing a common vocabulary for business data, all members of the company are on the same page, making it easy to move forward in their data governance program.
Build a Data Catalog
Another common best practice is to build a data catalog, which collects metadata from company databases, data warehouses, data lakes, business intelligence systems and other sources, and uses it to create a searchable inventory of data assets. It also includes information such as how the data has been collected, where it came from, how it is being stored, what it is being used for, and who in the company has access to it. Tracking how data flows through an organization, and classifying them based on factors such as whether it contains personal information, helps determine how data governance policies can be applied to individual data assets.
Alternative: Start With an Outcome
Doing data governance in such a broad and comprehensive way isn’t necessarily the most popular or efficient way to do it, though.
“If you’re starting from zero, and you’re saying ‘Let’s implement data governance!’ And you follow the standard best practices approach. It’s inevitably a multi-year thing for most organizations,” Hawker said. Not to mention an expensive one. “And if you’re perceived as slowing things down, if you’re perceived as getting in the way of business processes, you’re going to fail.”
Instead, Hawker suggests turning the process upside down and starting with an outcome. Identify a measurable KPI that has already been established and that leaders in the company already care a lot about — reducing the time to onboard a new customer, for example, or identifying 20 percent more qualified leads. And then build a data governance program around that outcome to ensure that the goal can be reached.
Once that one outcome is delivered, do it again for a different one, and so on, until a full data governance program is in place.
No matter how an organization chooses to approach it, data governance isn’t easy. But it can certainly be rewarding. Tsai likens data governance to demolishing a house, where the company is the house and its data is the infrastructure that keeps the house standing.
“Once you start ripping the house apart — the actual plumbing and the framework — that is, I would say, the dirty work that comes with doing data governance. And most companies don’t want to invest in it because it takes so much time and effort to do it,” she explained. “But there’s no risk. There’s no harm in just trying something.”
Benefits of Data Governance
Data governance has a variety of benefits that make it worth the effort.
Beyond more accurate data analytics and stronger regulatory compliance, data governance can improve data quality and even help identify new market or product opportunities.
Enhanced Data Monitoring
Data governance also allows companies to intricately catalog their data, and closely measure and monitor its usage, potentially identifying any dark data — data that is going unused, which is a waste of opportunity and money — that may be lurking around.
“While it’s cheaper to store, manage and use data these days with great cloud technologies that are out there, it isn’t free. There is a cost associated with it. And data volumes are growing, and the use is growing,” Collibra’s Militscher said. “If data doesn’t need to be collected or stored because it’s never going to get used, then get rid of it. You’re saving yourself some money.”
“Great data governance is completely invisible and completely silent. You know you are doing it well when the business is humming.”
In the end, having consistent, uniform data across an organization, as well as clear rules regarding who can access that data, where and when, can help businesses run with more efficiency. They no longer have to be bogged down by the chaos that so often comes with disorganized data and unclear requirements. Instead, they can move forward with confidence and agility, without having to work too hard.
“Great governance is completely invisible and completely silent. You know when you are doing it well when the business is humming,” Hawker said. “When data is at the very core of how you operate, but it’s not viewed as something different from how you operate. It’s just viewed as what flour is to a cake.”
How to Get Started With Data Governance
While data governance has obvious advantages, this method is only effective when applied in the right way and in the right situation. Below are a few tips companies can follow to ensure they get the most out of their data governance efforts.
Confirm Whether There’s a Need for Data Governance
Data governance is something that requires support and cooperation from the entire employee base. If teams or company leadership aren’t on board, it’s difficult to implement a data governance program. Situations that call for data governance include when data has become too complex, company data has become too much for one team to handle or teams need a holistic view of data to better perform their roles.
Create Clear-Cut Data Governance Policies
To make the transition smoother, teams can come together and develop policies for how to adopt a data governance plan. This lays the groundwork for how to make decisions regarding matters like using standard terminology, granting access to users and distinguishing when data can be used. Businesses then have their own understanding of ‘compliance’ and can ensure employees are following company-specific data governance rules. This should all be documented in a centralized location.
Establish Concrete Goals for Data Governance
Teams must define the goals they want a data governance program to address. Perhaps employees want to more easily leverage data to predict future business trends or use sales data to improve the customer experience. Data governance is meant to address specific goals before being scaled to meet other challenges if needed, so it’s crucial to follow methods like the SMART goals approach to keep the scope of a data governance program manageable.
Design a Data Governance Roadmap
All interested employees can come together and brainstorm a data governance roadmap that lays out important details to win over stakeholders. This roadmap covers things like data governance decision frameworks, how the program impacts teams and what metrics will be used to determine the program’s success.
Determine Data Access for Appropriate Personnel
A major question with any data governance program is who gets access to what data. Maybe only members on the IT and data analytics teams gain full access, for security reasons. Or perhaps marketing and sales personnel also get access to inform their campaigns and strategies. Keep in mind that data access can also change over time as the program evolves and business and security needs change.
Measure the Impact of Data Governance
The metrics that determine the success of a data governance program depend on the goals teams and stakeholders established for the program. So, metrics can include everything from tracking employee productivity to monitoring the number of customer conversions. Based on their findings, teams may decide to continue the program, end it or even scale it to address challenges in other areas of the business.
Frequently Asked Questions
What is data governance?
Data governance encompasses all of the policies and procedures companies set in place to ensure their data is secure, reliable and accessible and usable for authorized personnel. This way, data is only used for specific purposes and can be trusted by those relying on it.
What are some examples of data governance?
Storing data in one location, limiting access to members on the data analytics team and removing outdated or inaccurate data are all examples of data governance in action.
What are the principles of data governance?
Connecting data across different teams and departments, making sure data is reliable and consistent and determining the value of data are a few principles of data governance to follow.