It does not matter whether you work at a big or small business; your business will profit from the benefits that come with data governance. This article is going to explain what is data governance and why it’s vital to your business as well as learning  how to harness its power to your advantage.

Stating practices, processes, and communications, through data governance ensures you have the required data quality for your business.

This article covers, scroll ahead to your relevant section or dive in to learn all about data governance:

  • What Is Data Governance?
  • What Types Of Data Governance Exist?
  • Does Data Governance Exist In My Business?
  • Data Governance Use Examples
  • Why Is Data Governance Important?
  • Data Governance Use Examples
  • What Are the Benefits of Data Governance?
  • What Does Good Data Governance Look Like?
  • 5 Components Of Good Data Governance
  • Data Governance Roles
  • Data Governance Best Practices
  • Data Governance Tools


The Data Governance Institute (DGI) defines it as a practical and actionable framework that helps a variety of data stakeholders across any organization identify and meet their information needs. Data governance describes the formalized management of people, processes, and technologies to connect business and data strategies with data quality. 

Consequently, data governance supports data decision-making, enforcement, and operations. Think of data governance as a necessary businTess service.

Data governance affects data quality throughout the organization. You can see the importance of data governance guidelines and training materials through improved business use of its data.

What Types Of Data Governance Exist?

Data governance may look different depending on the culture, business or industry. Some organizations may have a centralized approach, implementing a council, governing body, or office responsible for successful data governance development and implementation.

Others may take a decentralized approach by delivering a data catalog and include data governance-related activities as part of their daily stand-ups. An organization may develop a mobile web app and train users to set privacy preferences. Then the group follows up with field studies on the usage.

In a third, non-invasive approach, companies create a data governance structure by integrating existing practices and processes. For example, you may be able to improve data quality among existing databases and Excel spreadsheets without having to spend lots of time and money.

data governance guide article photo

Does Data Governance Exist In My Business?

By default, any organization has some sort of data governance, even if it does not recognize the importance of data governance. Data will either be accessible or unavailable to other business users and systems, in part due to data quality.

Letting data governance just happen without understanding its importance will cause a business to fail. You want to put the best data governance resources together to get good data quality and data governance benefits.

Data Governance Use Examples

Exponentially growing customer data sets need to be secure and accessible. For example, let’s say you run a small food delivery service where you record sensitive data including, customer names, addresses,  phone numbers, and emails. Even a minor mistake in customer information leads to problems, including wrong and late delivery.

Plus, if you offer online payment transactions, you will need to manage this financial data. Thanks to a growing number of data regulations, in over 40 countries, it has become even more important to be careful about how and where you keep data.

In some cases, you may not be fully aware of the kind and amount of data the company deals with regularly, especially online. Cookies are a great example of this, every time a customer visits a website, a cookie captures small bits of information about the person. For example, some cookies track a user’s location.

Companies gather this type of data to analyze trends and make the customer experience better with the aim of improving sales and growing the business. Depending on the cookie, this data can be stored on your server indefinitely or temporarily. Whatever the case, you remain responsible for how and where you keep that data.


You must consider good data governance as mandatory and no more optional. Think of data as a currency, an asset. Just as you wish to manage a business’s financial assets and liabilities; so, do you want to manage critical data assets, information essential to a company like a customer’s contact information, and potential for fines.

Protecting Critical Data Assets

Data governance frameworks help companies grow towards a data-driven organization, the highest maturity level phase. As more business happens online, data quality becomes critical in running these transactions smoothly. Moreover, to give a better customer experience, companies need to transform their capabilities digitally.

Data governance that manages critical data assets helps businesses retain clients and find new buyers. Customers tend to trust companies that are serious about their data quality. They stay away from businesses that pay no attention to their privacy and service needs.

Good Data Governance Reduces Data Liabilities

A solid data governance framework also reduces data liabilities. Failure to take the proper steps to govern and secure data can be a very costly mistake. Companies have gone out of business for their failure to manage customer data appropriately.

Hospitals, for example, cannot operate if they mix up patient data. They must keep every minor detail with great data quality: safe, secure, clean, and sorted. That way, these medical institutions ensure a patient has proper care.

Similarly, educational institutions need be high data quality as well. They use data to mark students and award scholarships, admissions, etc. Even a simple mistake can cause students to fail or notable professors to leave.

Good Data Governance Produces High Quality Data

Good data governance ensures sound data quality through:

  • Uniform and consistent processes You need to be confident that you can trust and understand your data to make more comprehensive and better decisions. Good data governance improves the decision-making process through uniformity and consistency.

That way, you can search for or use the same data set through the same means and get a predictable result.

  • Scalability: Growing businesses face increasing data volumes, variety, and speed. Good data governance helps manage these through clear rules for changing data and processes.
  • Data reuse: Data governance guides data reuse by reducing the number of data transformations or additions. These data governance best practices keep data quality high by standardizing customer or product entities and parameters and reducing the chance of duplicates.
  • Control Mechanisms Business needs to use data governance’s control mechanisms to optimize data quality and reduce a Master Data Management System’s costs. In MDM, multiple departments and branches in the business share customer information. They need to be on the same page about presenting and using the data and what it means.

 Good data governance standardizes customer data increasing its quality before it reaches the MDM system. Data governance best practices do this by defining desired customer data quality thresholds and establishing reference data to use in customer master records.

As the amount of data in the MDM system increases, the business can deal with that better. Companies spend less time cleaning and structuring the data because of data governance control mechanisms

  • Master Data Management: When you have a solid MDM system, it provides data governance to conduct transparent and clear communication through standardization. You can point to the same customer in a sound MDM system, a significant prerequisite for enterprise-wide data-centric initiatives.
  • Safe and secure external and internal data: Good data governance assigns access to data based on individuals or departments, securing external and internal data from unwarranted use. Should department roles evolve, data governance reviews and monitors privacy policies to see if data access can change. Because data governance guides data quality and its standards, updating data access becomes much more manageable.
  • Successful audits and data profiling: Data governance sets the stage for successful data audits and data profiling giving you confidence in your data quality.

From a good data governance framework, you also know what changes you need to standardize the data better under one single view. Audits and data profiling, through data governance, help you keep up with changing data regulations.

All in all, data governance ensures you use data most efficiently without having to spend a lot of money. It may look like a significant investment since you will want to automate some data governance tasks with tools. The cost savings from data governance comes in reducing the risk for a fine or non-compliance.

 You can show a sound data governance plan is a cost-saving initiative to your company’s executives. Especially with higher-level management support, you can highlight how your company will excel with data governance servicing data assets, reducing liability, and producing data quality.


A successful data governance initiative provides the data quality required for a business to use and trust its critical data assets. You know your data governance initiatives work well when you see complete, consistent, and accurate data.

Often companies take data governance initiatives to solve worthwhile goals and use cases like legacy modernization, credit risk management, using technologies (data warehouses data lakes, business intelligence applications) analytics, and improving business analytics. These data governance reasons often lead to project or department-based initiatives.

However, in taking this approach alone, companies miss the benefits of data governance., Programs must be comprehensive and continuous to do data governance well. When data governance stops at the end of a project, the archived and stored data can cause legal problems, as British Airways learned.

5 Components Of Good Data Governance

Any suitable data governance framework designed for data quality typically manages and coordinates five components:

  • Strategy & Practices: Businesses have different goals, such as adopting new technologies and expanding data usage to present a better customer experience, improving auditing to comply with data privacy regulations, or acquiring and merging with another company to provide better offerings. Data governance strategy and practices guide what people, technologies, and other tools need to do with data to meet these business objectives.
  • Tools: Companies use a variety of data governance tools to maintain the quality of data. These include data editors, linking tools, and mining tools. For example, you may implement an easily usable data cleansing tool to integrate and standardize data from various sources to get the data quality needed for a MDM.
  • Metrics: Data governance metrics quantify the data security and accessibility from data quality changes. An example of data governance metrics may include the difference in the number of reported data usability issues from poor data quality, the time it takes for a salesperson to contact a customer during a campaign, or the number of returned mailings from incorrect addresses.
  • Data Stewardship and Other Roles: Since people create, transform, use, and delete data across the life of company data, companies need to govern those behaviors to make data consistent, updated, secure, and accessible. What people do and do not do to govern data greatly impacts its quality.

Data Governance Roles

 Anyone who works with data needs to be assigned a data governance role. Specific job kinds will vary with the size of the organization and the type of business.

 In general, you will find five data governance roles:

  • Data Governance Managers: Data Governance Managers make decisions around data governance. They design and implement the program, with feedback from those with others who create, own, steward, and use the data.
  • Data Creators: Data Creators input the data into the database system. Examples of data creators include customers, salespeople, representatives, or chatbots.
  • Data Owners: Data owners have legal, explicit, or implicit authority to restrict or make their data accessible. A data creator may or may not be a data owner. For example, a salesperson may create customer contact information, but legally the customer remains in charge of who may or may not use that data.
  • Data Stewards: Data stewards, usually subject matter experts, enforce data governance policies and practices with that data. They make sure their colleagues’ data use meets compliance and accessibility requirements.
  • Data Users: Data users search for, view, transform, or act on some data. Data users need to know what data they need to do business and flag data stewards when the data they can access violates the data owner’s privacy.
  • Processes: Data Governance processes describe activities to meet business data quality requirements. An example of a data governance process includes data cleansing, also known as data scrubbing. Data cleansing identifies, correlate, and removes duplicate information.


Coordinating, initiating, and maintaining the five data governance factors above across the organization can be difficult. Usually, companies start with a baseline on what data governance already exists and how data gets used. Depending on a company’s goals, existing resources, revenue, and timeline depend on what data governance strategy to utilise.

The following describes one approach a company may take. As a first step, the organization defines the roles like stewards and owners of the data. Next, Data Governance Managers define processes including how the data will be backed up, protected, archived, and stored to remain protected against attacks and theft.

Those in data governance roles work together to develop procedures and standards and explain how to use critical data assets. That same team defines audit procedures to ensure legal and organizational compliance.

Once a data strategy is in place, the data governance team forms policies. Managers guide the data governance framework to the data governance they want (centralized, decentralized, or non-invasive),

You can learn more about other data governance best practices and collaborate with colleagues through the Data Governance Professionals Organization, the Data Governance Institute, and the Data Management Association (DAMA).

Data Governance Tools

As we’ve gone over in this article, there are different ways to approach data governance. In our opinion, scalable, open-source tools are the best. You can economically and quickly integrate them with your existing business environment.

You can also opt for a cloud-based solution for more flexibility. Such tools are easy to use and more affordable as well. Plus, they remove the need to make additional storage or computing investments, thus reducing overhead costs.

Search the market and compare available options. The best data governance tools include features that: 

  • Understand and Capture Your Data: The software must perform these actions through profiling, benchmarking, and discovering tools and capabilities. For example, the tool must be able to identify relevant data and send a notification to you. 
  • Control Data: The tool must give you the power and ability to control your data with a mix of reviewing and monitoring features.
  • Improve Data Quality: The tool must improve the quality of your data with data enrichment, data cleansing, and data validation. Additionally, it should be able to sort data out.
  • Empower Users: The software must be able to make users feel empowered by contributing to data-related tasks. Self-service tools can be very beneficial in this regard.
  • Document Data: The tool must be able to document your data to improve its data quality. Plus, it should do it effortlessly.

WinPure Data Governance

WinPure is among the most popular data cleansing and data management software.  The recipient of several awards increases efficiency and ensures the data is accurate. Our customers find our tools very useful, as they offer several benefits for businesses struggling with their data governance.

Moreover, you will find affordable and suitable tools at WinPure for all kinds of businesses. Plus, they can play a critical role in supporting other data governance services towards better data quality. 

If you’re still unsure how to do data governance or why it is vital for the data quality you need, talk to us. We’ll answer all your questions and advise you on the right tool for your business’ data governance needs. 

You can start with a free trial to know more about WinPure and how it can benefit your business. Try our free trial today.

Contact us today and learn more about how our set of tools can benefit your business.

Written by Michelle Knight

Michelle Knight has a background in software testing, a Master's in Library and Information Science from Simmons College, and an Association for Information Science and Technology (ASIST) award. At WinPure, she works as our Product Marketing Specialist and has a knack for explaining complicated data management topics to business people.

Share this Post

Download the 30-Day Free Trial

and improve your data quality with no-code:

  • Data Profiling
  • Data Cleansing & Standardization
  • Data Matching
  • Data Deduplication
  • AI Entity Resolution
  • Address Verification

…. and much more!

"*" indicates required fields

This field is for validation purposes and should be left unchanged.