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Data Governance Overview: Meaning, Importance, Implementation

The author of this article is EPAM Senior Business Analyst, Shuchi Satija.

Shuchi Satija, Senior Business Analyst

What is data governance?

Data governance refers to a set of practices, policies, and standards adopted by an organization to protect its data assets and to ensure that the quality, availability, usability, reliability, and integrity of its data are well maintained.

Why is data governance important?

Traditionally, data governance was important from a compliance and regulatory standpoint. Today, however, businesses rely heavily on data analytics for decision making, so maintaining the consistency, integrity, and quality of data is even more crucial. The success of a business depends on good decision making, and an organization can only make good decisions if it has a comprehensive understanding of its data.

The key reasons why every company should implement data governance are identified below.

Compliance with data privacy laws and regulations

One of the main reasons for implementing data governance is regulatory compliance. Data handling standards and regulations vary from industry to industry and region to region, but some of the common ones are: 

  • HIPAA (Health Insurance Portability and Accountability Act of 1996),  

  • GDPR (General Data Protection Regulation),  

  • PCI DSS (Payment Card Industry Data Security Standard),  

  • SOX (Sarbanes-Oxley Act) etc.  

Any non-compliance with these standards can result in heavy penalties and reputational damage for an organization. 

Improvements in data quality

Data governance defines certain KPI (Key Performance Indicators) and metrics to monitor data quality. Any deviations from the defined values of these KPIs help identify areas where corrective actions need to be taken to improve data quality.  

Common understanding of data across industry 

Data governance ensures that consistent terminology, standards, and definitions are applied across different business units in the organization. It also helps reduce confusion and promotes uniform understanding of data across the industry. 

More clarity, transparency, and better data insights

With proper data management and organization of data in place, it becomes easy to extract meaningful results and generate constructive and useful insights. A robust data governance strategy also provides a clearer, more transparent, view of data assets and helps in making well-informed decisions.

Costs reduction

If data is not well managed, it can lead to misunderstandings among various teams, incorrect data classification, poor data quality and decision making, etc. This can eventually cost an organization dearly in terms of money and reputation.

How do you implement data governance in an organization?

To implement data governance, it is vital to design a thoughtful data governance strategy, which will define how the data will be managed across the organization. Designing an effective strategy involves various steps. Some of the key steps are listed below.

1. Identify the goals and objectives of the organization

Start with a thorough understanding of the organization’s business objectives and strategic goals, and how a data governance program can help in achieving them.

2. Evaluate the current, or AS-IS state of data governance

Analyzing the data management framework, policies, and procedures currently in place will provide a fair idea of the areas that need improvement.

3. Clearly define the goals and scope of the data governance program

Clearly laying out the goals and scope of the program – such as improvement in data security, compliance with regulations, quality of data, etc. – will establish a proper foundation. Identification of critical data assets is also very important.

4. Establish a data governance council

Setting up a data governance committee or team that can oversee the initiative and make the high-level decisions will help steer the program in the right direction. The identification of key stakeholders, such as data owners and stewards, the compliance and legal teams, high level executives, etc., will be very useful since these individuals need to be involved in, or duly informed of, the ongoing data governance work.

5. Define and assign the roles and responsibilities

There are some roles that are key to the data governance program, so it is important that they are clearly defined in your data governance strategy and understood by everyone. Here is a brief overview of these roles:

Data owner

A data owner is a senior-level person within a department who is ultimately accountable for a specific data asset, and oversees the data governance program around that data asset. The owner is responsible for determining and defining who has the right to access the data, reviewing the standards and processes around the data, and is usually the final decision-making authority for a specific asset.

Data steward

Data stewards are the ones who have deeper domain knowledge (they could be SMEs for certain areas) and are responsible for managing the integrity, usability, and quality of data assets. They play a crucial role in defining and promoting the best standards, practices, and rules around data.

Data custodian

This is more of a technical role. A data custodian is mainly responsible for managing data storage and access, and implementing security measures for data protection. This person is also responsible for ensuring that data is maintained, stored, and backed up per the defined rules and policies. 

6. Develop policies and procedures for data governance

Develop and document policies and procedures that clearly define how data will be managed, used, accessed, and secured within the organization. The standards for quality, privacy, security, and retention of data should also be defined.

7. Identify appropriate tools and technologies

Identifying and deploying appropriate tools, such as master data management systems, data governance platforms, data lineage and cataloging tools, etc., is a key element in the overall data governance strategy.

8. Define the metrics for the program

Establish KPI to assess the effectiveness of the program and use the results to refine it.

9. Develop communication and training plans

A plan should be developed for communicating progress to key stakeholders, and keeping them updated on any roadblocks, milestones etc. A training plan should also be created to train the employees of the organization about best practices for handling data, and raising awareness of the practices, policies, and overall importance of the data governance program.

10. Engage in continuous monitoring and review

Continuous review and monitoring of the data governance strategy will help in assessing whether any improvements or amendments are required to adapt to changes in the business requirements or regulatory standards, and to ensure that the program is relevant and effective in meeting the organizational goals and objectives.

What software can be used?

There are a number of data governance products available on the market, including: IBM Cloud Pak for Data, SAP Master Data Governance, Alation, SAS Data Management, OneTrust Privacy and Data Governance Cloud, Atlan, Collibra, etc.

Before choosing any of these products, organizations should start by assessing the current maturity level of their data governance program, and identifying their specific requirements and the features and functionalities that are essential to them.

Ease of implementation and integration of the above-mentioned solutions, compliance with industry standards, and cost are important factors to consider when deciding which product would be the best fit for an organization.

Examples of implementation

One of the use cases in the BFSI (banking, financial services, and insurance) domain is to ensure the accuracy and integrity of the data that is reported as part of regulatory requirements, which requires well-managed data and appropriately tracked lineage.

Other use cases include managing data privacy and quality in the areas of healthcare, e-commerce, retail, etc. With the increase in data and content generated through AI tools, there is going to be a surge in the need for data governance implementation in this area as well.


To manage and leverage data assets in any organization, an effective data governance strategy and framework is crucial. Data governance will aid in the better understanding of organizational data, promoting effective decision making. In addition, fostering a data driven culture enables organizations to mitigate risks, increase accountability, identify opportunities, and promote the overall growth of a company.

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