Data Governance Best Practices

Your data assets are vital to the success of your business. Are your data management practices as good as they can be?

This article explores the nature of data governance and explains the key data governance best practices to keep in mind when developing and implementing a data management program.

What Is Data Governance?

The Data Governance Institute defines data governance as follows:

“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.”

According to the Data Management Association, data governance comprises 10 topics:

  1. Data architecture
  2. Data modeling and design
  3. Data storage and operations
  4. Data security
  5. Data integration and interoperability
  6. Documents and content
  7. Reference and master data
  8. Data warehousing and business intelligence
  9. Metadata
  10. Data quality

Data governance is the full range of processes and practices that an enterprise uses to manage data flows and handle information. A data governance structure provides the framework that delineates the roles, responsibilities and processes that govern an organization’s data. It should establish standards for data management, align with business objectives, and function well within the company’s business model. Data governance is practical and actionable. When done properly, it involves everyone in the organization.

How Data Governance Affects an Organization’s Health and Profitability

Data governance supports the integrity and quality of your data, which leads to multiple business benefits. In particular, an effective data governance strategy can help your organization:

  • Ensure information quality, which improves data analytics and decision-making
  • Better identify and minimize IT risks related to data, such as the risk of data breaches
  • More easily implement the protections required by regulatory agencies and laws such as HIPAA
  • Enhance information exchange and operational efficiency

Roles and Responsibilities on a Data Governance Team

Some organizations have a dedicated data governance team, while others have employees assume the additional responsibilities in addition to their normal duties. There’s a common misconception that data governance is only an IT job. The reality is, while IT teams are responsible for providing solutions and developing infrastructure services, other team members are just as vital; for example, they provide guidance on data governance policies and rules.

The key data governance roles that need to be filled include:

  • Data steward — This is an operational duty that focuses on implementing and coordinating policies and procedures. Data stewards manage corporate data projects, make data-related decisions, issue recommendations and develop relevant policies.
  • Data governance council — This team is responsible for setting up the data governance program, measuring success and gathering metrics.
  • Data stakeholders — These are the people who own and use specific data assets. They usually include individuals and teams in the human resources, IT, risk management, compliance and legal departments. Their insights and needs should be considered in decisions about policies, procedures, business rules and technology approaches.

Data Governance Best Practices

Slips, lapses and mistakes on the part of data entry team members add up fast. A company with an error rate of even 1% will face serious data problems. Automating all you can mitigates the risk of human error.

  • Establish a data governance structure appropriate to your organization’s size and needs.

You can’t simply adopt a prebuilt data governance model; you will need to customize it to fit your organization’s size and needs.

  • Keep in mind all three components of effective data governance — people, processes and technology.

Get the right people to handle your data, and give your them the authority to implement the most effective practices for your organization. Invest in the technology necessary to manage your critical data. Don’t let front-end prices prevent you from getting the software you need to secure your data.

  • Establish metrics and regularly assess your work against those metrics.

The only way to measure your progress is to identify markers and checkpoints and then rigorously evaluate your performance against them.

  • Start small but think big. Keep in mind that data governance is an ongoing process, not a one-time project.

Business owners can be tempted to read a blog on implementing data governance structures and then launch a project to clean up all their data. That’s a great start, but you can’t end it there. Data must be acquired, stored, secured, cleaned and surfaced regularly in order to be useful business intelligence.

  • Expect an increasing volume of data.

The International Data Corporation predicts that data will grow 61% to 175 zettabytes worldwide by 2025. Make sure your data management team is well-equipped to deal with the increasing amount of information.

  • Expect more protection regulations to govern data assets.

Europe’s General Data Protection Regulation (GDPR) affects how companies do business and how people think about data protection. Expect to see more and more government and agency regulations for data management, and ensure your data governance team is up to date on the most recent regulations and practices.

Implementing data governance

To implement an effective data governance strategy, follow the data governance best practices laid out here. Begin by hiring the right team members and staying on top of changes in regulations, technology and data management practices. Proper governance of your critical data is essential for making the best business decisions.

Product Evangelist at Netwrix Corporation, writer, and presenter. Ryan specializes in evangelizing cybersecurity and promoting the importance of visibility into IT changes and data access. As an author, Ryan focuses on IT security trends, surveys, and industry insights.