Data Governance: Definition, Benefits, and Best Practices

By Indeed Editorial Team

Published May 22, 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

If you work in an IT or technical department of an organization or you're a data expert, it's important to understand how to govern data. The concept of handling data is essential to almost every industry. Understanding this concept can help you create an efficient strategy to manage data for an organization. In this article, we explore the idea of data governance, describe how it differs from other similar concepts, highlight the benefits of governing data, explore who governs data, and discuss best practices.

What is data governance?

Data governance is a detailed plan containing every process, strategy, role, and policy that organizations use to manage their data. It's the totality of a company's protocols and standards when collecting, securing, and maintaining data. Governing data is a major aspect of data management that focuses mainly on planning and control. While data management encompasses the entire life cycle of data, the governance of data creates plans to determine who has authority over unique data groups in a company. Examples of unique data are employee contact details, client sales records, and management details.

Similar concepts to data governance

The act of governing data is sometimes mistaken for other similar concepts. Here's a list of similar concepts to governance of data and explanations of how they differ:

Data management

This refers to the processes of managing an organization's complete data life cycle. Data management includes enacting the procedures and policies for governing data. Some other elements involved in data management are data quality, data security, database upkeep, metadata management, and data warehousing.

Master data management

Master data management is a sub-field of data management that monitors and enhances the data of an organization. It provides information on clients, suppliers, and retailers. It gathers different pieces of data from various departments in an organization and combines it into a uniform set. Proper governance of data is critical for good master data management. For example, a model for governing data can help define the model for managing master data, detail the retention regulations for the data, and explain the duties for authoring, curating, and accessing data.

Read more: 10 Characteristics of Big Data and How You Can Use Them

Data stewardship

While governing data enables accurate assignment of data to individuals, data stewardship focuses more on operations that help ensure the accuracy of an organization's data. Data stewardship involves operating data, while governing data focuses on strategies, responsibilities, and policies affecting data. Data stewardship also involves ensuring the consistency of the data in line with the governance strategy, data quality, data compliance, data security, and other data assets.

Read more: Top 20 Big Data Tools: Big Data and Types of Big Data Jobs

Benefits of governing data

Governance of data is an essential part of keeping information secure in an organization. Below are the significant benefits of establishing a robust data governing strategy:

  • Ensured consistency: It establishes principles and standards for all departments within an organization. It can help team members understand data and can improve consistency across teams.

  • Improved compliance: There are specific guidelines for securing data in some industries, such as patient health information. It can include clear plans to manage these requirements, improving overall compliance.

  • Increased transparency: It can improve transparency for all data processes and roles. It establishes clear responsibilities and standards for storing and accessing information, making data more accessible to different team members and stakeholders.

  • Higher-quality data: The standards for governing data define the quality expectations for reliable and accurate data. When each team member understands their role and the principles for securing data safely, it can improve the overall quality of data systems.

  • More effective data management: It improves other data management processes, including the collection and removal of data. It may also help improve the monitoring and tracking of data procedures, making management more effective over time.

  • Reduced costs: It makes the data management process more efficient, which can lower costs for an organization. Companies may create plans to reuse data and procedures, which can reduce expenses.

Who governs data?

Organizations in most industries use data governing processes to manage data about their employees, customers, suppliers, or patients. Here are some typical roles involved in overseeing data:

Chief data officer (CDO)

Some large organizations have a designated CDO who oversees data management, including the strategies for governing data. The CDO is typically an executive who works with other senior managers to create effective plans for an organization. They may manage governance tools, teams, and protocols. They may also devise the initial data control plans, handle the funding for the data team, and review the governance system.

Related: What Does a Data Scientist Do? And How to Become One

Data owner

The data owner is the person responsible for a specific type of data. For example, one data owner may be responsible for customers' contact information in an organization. They review data for accuracy and quality and collaborate with other data owners to resolve issues. Typically, this person is part of a team or department that manages the data. For example, a data owner who oversees financial records typically works in the finance department.

Data steward

A data steward oversees a domain or section of data. They may vote on data strategies and policies, resolve data issues, and collaborate with other team members to ensure functionality across departments and information sets. They're often an IT professional, although, in some companies, they may also have a background in business or finance.

Data operators

Data operators, also called custodians, oversee the technical maintenance of data systems. They work to ensure secure data is accessible to the correct team members. They also perform computer and network administrative duties to secure and sustain data sets within an organization.

Data governing committee

A data governing committee determines data principles and standards. The members often work with the CDO to establish the procedures and protocols for data management. This committee is typically separate from the data governing team and mainly includes executives and leaders.

Data governing team

Some organizations may also have a separate data governing team that performs the governance duties. In other organizations, the stewards, owners, and custodians perform the operational tasks. If a company has a governance team, it may include the following roles:

  • Data governing manager: This person oversees the other governance of data team members.

  • Data architect: A data architect designs strategic solutions and processes for an organization's data systems.

  • Compliance specialist: In specific industries, this individual specializes in regulations to ensure the company's data protocols comply with local guidelines.

  • Data analyst: A governance team may include data analysts who assess data sets to identify trends and make predictions.

  • Data strategist: A data strategist analyzes the organization's data strategies to find areas for improvement.

Read more: What Is a Data Modeller and What Do They Do?

Best practices for governing data

A governance of data plan varies according to an organization's size and type of industry. Below are tips and practices for governing data effectively:

Start small

The process of governing data involves various steps, roles, policies, and procedures. It's essential to separate the work into small, gradual, and systematic parts. Starting small can help an organization develop an efficient strategy for governing data. An excellent way to start is by establishing the relevant stakeholders before delegating any role or responsibility.

Set SMART goals

SMART stands for specific, measurable, actionable, relevant, and timely. Set SMART goals for the team in charge of governing data and other colleagues involved in the project. For example, if a company aims to train all employees in a particular skill, it can use a SMART goal to achieve this by making small, actionable plans. In this instance, it can develop an idea for the different departments to offer workshops or training over a specific time.

Identify clear roles

Succeeding in a governance of data project requires the creation of a clear framework that allows all team members to understand their roles and responsibilities from the start. Management can provide instructions to data owners to ensure that everyone has an understanding of their responsibilities. Another method of identifying clear roles is to organize pre-project training for all team members to get sufficient clarity on their tasks and the project.

Define and use metrics

Using performance indicators is an excellent way to monitor the success of a data governing strategy. An organization can develop measurements to monitor the quantity and quality of data and the estimated duration of a sub-project or an entire project. For example, management can help reduce the time it takes to get a data report and subsequently calculate it to monitor its effectiveness.

Review and revise systems frequently

It's common for data governing processes to evolve with technical and organizational advances. It can be useful to set a timeframe for reviewing and revising the organization's protocols of its data governing strategy. For example, management can arrange a biannual meeting with each team member to discuss the progress of the project and monitor it to see if it remains aligned with the organization's aims.

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