What is data governance and why is it important?

At a glance

• Data governance is the process of managing data through policies and standards across a company.
• Data governance strategies are vital for stakeholders when responding to risk and making certain decisions.
• Data governance is not the same thing as data management. Data governance refers to how employees use data, while data management refers to processes used for collecting and analyzing data assets.
• UOPX offers an online Bachelor of Science in Data Science degree that teaches students how to analyze, manipulate and process data. Learn more!

The definition of data governance refers to the policies that determine how employees manage information across a company. It combines an organization’s standards on data collection, data quality, storage and ethical use into a unified data governance framework. As the primary data stewards, company stakeholders consult their data governance strategies when making decisions and determining how to respond to risks.

As the international big data market could reach a $273.4 billion valuation by 2026, data governance initiatives will play a growing role, guiding how companies learn from the data assets they collect. This means experts with experience and education in data science and data governance tools will be in demand, expanding the field of data science careers.

Data governance vs. data management

Data governance and data management are similar but unique fields. A data governance framework determines how employees use data. Data management is the actual set of processes — often including data governance tools — that collect and analyze data assets across the organization.

Without data governance, data management would be ineffective. The former outlines the standards for proper data collection, states where data should be stored and why and how data should be used. Data management operates on top of that foundation and includes actions like data analysis, security and integration.

Key elements of a data governance framework

A company’s data governance framework includes several key fields. Together, these fields identify how employees should navigate all data — whether the information is generated by employees or customers. Most data governance frameworks include elements like data quality and consistency, data security and data ownership.

Data quality and consistency

Data governance encourages a high level of data consistency and quality across a company. It streamlines the process by treating all data the same — whether it’s through protection, collection or the actual use of data. This helps minimize data usage errors and protects data quality as employees use and share it.

Consistency is a critical data governance feature for companies of all sizes, particularly in labeling. Employees should use consistent labeling strategies that help identify the data source, use and required security. Universal labeling standards allow employees of different departments to share data. They also reduce misunderstandings when one employee collects data and another uses it.

Compliance and regulatory requirements

One major feature of data governance initiatives is compliance — ensuring that employees use data following protection regulations. These regulations outline acceptable ways that companies can navigate data — from collection through disposal. They also identify ways a company can manage data subject rights and obtain consent from customers during the data collection process.

Regulatory requirements often include the General Data Protection Regulation and the California Consumer Privacy Act, both of which dictate acceptable ways to use personal data.

Data security and data privacy

Many data governance procedures also feature sections on security and privacy. Data governance includes guidelines on how to identify sensitive information within a company’s data catalog and take steps to protect it.

Data security and privacy strategies share a common goal: prevent data loss and misuse. Data governance includes strategies to help prevent data loss, including regular audits on companywide data usage habits. It also includes education that teaches employees how to be good data stewards who use collected data ethically. These same data governance policies set controls on data access so that employees can handle only the data they need to.

Data ownership

First, data governance strategies identify data owners — employees who need access to specific data sets. Data governance also sets limits on which data certain employees can access. IT teams follow these accountability and data stewardship standards when setting data access permissions across a company.

Data ownership limits the data an employee can access. It allows employees to see, use and share the information they need without granting them access to information they won’t use. This data stewardship practice limits overall data sharing and creates transparency and accountability in how each employee uses the information a company stores.

Data governance tools and technologies

Companies use a wide variety of resources to support their data governance framework. Here are some types of data governance tools that companies can use when fulfilling data management processes:

• Data cataloging: Tools that create a single destination for all stored data. They make it easy for employees to find data points, even if they don’t know exactly where to find them. Companies depend on tools like Informatica, Alation and Qlik to securely collect and organize data no matter its original location.
• Data modeling: Tools that help companies better understand the data they collect and add to their data catalog. These resources showcase new insights from information, often in a graphic or infographic format. Companies depend on Lucidchart, Archi, Apache Spark and other data modeling tools to highlight new trends in collected data.
• Data quality management: Data stewardship tools that help company stakeholders maintain quality of their data assets over time. They might offer features in data cleansing, storage or monitoring. Tools like OpenRefine, Talend and Precisely help companies manage and manipulate large data sets in seconds.

These tools help companies better understand the scope and purpose of the data they collect. They can also minimize the possibility of user error when employees access specific pieces of data.