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

Without 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.

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 of these frameworks include elements like data quality and consistency, data security and data ownership.

A high level of data consistency and quality is encouraged across a company. A good strategy 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.

This is applicable 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.

One major feature of what is data governance involves 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 EU's General Data Protection Regulation��and the��, both of which dictate acceptable ways to use personal data.

Many governance procedures also feature sections on security and privacy. This 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. This 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 policies set controls on data access so that employees can handle only the data they need to.

What is data governance's way of identifying data owners? This includes employees who need access to specific data sets. Policies 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.

Executives make fundamental decisions based on data. They might create new products, hire employees or invest funding based on the data they collect. This is an important part of��business intelligence.��It plays a role in creating��analysis strategies��that help executives make data-driven decisions. When company stakeholders access data, governance processes ensure that information in the data catalog is accurate, reliable and consistent.

Every stage of stakeholder decision-making can be included. It provides executives with the right data at the right time and protects that data while in use. It also allows for easy data sharing between different executives. Most importantly, it supports data quality — helping company leaders make informed decisions during the business intelligence and analytics process.

There are many factors to consider when planning a governance program. For example, companies should plan out a strategy for data collection and storage. They should also determine how the information will be disposed of after use.

Here are some other priorities for businesses to consider when implementing a program:

• Goals and objectives:��Identify specific business outcomes the company wants to achieve by collecting data. Outline how a data governance program will protect those interests as data is collected.
• Establish a program structure:��Assign roles and reporting for the data collection and usage process. A company’s primary data stewards should understand their responsibilities in gathering, analyzing and securing data.
• Identify existing data:��Audit how well the company has collected data over time. Determine if the company already possesses some of the data that is needed.
• Define acceptable data usage policies:��Create policies that ensure data is only used in ethical ways. Make sure employees understand these policies and the consequences if data is used incorrectly or shared outside of appropriate access permissions.

Data governance doesn’t end after data collection. Companies should monitor how employees use data to find opportunities for improvement. This often means collecting feedback from employees, particularly feedback that identifies how well they can manage data using the current data governance policies.

If you’re interested in learning more about what is data governance and perhaps pursuing a career in data, ������Ƶ offers a��Bachelor of Science in Data Science.����

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