��ɫ��Ƶ

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.

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 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����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 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����and the��, both of which dictate acceptable ways to use personal data.

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.

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.

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

Data governance can support every stage of stakeholder decision-making. 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 to consider when implementing a data governance program:

• Goals and objectives:��Identify specific business outcomes your company wants to achieve by collecting data. Outline how your data governance program will protect those interests as data is collected.
• Establish a data governance 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 your existing data:��Audit how well your company has collected data over time. Determine if you already possess some of the data you need.
• 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 pursuing a career in data, ��ɫ��Ƶ can help you gain the foundational knowledge to work in this field. UOPX offers a��Bachelor of Science in Data Science degree��that teaches students how to analyze, manipulate and process data.

Students learn career-ready skills like machine learning, programming, data mining, statistical analysis and more. These skills can help graduates prepare for a variety of employment opportunities in the data science industry.

This UOPX program prepares graduates with the real-world skills to pursue roles like research scientist, data analyst and business intelligence analyst. While these roles are not exactly equivalent to that of a data scientist, the U.S. Bureau of Labor Statistics lists them as similar occupations that work with data and require many of the same IT skills.

To learn more about this program and how ��ɫ��Ƶ can help you save time and money on your degree, visit��the ��ɫ��Ƶ website.