Learn about the key differences between data quality and data governance and how they can work together to improve your data strategy.
Data quality and data governance define different parts of an organization’s data management strategies, but they are not mutually exclusive. Together, they can help your business improve its bottom line by providing better visibility into corporate assets while driving efficiencies and operational improvements that lead to greater business efficiency. This comparison defines both terms, explains their differences, and covers how data quality and data management best practices can be used together.
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What is data management?
Data management is the process of establishing, balancing and maintaining information in an organization. It is intended to ensure that the information is collected, stored, processed and disposed of permanently.
Data governance covers the strategies and processes needed to effectively manage enterprise data for business decision making. It also provides a framework for managing risk associated with businesses in an uncertain regulatory environment.
In short, data governance is the management of all organizational information assets – not just data, but also documents, applications, networks, configurations and metadata.
What is data quality?
Data quality is a measure of how complete, accurate, relevant, timely, consistent and reliable the information is. If the data has all these qualities, it is considered high quality. Businesses with high-quality data can make better decisions about what direction they want to take their company, what strategies they want to implement, and what information they have to be successful.
See: Electronic Data Disposal Policy (TechRepublic Premium)
Any deficiencies in data quality can lead to poor decision making. The higher the quality of your data, the more valuable it is.
What are the main differences between data management and data quality?
The main difference between information management and information quality is that information management focuses on the overall information management activities for people, processes and technology. Data governance applications include designing the right approach to storing data, managing its lifecycle, identifying data that needs to be modified or deleted, appointing someone as a responsible data coordinator, and investing in technology to maintain data governance.
Data governance controls who has access to data, how data is accessed, who analyzes the data, and who reports on the data. On the other hand, data quality focuses on identifying data problems or inconsistencies in individual pieces of data, such as names or addresses, to better address these issues. It also covers designing and implementing certain procedures to ensure that data is accurate, consistent, relevant and complete.
The most important difference is that data quality is data accuracy, but data management is more concerned with how enterprises use data.
How data management and data quality overlap
Data quality is an important part of data management, but it should not be considered a substitute for management. The relationship between data quality and data management is symbiotic; Both are essential to achieving proper enterprise information management.
Without good data quality practices, organizations will struggle to maintain the complete and accurate data they rely on to provide input to other corporate processes. Poorly managed metadata can undermine business intelligence initiatives by introducing inaccuracies into reporting tools. Furthermore, poor data quality makes it difficult to extract insights from raw data.
See: What is the difference between information management and information management? (Republic of Tech)
As such, companies must find an appropriate balance between these two important elements of data management. It is not enough to have one without the other; Organizations must have strong governance practices in place when implementing strong data quality strategies.
How to integrate data quality and data management for your organization
Information quality and management goals are achieved through strategic decisions, operational efforts, continuous monitoring and a willingness to innovate. Take inventory of your organization’s data to understand what you have, where it lives, how it’s accessed, who uses it in which workflow, how often it’s used, and why you need it.
Use this information to determine the most critical data sets to work on first. Next, refine the most critical data sets by defining key performance indicators that will measure progress. Then identify opportunities for automation or efficiency by creating an action plan based on those KPIs. Finally, determine if management policies are applicable and need to be updated or created.
Continue to use machine learning and artificial intelligence tools to improve data accuracy and empower employees to take responsibility for their data. Additionally, monitor any regulations that may affect your organization, such as GDPR, to ensure compliance. And don’t forget about security: security measures help protect against human error and malicious behavior – without them, all other efforts fail.
If data management is not effective, high data quality may not be achieved. Conversely, organizations cannot achieve effective data management if data quality is poor or non-existent. Both must be in place to get the results you want.
