Your Guide to Data Quality Analysis

Data is growing. And it’s growing fast. In fact, some say we will generate 181 zettabytes of data in 2025, up more than 150% since 2023. 

But as the volume of data increases, many organizations are discovering that the quality of their data is decreasing. And because bad data leads to bad decisions, data leaders are prioritizing the task of improving data quality management. It’s a big undertaking, which is why it’s important to start by assessing your data, the first step in the master data management (MDM) journey. By analyzing the current state of your data, you reveal a clear starting point that helps you to define where you are—and where you want to go, laying a solid, realistic foundation to deliver high-quality, trustworthy data that drives actual business value. 

Good Data vs. Bad Data

When it comes to analyzing the quality of your data, the best place to start is by identifying which data is good—and which is not. Hallmarks of bad data include data that is:

• Incorrect: The data values are simply wrong.
• Incomplete: The data is missing some of its values.
• Inconsistent: The same data values are represented in multiple ways across data sets.
• Non-compliant: The data doesn’t comply with the organization’s security and/or governance policies.
• Siloed: The data lives in a departmental or divisional system and can’t be accessed or used by others for analytical and operational use cases. 

Good data, on the other hand, is exactly the opposite. Characteristics that define good data quality include:

• Accurate: All the data values are correct, up-to-date, and version-controlled.
• Comprehensive: The data fields and columns are complete and enriched with external data. 
• Integrated: The data is accessible by users across the organization for use in analytical and operational use cases. 
• Compliant: The data adheres to the organization’s security and/or governance policies.
• Mastered: The data is curated, matched, and continuously-updated in real time.  

Analyzing Your Data’s Quality

Conducting a data quality analysis is important when it comes to prioritizing your data clean-up efforts and to data quality management. To get started, you should:

• Identify stakeholders: Involve business users, data stewards, and IT in your analysis, as they all have a role to play in the maintenance of your data’s quality. You’ll also want someone from your governance team to participate as well. 
• Define what “good” means: Your data will never be perfect. That’s why it is important for you to define what “good data” means for your organization. Prioritize the attributes that are important for your business, define how you will measure the quality of them, and set up processes to monitor the metrics. 
• Identify your data sources: Data comes from many places, both internal and external. For this reason, it’s essential that you understand and document your data sources. For example, is data manually entered by a departmental analyst? Pulled from an enterprise system? Housed in a series of spreadsheets? Uploaded from a third-party source? If you’re like most organizations, it’s likely all of the above.
• Review your governance and security policies: Governance and security policies dictate who can access which data as well as how you store and protect that data across systems and sources. Conduct an audit to ensure that all of your data is compliant with your security and governance policies.
• Employ the right technology: Ensure you have the right technology in place to help your data remain clean, curated, and consumable for everyone who needs it. AI-native MDM solutions make improving data quality faster, easier, and more efficient, especially when compared with traditional, rules-based solutions. 

Get the Best Version of Your Data with AI-native MDM

Successful data leaders recognize that to improve data quality, they must embark on an MDM journey. By following an MDM journey, organizations can assess and improve the quality of their data so they can use it to confidently answer questions like “How many customers do I have?”; “Which providers deliver the specific set of services I need?”; and “What markets have the highest potential for revenue growth?” And because these companies analyze their data’s quality and improve it before they operationalize it, they see higher degrees of success. 

A key enabler of the journey is AI-native MDM, which combines AI's efficiency and scalability with business context and human expertise to provide the advanced capabilities you need to deliver the best version of your data. Investing in an AI-native MDM solution helps organizations to improve their data quality so they can deliver the trustworthy data everyone across the organization needs to unlock insights, streamline operations, and drive innovation that accelerates business success. 

As businesses continue to rely on data to drive decisions, ensuring its quality is essential. Conducting a data quality analysis helps to uncover hidden issues so you know what data to improve and then review with end users. Prioritizing data quality instills confidence that your data is clean, curated, and ready to operationalize so that your users can use it to engage customers with exceptional experiences, uncover new opportunities to grow revenue, and streamline operations.