Your Guide to Data Quality Analysis

Data is growing. And it’s growing fast. In fact, some say we will generate three times more data volume in 2023 than we did just four years ago. 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. It’s a big undertaking, which is why it’s important to start with a data quality analysis.‍

‍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 analytics and reporting

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 analytics and reporting
       
• Compliant: the data adheres to the organization’s security and/or governance policies
       
• Mastered: the data is curated, matched, and continuously-updated

‍Analyzing your data’s quality

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

       
• Identify stakeholders: Involve IT, data stewards, and data product owners 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. 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.

‍Get the best version of your data with data products

‍Successful data leaders are implementing a data product strategy that enables them to deliver the high-quality data their organization expects. Implemented through the design and use of data products, a data product strategy treats data like an asset, bringing structure to the ownership, processes, and technology needed to ensure an organization has clean, curated, continuously-updated data.Designed for consumption and ready for use, Tamr’s integrated, turn-key data products combine machine learning optimized for scale and accuracy, a low-code/no-code environment, and integrated data enrichment to streamline operations and improve data quality. With Tamr, you can quickly set up a mastering flow for common business entities, create a data product layer, consolidate and author data, manage mastered entities, and provide data to systems, processes, and users for both operational and analytical use cases. As a result, your organization has the clean, accurate data it needs to drive better, more effective decision-making.