7 Data Quality Metrics You Should Measure (but Don’t)

High-quality data is a critical component of any successful data product strategy. But how do you judge the quality of your data? The key is to establish key data quality metrics and KPIs that you can measure over time.

Tracking and recording accuracy, completeness, and consistency of data helps organizations identify issues early. It also enables them to prioritize data quality improvements to maximize value. 

The basic premise is that what gets measured gets managed. By establishing data quality KPIs and metrics, you can identify where issues exist and take action to quickly resolve them. But where should you begin? Below is a list of data quality metric examples that every organization should track.

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7 Data Quality Metrics Every Organization Should Measure

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1. Accuracy

Measuring data accuracy is an obvious first place to start. After all, if your data is incorrect, your analysis and your decisions will be wrong, too. To measure accuracy, you want to gauge how closely your data values depict reality. Measuring against a known source of correct information is a good place to start. If multiple known sources exist, select the one that is most similar to your data. 

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2. Completeness

Determining the completeness of your data starts with understanding if it’s comprehensive - or not. A simple place to start is by looking at whether or not blank fields exist in your data. If blank fields exist, how prevalent are they? It’s also important to remember that not all fields are created equal. Required or mandatory fields hold higher weight than those that are “nice to have.” When evaluating completeness, start by evaluating how many of your mandatory fields are missing information. From there, you can move on to optional fields. 

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3. Consistency

Evaluating consistency involves looking at records from different sources and determining if the values are identical, both in terms of meaning as well as structure and format. Discrepancies across sources indicate that your data is not as consistent as it could be, and may require cleansing. 

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4. Timeliness

When assessing timeliness, you’re answering the question “how up-to-date is my data?” When establishing a data quality metric related to timeliness, it’s important to remember that not all data is - or can be - available in real-time. In some cases, “timely” may mean that the data was refreshed within the past 24 hours - and that’s ok. Taking these nuances into account is key to setting your benchmarks accordingly. 

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5. Uniqueness

Analyzing uniqueness involves understanding where - and how much - duplicate data exists. Multiple, identical - or even similar - records skew insights, leading to poor decisions or worse, reputational harm or non-compliance with regulations. Getting ahead of deduplication issues is key, as redundant data negatively impacts both analytical and operational use cases. 

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6. Validity

When measuring validity, you’re looking for how well your data adheres to established standards. If, for example, data is entered in the wrong format, it could be deemed unusable and be excluded from a report or analysis, which, like other data quality issues, can distort results. 

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7. Integrity

To ensure data is of high quality, you must also ensure that it has the appropriate safeguards and governance in place to prevent improper use and/or modification. You should also assess how well the data complies with relevant regulations, including data protection laws such as GDPR or CCPA as well as standards specific to your industry. 

Maintaining data quality is an ongoing process that requires continuous monitoring, proactive measures, and adherence to best practices to ensure that your data remains accurate, reliable, and trustworthy. By establishing data quality metrics and KPIs and keeping a constant eye on them, you can more easily spot anomalies and correct them before they become a pervasive problem for your organization. 

Click here to learn more about how Tamr can help improve your organization's data quality, or request a demo request a demo. 

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