Data Quality Metrics: How to Measure the Accuracy of Your Data

High-quality data is a powerful asset for any business to have, but collecting and maintaining accurate data can be a challenge for any company. Data inaccuracies can come in the form of duplicated data, misspelled words, or inconsistencies to name but a few but this is by no means a comprehensive list.

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Poor-quality data can affect businesses as it can skew insights, leading to bad decision-making. This can lead to lost revenue (up to  $12.9 million every year), wasted time, and low customer confidence. However, poor-quality data doesn’t have to be a problem if there are measures in place to reduce risks. When good data quality metrics are in place, businesses can ensure the quality of data.

What Are Data Quality Metrics?

Data quality metrics are indicators that help businesses determine the quality of their data in terms of its accuracy, completeness, timeliness, consistency, validity, and consistency. In short, they help businesses check that the data they have meets their needs. 

These metrics can highlight and separate high-quality data from low-quality data providing businesses with more reliable and useful information.

Why Is Data Accuracy Important?

Accurate data provides a solid foundation on which businesses can base their decisions. High-quality data provides more meaningful insights into customer behaviors and needs, which in turn gives companies a competitive advantage due to a better understanding of their customers and business performance.

In addition, high-quality data will improve the effectiveness of marketing operations, leading to the overall benefits of increased customer satisfaction, business practices, and profits.

If your business isn't measuring data accuracy, it’s vital it starts as soon as possible.

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How Can Data Quality Be Improved and Maintained?

Implementing a framework with defined metrics can help business leaders measure data accuracy on an ongoing basis. A program that will continuously profile, verify, and validate data as well as assess data sources is a great place to start.

Data becomes more relevant to a business when it aligns with the company's ethos and objectives. When designing data quality metrics, it's crucial to keep this in mind. Equally important is the integration of data governance with data quality.

This is the process of defining who will use data, and how that data will be gathered and managed. It’s important to avoid data governance mistakes in the early phase of implementing data quality metrics.

To this effect, you’ll want to be answering the following questions:

• What data is important?

• How will the data be used?

• Who will be accessing and using the data?

• How will we be ensuring that the data is kept secure at all times?

• What metrics should be used to track data quality and accuracy?

Using Data Intelligence to Define Key Data Quality Metrics

Data intelligence helps organizations answer questions about data. The answers can, in turn, help deliver reliable, quality data. The fundamentals of data intelligence can form the basis of data quality metrics frameworks by asking and answering questions such as:

Who will use the data or access it?

Where did the data come from?

When was the data collected and when will it be used?

Why do we need the data?

How can we use the data?

Once businesses understand the data that will be useful, they can implement data quality metrics.

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Which Data Metrics Should Business Leaders Consider?

For data to be high-quality and of benefit, it should be accurate, complete, and consistent amongst other things. Here are some of the key metrics to put in place when improving data accuracy.

1. Completeness

Useful data must paint a complete picture. This means all required data fields should be completed, whether that’s by a customer at an online checkout or a data-entry clerk in an office. Monitoring the number of empty or incomplete fields and calculating it as a percentage can provide insight into how complete the data is. Moving forward, utilizing required fields can reduce the problem.

2. Accuracy

Businesses must review how accurate the data that has been entered is, and ask themselves: Does it represent reality? Is data reliable or should it be replaced? This can be worked out by measuring the ratio of data to errors and reduced by formatting fields.

Inaccurate data leads to bad decisions and wasted time and effort. For example, if your website features a data capture element that doesn't include any rules, you could end up with inaccurate data that isn’t useful. 

Imagine your business going to the trouble of finding the cheapest sg domain for your Singapore-based business and building a website that hasn’t been designed with effective data capture tools. To ensure maximum data accuracy, you always need a data capture feature.

3. Consistency 

Data can be stored in different formats across larger organizations. It’s important that the same examples of data be consistent across all storage formats. Consistency does not necessarily mean accuracy, however. If one inaccuracy is spotted, the error will be replicated across all consistent data formats and therefore more easily rectified. 

4. Integrity

Data integrity means ensuring that where data connects with or has relationships with other pieces of data in the system these are true to one another. In simpler terms, if a customer receives a full refund for a returned product, the refunded amount must equal the amount the customer initially paid.

Data integrity also means checking that when data is transferred or reformatted, it is done so in a way that does not affect the original data.

5. Timeliness

Out-of-date information can lose its reliability. Depending on what the data is concerning, and the industry that’s using it, data can lose its usefulness at different times. Old data can affect data quality in many ways. For instance, having out-of-date email addresses stored for some customers means that email marketing or newsletters will not reach them.

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Analytical data that has become out-of-date can lead to poor business decisions. A streaming data platform can provide real-time analytics, giving your business access to data insights much quicker than ever before.

6. Validity

The validity of data means how well it conforms to syntax, such a format, type, or range. It can be checked by formatting fields. For example, if a monetary value is required, the field can be set to $0.00 to reduce the risk of a data-entry error. 

7. Relevance

Businesses collect a lot of data. To ensure efficiency, it’s important that the right data reaches the right people. For example, the marketing team may use personal customer information, but business leaders or stakeholders will want to see more performance-based analytics such as quarterly buying trends. 

Managing datasets is key to making sure that the right people get the right access and data governance software can help achieve it. But what is data governance, exactly? Data governance refers to the practice of managing data based on specific internal requirements and standards that help guarantee its usability, availability, accessibility, and security.

8. Usability

If the format or reporting of data isn’t user-friendly, even quality data can become unusable. To be useable, data must be easy to extract and understand. Using an analytics data warehouse can help businesses store data from different sources in a business-friendly way making for easier reporting and insights.

9. Duplicates

Duplicates are a no-no in data accuracy. Data should be unique in the sense that it is not duplicated as this is redundant data. Duplicated data can skew results and lead to ill-informed decisions. By measuring the percentage of duplicated data, analysts or data quality managers can determine how much data is redundant.

How Can Data Quality Metrics Be Implemented?

The first thing businesses must do is embrace a culture of data quality. This involves conducting a data assessment to determine what data to measure and how to maintain quality and consistency moving forward. Because not all data has the same value, it’s important not to waste resources on data that isn’t relevant.

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A data assessment approach would look at things such as which parts of the data need checking and what measurable parameters can be used. This should include high and low-quality determiners. Then, once the metrics are in place, review the results to make improvements. Example metrics are:

• Ratio of data to errors

• Number of empty or incomplete values

• Number of data conversion errors

• Quantity of ‘dark’ or unusable data

• Email bounce rates due to incorrect or out-of-date data

• The time it takes to get value from data

• The cost of storing data VS the value gained from it

Once the data quality standards are set and strict controls are in place, data quality metrics can be automated, saving businesses time and reducing the risk of further errors. By running automated checks, data quality can be checked periodically to ensure accuracy moving forward.

If your company uses a managed service provider business, then data storage can be managed externally. In order to use an external company, businesses must first understand the data they have and the results they want.

Final Thoughts

Measuring data quality is becoming more relevant to businesses as data growth continues at a rapid rate. Data is one of the most valuable assets a business has since it presents insights that can drive powerful decision-making.  

When data is poor quality, however, businesses might make poor decisions and miss out on opportunities that could help boost revenue. Poor-quality data also wastes the time of your team and can leave customers disgruntled, giving you a bad reputation. 

By taking measures to ensure the best quality data possible, businesses are protecting themselves and providing themselves with a strong foundation for growth. 

Don’t wait any longer – start measuring the accuracy of your data today.