What Is Data Accuracy? Definition, Examples, and Best Practices
If you care about whether your business succeeds or fails, you should care about data accuracy. Data accuracy is important because it has an impact on your company's bottom line. Unfortunately, that impact often goes undetected—until it’s too late.
Say your business uses data for operational purposes, and your data is inaccurate. You could upset an entire segment of customers whose names you got wrong in an email—damaging your reputation and losing their trust. Or, you could lose profitable sales because you inadvertently listed an in-demand item as “out of stock” on your ecommerce website.
If your business uses data for decision-making purposes, on the other hand, and your data is inaccurate, it could have profound consequences. As an example, imagine using inaccurate market data to make a business decision about where to open your next location, only to find out that the region you chose has a median income too low to afford your products or services.
Now that you know why data accuracy matters, let’s dive into exactly what it means. In this blog post, you’ll find a definition, three examples of inaccurate data, and four methods for measuring data accuracy.
Introduction to Data Quality
There are many varying definitions of “data quality”, with some definitions defining it with terms such as “accurate data” or “timeliness”, but we take a more robust approach to defining data quality to help you inform your data management strategy for you toa void all possible data quality issues.
What is data accuracy?
Data accuracy is one of ten dimensions of data quality, and one of three dimensions that influence data integrity. Data is considered accurate if it describes the real world. Ask yourself: Do the entities actually exist in your data collection, do they have the attributes you describe in your data model, and do events occur at the times and with the attributes you claim? Accuracy is fractal, so it’s important to examine each level of abstraction.
Examples of inaccurate data
Imagine you’re a lead analytics engineer at Rainforest, an ecommerce company that sells hydroponic aquariums to high-end restaurants. Your data would be considered “bad data” / ”inaccurate data” if the number of aquariums shipped from the warehouse did not match the actual number sold as reported by your sales team, due to accidental manual data entry in the data source. The same would be true if the geographies assigned to each sales rep were not correct, or the dollar amount of a specific sale was off by a significant amount. These are but two examples of data inaccuracy.
Note: as more companies move toward automation in their “big data” strategy, poor data quality can cause negative downstream impacts for all uses of data from artificial intelligence to data analytics.
How do you measure data accuracy?
To test your any data quality dimension, you must measure, track, and create validation for a relevant data quality metric. In the case of data accuracy, you can measure the degree to which your data matches against a reference set (e.g. your data sources), corroborates with other data, passes rules and thresholds that classify data errors, or can be verified by humans. As part of the entire set of 10 data quality dimensions, this dimension is certainly interlinked with others; for example, in the earlier Rainforest scenario, the data might have been mismatched due to an issue with stale data related to pipeline run errors, or a partial load leading to data incompleteness.
How to ensure data accuracy
One way to ensure data accuracy is through anomaly detection, sometimes called outlier analysis, which helps you to identify unexpected values or events in a data set.
Using the example of a sale that was reported inaccurately, anomaly detection software would notify you instantly if that value was outside of the normal range. The software knows it’s outside of the normal range because its machine learning model learns from your historical metadata.
Here’s how anomaly detection helps Andrew Mackenzie, Business Intelligence Architect at Appcues, perform his role:
“The important thing is that when things break, I know immediately—and I can usually fix them before any of my stakeholders find out.”
In other words, you can say goodbye to the dreaded WTF message from your stakeholders. In that way, automated, real-time anomaly detection is like a friend who’s always looking out for you.
To take anomaly detection for a spin and put an end to poor data quality, sign up for Metaplane’s free-forever plan or test our most advanced features with a 14-day free trial. Implementation takes under 30 minutes.