We live in a time when we have more data available to us than we will need most of the time. Just about everything can be measured and stored, and the information about that can be used in some way.

Data accuracy comes into play when you want to use the data gathered in a way that may benefit you or your business. One of these benefits is potentially lowering information security risk. On the other hand, inconsistency and data accuracy issues could cost your business money as you may need to employ someone to fix the data quality issues. More than that, you could make judgment calls and business decisions based on poor data quality and analytics that you might not have made if you had used a more accurate data source. 

What Is Data Accuracy?

Before we speak about data accuracy, we need to define ‘data.’ Data is information, facts, and statistics that are gathered, while security metrics are data that can be collected and analyzed. This information is then analyzed and used. For example, a company can use specific data to make decisions about its products or services, how to interact with current clients, and how to convert potential clients into paying customers.

Data accuracy refers to the quality of the data a company has collected and used to base its business decisions on. For data to be accurate, it needs to reflect reality closely. Making business decisions based on inaccurate data could cost a company time, resources, and money.

For example, a restaurant relies on accurate data to project potential sales and order perishable food stock accordingly. Working on inaccurate data could leave them with far more mussels than they can sell before they go bad.

What Is the Difference Between Data Accuracy and Data Integrity?

While closely related, data accuracy and integrity are not the same things. Data integrity refers to how well data stays consistent, accurate, and relevant over time. This is crucial if a company were to use

Consider for a moment a company that collected data on their customers’ purchasing habits two years ago. It spent a large part of its marketing budget on getting the most up-to-date and accurate data, analyzed it, and used it to create a few successful marketing campaigns. As a result, sales increased, as did the company’s profits.

Today that same data is out of date and inaccurate. It likely doesn’t reflect the purchasing habits of the company’s current customers. Any marketing efforts based on this outdated data may reach and convert a handful of older customers. At the same time, it could potentially alienate newer customers with marketing messages that speak to an entirely different buyer persona.

For example, an events company may use information based on its customers’ marital status for specific marketing campaigns. It may advertise events where single customers could meet and socialize with other singles in their area to unmarried customers. This campaign could do well when directed to single people in a specific area. The same campaign sent to married individuals probably won’t.

This company must maintain data integrity by having the most accurate and up-to-date information on its customers’ marital status.

What Determines Data Accuracy?

Data accuracy is determined by how accurately data has been captured, how well it is stored, and how well it is managed and maintained once stored. You want high-quality data. Cybersecurity audits review IT infrastructure, helping to determine the quality of the storage systems. This keeps stored data safe, ensuring higher quality data. 

To maintain data accuracy, the data should be captured correctly. This applies when data is captured manually (an employee or the customer personally enters their personal data) or when data capture occurs automatically (your business uses software or programs to harvest customer data).

When capturing data, care should be taken not to include duplicate data or enter the data in incorrect fields. Data capturing should also be as thorough as possible to avoid incomplete data.

Any outdated or irrelevant data should be updated or removed when maintaining data. This will ensure that the data used to base business decisions on is clear, concise, and trustworthy. In addition, data should be accessible and, ideally, sharable amongst different programs and team members.

How Do You Measure Data Accuracy and Can Total Accuracy Be Achieved?

It may be unrealistic to expect to achieve total data accuracy. However, it isn’t entirely out of reach.

When measuring data accuracy, you need to look at the percentage of errors compared to the total number of records or data elements. A lower percentage of errors concerning the total number of entries means higher quality (and more accurate) data.

An excellent way to measure data accuracy is to compare data to a reference data set. For example, suppose you wanted to check whether your customers’ zip codes are correct. You could compare them to those listed in the United States Postal Service’s address registry.

Other data might be trickier to measure. For example, measuring whether data on your customers’ preferred products is accurate cannot be compared to a data set that already exists. In this case, you could benefit from analyzing their buying habits or conducting a survey to use as a reference and confirmation.

How Can You Improve Your Data Accuracy?

Since data accuracy is (or should be) a central part of your business, you must improve your data accuracy as much as possible. Here are our best practices to improve data accuracy for your business.

Accurate capturing.

Capturing data accurately is the first obvious step in improving your data accuracy. Data used to be something that the IT guys worked with. Today, data is captured and used by various employees. Unfortunately, often employees aren’t aware of the importance of the data they capture, modify, and use.

Even though it is often up to employees to perform data collection tasks, it is up to the company to ensure data entry accuracy. Data awareness training could educate employees on the importance of entering accurate data and maintaining the accuracy of existing data. In addition, it could be helpful to have clear standards and data quality protocols. This will ensure higher accuracy when capturing and maintaining data.

Be intentional with your data.

Every interaction with a customer or potential customer is an opportunity to gather data. Unfortunately, some companies jump on this opportunity and end up with far more data than they can handle.

Too much data is not necessarily a good thing. This is especially true if you cannot store, analyze, and use the data in a way that will benefit you or your business. In fact, the more data you gather, the higher the chances for data inaccuracies to creep in.

Having an overwhelming amount of data also means you or your employees will likely be unable to analyze the data timeously. Data analysts need sufficient time to perform their data analysis duties to draw accurate conclusions. If the amount of data is too much, a company could miss some benefits or opportunities it could have harnessed.

Data that isn’t being used for specific purposes, like creating specific marketing campaigns or improving a product, service, or experience, is unnecessary. Be careful not to spend resources on data just to have it ‘in case.’

Use proper programs.

An Excel spreadsheet might work if you have very little data, but this method of collecting, storing, and analyzing data is outdated and impractical. Using a spreadsheet or similar method for handling your data is not only time-consuming but can negatively affect your data accuracy. These programs allow errors to slip in easily since many processes are performed manually.

Newer, automated programs for data capturing, data storing, and data analytics make it more efficient and accurate to gather, store, analyze, and use data. Some of these programs often use machine learning and multiple sources to improve a company’s data accuracy and integrity.

What Does Data Accuracy Mean for Your Research and Business?

Inaccurate data can cost your company money. Bad data can cost your company between 15 and 30 percent of its revenue. Annually, businesses in the US lose about $3.1 trillion because of poor data.

If you have bad data, you may make mistakes or make decisions that could cost your business money.

If your data is inaccurate, it needs to be fixed. To fix your data, you need to employ someone to identify incorrect data and correct it. Ultimately, this will affect your return on investment since you need to pay someone to fix unnecessary mistakes.

When you have accurate data, your employees can trust the information they have and the conclusions they draw from analyzing it. Decisions can be made confidently, knowing that the data these decisions are based on is reliable and closely reflects reality.

How Can RiskRecon Help Me?

RiskRecon, a Mastercard company, is dedicated to providing its customers with the highest accuracy possible with an asset attribution independently certified to 99.1% accuracy. You can view the RiskRecon demo here.

Data accuracy is a crucial part of a business. Using inaccurate data can cost a company it's time, resources, and money – both in added expenses and lost income.

Ensuring your data is correct when captured is the first step in improving your data accuracy. Maintaining that accuracy involves updating old data and removing irrelevant data.

Keeping and using just the necessary data while using proper programs or software will ensure that you and your employees aren’t overwhelmed by the data and make the best decisions based on accurate and relevant information.