Is your data error-free? Data accuracy is whether or not data values are correct and consistent. In data management, data accuracy is the first and most critical component of the data quality framework.
Here’s an example: in the US, dates follow the MM/DD/YYYY format, but in the EU and other countries, they use DD/MM/YYYY. So, if the date 10/11/2022 is in the data, should it be read as October 11th or as the 10th of November? This is the classic meaning vs form problem that can negatively affect data accuracy.
Data accuracy is crucial: without it, how can businesses make key decisions? How can they understand their market position and their competitors? Data accuracy provides critical insights, analytics, and intelligence to the decision-makers in your organization.
How to Ensure Data Accuracy
1. Assign Permissions to Change Data
One common cause of data inaccuracy is inconsistencies between departments. If people in different teams have access to the same datasets, they’re likely to do different things with that data—leading to discrepancies or duplication. The first step to ensure your data’s accuracy is to establish procedures for who is allowed to view, organize, and change data. Doing so limits the chances of incorrect edits since the people who have access to the data have been trained in handling it. Should a mistake happen, permissions make it easier to identify and correct the error.
2. Create a Centralized Database
While you want to limit who can view and change the data within your organization, it’s best to store and organize the data in the same place. This way, if an employee needs the data, he or she knows exactly where to look and—provided he or she has been granted access to it—view the data.
3. Standardize and Simplify the Entry Process
Having a defined structure as to how data should be entered ensures consistency and minimize errors, but it’s also worth exploring ways to make the data process easier. Restricting the number of options within a particular data field limits errors and standardizes information. Would a drop-down list be easier than manually typing in data? If so, it would probably be worth implementing into the data entry process.
Speaking of implementation, automation is your friend, so look for ways to include it in the process wherever possible. For example, automation can help data matching and duplicate removal processes. 
While we've been entering data manually for years, automation eases the process.
Another reason data becomes inaccurate is that it isn’t properly “cleaned.” “Dirty data” is one of the main sources of low-quality data and, by extension, poor data analysis. Regularly performing data audits to see what needs updating will also help you and your teams identify ways to improve the data entry process. As if this weren’t enough, regular data audits help minimize security and compliance issues.
5. Start Small
Undeniably, you probably have the temptation to implement these changes across your entire organization all at once, but our advice would be to resist this urge. Not only would such a change be jarring to your employees, but a wholesale shift to a completely new model of data storage is likely to bring errors with it. Which undermines the goal of data accuracy, doesn’t it?
Instead, teams should work on the accuracy of one database or operation at a time, starting with the most important data.
Turn to Copy Systems for Your Data Accuracy Needs
Data accuracy is critical to your business’ success and your decision-making. After all, how can you form a coherent, accurate strategy if you don’t have the right data? Copy Systems has the data accuracy systems you need to ensure your decision-making is sound. If you want to know more about the solutions we can offer you, contact us!
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