The world has come to rely on data. Data-driven analytics fuel marketing strategies, supply chain operations, and more, and often to impressive results. However, without careful attention to data accuracy, these analytics can steer businesses in the wrong direction.

Just as data analytics can be detrimental if not executed properly, so too can the misapplication of data analysis lead to unintended consequences. This is especially true when it comes to understanding accuracy in data.

What Is Data Accuracy?

Data accuracy is, as its sounds, whether or not given values are correct and consistent. The two most important characteristics of this are form and content, and a data set must be correct in both fields to be accurate.

For example, imagine a database containing information on employees’ birthdays, and one worker’s birthday is January 5th, 1996. U.S. formats would record that as 1/5/1996, but if this employee is European, they may record it as 5/1/1996. This difference could cause the database to incorrectly state that the worker’s birthday is May 1, 1996.

In this example, while the data’s content was correct, its form wasn’t, so it wasn’t accurate in the end. If information is of any use to a company, it must be accurate in both form and content.

Why Is Data Accuracy Important?

While the birthday example may not have significant ramifications, data accuracy can have widespread ripple effects. Consider how some hospitals use AI to predict the best treatment course for cancer patients. If the data this AI analyzes isn’t accurate, it won’t produce reliable predictions, potentially leading to minimally effective or even harmful treatments.

Studies have shown that bad data costs businesses 30% or more of their revenue on average. If companies are making course-changing decisions based on data analytics, their databases must be accurate. As the world comes to rely more heavily on data, this becomes a more pressing concern.

How to Improve Data Accuracy

Before using data to train an algorithm or fuel business decisions, data scientists must ensure accuracy. Thankfully, organizations can take several steps to improve their data accuracy. Here are five of the most important actions.

1. Gather Data From the Right Sources

One of the best ways to improve data accuracy is to start with higher-quality information. Companies should review their internal and external data sources to ensure what they’re gathering is true to life. That includes making sure sensors are working correctly, collecting large enough datasets, and vetting third-party sources.

Some third-party data sources track and publish reported errors, which serves as a useful vetting tool. When getting data from these external sources, businesses should always check these reports to gauge their reliability. Similarly, internal error reports can reveal if one data-gathering process may need adjustment.

2. Ease Data Entry Workloads

Some data is accurate from the source but becomes inaccurate in the data entry process. Errors in entry and organization can taint good information, so organizations must work to eliminate these mistakes. One of the most significant fixes to this issue is easing the manual data entry workload.

If data entry workers have too much on their plate, they can become stressed or tired, leading to mistakes. Delegating the workload more evenly across teams, extending deadlines, or automating some processes can help prevent this stress. Mistakes will drop as a result.

3. Regulate Data Accessibility

Another common cause of data inaccuracy is inconsistencies between departments. If people across multiple teams have access to the same datasets, there will likely be discrepancies in their inputs. Differences in formats and standards between departments could result in duplication or inconsistencies.

Organizations can prevent these errors by regulating who has access to databases. Minimizing database accessibility makes it easier to standardize data entry methods and reduces the likelihood of duplication. This will also make it easier to trace mistakes to their source and improve security.

4. Review and Clean Data

After compiling information into a database, teams must cleanse it before using it in any analytics process. This will remove any errors that earlier steps didn’t prevent. Generally speaking, the data cleansing workflow should follow four basic steps: inspection, cleaning, verifying, and reporting.

In short, that means looking for errors, fixing or removing them (including standardizing formats), double-checking to verify the accuracy, and recording any changes made. That final step is easy to overlook but crucial, as it can reveal any error trends that emerge between data sets.

5. Start Small

While applying these fixes across an entire organization simultaneously may be tempting, that’s not feasible. Instead, teams should work on the accuracy of one database or operation at a time, starting with the most mission-critical data.

As teams slowly refine their databases, they’ll learn which fixes have the most significant impact and how to implement them efficiently. This gradual approach will maximize these improvements’ efficacy and minimize disruptions.

Data Accuracy Is Essential for Effective Analytics

Poor-quality data will lead to unreliable and possibly harmful outcomes. Data teams must pay attention to data accuracy if they hope to produce any meaningful results for their company.

These five steps provide an outline for improving any data operation’s accuracy. With these fixes, teams can ensure they’re working with the highest-quality data, leading to the most effective analytics.

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