Data is only useful if it tells a true story. Inaccurate data can lead to revenue loss, wasted money, incomplete or erroneous decision-making, and low sales. In a 2017 study, BMC discovered an extraction error rate between 8% and 42% in three different tests.
While no company is immune from potential mistakes, there are several things you can do to reduce the risk of using erroneous data.
5 Ways to Increase the Accuracy of Document Data Extraction
- Have a clear end goal
- Choose quality data sources
- Limit the number of extraction tools you use
- Avoid overloading your staff
- Implement accuracy standards
Have a clear end goal
Before you begin extracting data, know your goal. Then look at what factors are most relevant to your analysis of a specific task. Once you’ve determined your goal and identified key elements, set parameters to ensure your data collection is accurate.
Having a goal allows you to break data into categories. Not only will this help you analyse the data better, but it also helps highlight potential errors.
Choose quality data sources
Take the time to find the best data sources. Review internal and external resources to ensure that all incoming data is of the highest quality. If you discover erroneous data, identify where the error came from and what caused it. Something as simple as migration between databases or incorrect values can affect the result.
In the same category, consider that information decay can also contribute to bad data. Make sure email addresses, names, phone numbers, and job titles are all up to date.
Don’t forget to clear out old data regularly. Removing old data or incomplete data will go a long way towards preventing some of the most common errors.
Limit the number of extraction tools you use
There can be too many cooks in the kitchen when it comes to data extraction. Limit the number of people that interact with the data because multiple users entering data in different styles can result in missed and inaccurate data. Additionally, relying on data from numerous systems and databases can cause problems. Human error and even formatting issues between systems can result in incorrect data.
Avoid overloading your staff
People can only perform to the highest standards up to certain limits. Overloading your staff with too many studies, too much data or unreasonable deadlines is a recipe for errors and bad data. Using an automated extraction system, like Acodis, removes much of the risk for human error. Automated systems also help eliminate potential biases and provide results faster. Free up your skilled workers by using automated data extraction software to focus on more important tasks than copying and pasting.
Implement accuracy standards
To maximise accuracy, your company must implement high standards for data entry. Implementing higher standards may require additional training. Accuracy standards should include a bias towards neutrality. Pay attention to issues that lend themselves to an overly positive or negative bias, and analyse accordingly.
Develop standards that outline how employees should input information into the system. Small things from how a canton is abbreviated to whether you write Zug or ZG can affect data.
Quality control in data extraction ensures accuracy in your analysis and protects your organisation from making miscalculated decisions. Ensure that your data extraction software allows manual quality control and applies the learning to all other documents. A system powered by machine learning is usually the best tool.