What is Data Accuracy?
Data Accuracy is the measure of data quality based on factors like accuracy, completeness, and reliability. Data Accuracy is the first and foremost critical part of the data quality framework.
Importance of Data accuracy
It is significant to improve data entry accuracy because inaccurate data could result in poor business decisions that increase unwanted operational costs. Organizations make business decisions based on data quality that impact the company’s cash flow.
How to improve data entry accuracy? Unfortunately, this question will ruin your mind if you are dealing with a large volume of data.
While dealing with large volumes of data on a day-by-day basis, how can you ensure data entry accuracy? Here are five tips to How to improve data entry accuracy while capturing data:
How to improve data accuracy – 5 Important Suggestions
Inaccurate data entry can affect your business decision-making. That’s why you need to focus on data quality to ensure accuracy. You can follow these tips to improve data accuracy.
Automation reduces, and in some instances, even eliminates the challenges mentioned above, but it must get implemented properly.
Following that, here are some suggestions to ensure data entry accuracy:
1. Concentrate on Data That Is Useful
Accurate data, relevance, readability, accessibility, completeness, and timeliness are the significant factors in evaluating business data quality. Among these factors, accurate data gets frequently regarded as the most crucial of these characteristics to attain.
Nonetheless, according to one study, American businesses now feel that 32 percent of their data is inaccurate, up from 25 percent in 2014. In other words, in the vast majority of cases, the problem is escalating.
Data experts will have their job cut out to solve the problem and reach a more acceptable data entry accuracy rate.
Data values, for example, must reflect “the right value” while still representing unambiguous forms. Moreover, businesses will face the well-known data quality problem known as “GIGO” – garbage in, garbage out – unless they take corrective action.
2. Examine and Analyze Errors
Increasing the frequency and quality of quality checks is a simple method that businesses may undertake. The technique gets based on the idea that if no quality checks get performed in the future, every piece of data will degrade in quality.
Increasing the number of checks, in addition to the frequency, can pay off big time. Clients love knowing that incorrect data will not contaminate their bright new business systems, even if the result set contains zero records. To put it another way, measure twice and cut once.
3. Standardize Processes
Companies must verify results regularly, regardless of the data entry accuracy rate they choose. Sight verification, double-key data capture verification, field validation, program adjustments, and post-processing reports are all actionable data entry accuracy verification methods.
Moreover, operators who collect data must understand what constitutes an error and when an error becomes an error. Application interpretation, field, and keystroke errors are all common sorts of errors.
Finally, data entry accuracy targets should get expressed as a percentage of total data collection. Whereas many businesses use lower percentages, typically set at 90% or less, to make achieving their data entry accuracy targets easier, companies can and should use greater data entry accuracy criteria.
4. Introduce Smart Automation tools like Machine Learning
Poor data acquisition is estimated to cost $14.2 million per year, according to one study. Aside from data predictions, AI, particularly Machine Learning, aids in data quality improvement by automating the data acquisition process through intelligent capture. Smart automation ensures that all relevant data gets captured and that the system is error-free.
In essence, Machine Learning can collect data without the need for manual intervention. Employees can skip administrative tasks and focus more on the customer if the most crucial facts get gathered automatically.
Here are some ways of how Machine Learning can help with data entry accuracy:
1. Detect and Identify Duplicate Records
Redundant and duplicate data entries can result in out-of-date records, resulting in poor data quality. You can use AI as ML to reduce duplicate records in a database and maintain precise golden keys. Without sophisticated techniques, identifying and removing repeated entries in a large company’s database is challenging. Intelligent technologies that can detect and remove duplicate keys can help an organization combat this.
A good example of AI implementation has intelligent features turned on by default to guarantee that contacts, leads, and business accounts are clean and free of duplicate entries.
2. Discover Anomalies
A minor human error can significantly impact the usefulness and quality of data in a CRM. An AI-enabled system can detect and eliminate flaws in a system. Moreover, you can also implement anomalies based on machine learning to improve data quality.
3. Include Third-party Data
AI can increase data quality by contributing to it, in addition to correcting and maintaining data integrity. By offering better and more complete data, third-party businesses and governmental units can considerably improve the quality of a management system and MDM platforms, allowing for more accurate and informed decision-making. Furthermore, AI gives recommendations on what to extract from a given set of data and how to integrate them.
Ultimately, when a business has all of its data in one location, it has significantly better chances of making well-informed decisions.
5. Observe and Provide Feedback
Whenever business leaders investigate what creates data inaccuracies in their firm, they should not be surprised if a long list of external and internal causes of errors emerges.
Here are three of the most common sources they are most likely to come across:
- Data Movement: Data Movement occurs when data gets altered wrong as it transfers from one database or system to another attributable to “disconnects” between various databases.
- Data Decay: This includes discrepancies that develop over time as changes occur but don’t get represented in the data records. Marital status, phone numbers, and addresses are all common examples.
- Incorrect Values: This occurs when the “wrong” value is input at the start and never changed. Even if the cause is as basic as a typographical error, data inaccuracy would still be the outcome.
Considering recurring problems might emerge merely due to a lack of continuing maintenance and monitoring, optimizing data entry accuracy is an ongoing goal.
For illustration, if contact information data is left unmanaged for only 30 days, it is anticipated that around 2% of it will become “bad.” Now the question remains: What is the solution?
Businesses can adopt reliable data quality tools, such as the ones listed below, to help them stay ahead of the data accuracy curve:
- Data Entry Accuracy Training: Employees must typically get trained and retrained about increasing data quality, regardless of their skill level.
- Data Monitoring: This option contains deviations that are automatically corrected depending on pre-defined business rules and principles.
- Data Profiling: Analyzing the data regularly.
- Data Standardization: Ensuring that data meets the requirements of predefined data quality techniques.
- Geocoding: Fixing name and address data with automated pattern-matching methods.
- Matching and Linking: A data comparison that links data records that are similar but somewhat different.
One of the most pressing difficulties that large and small organizations face daily is the trade-off between speed and data entry accuracy.
Businesses that respond to the problem more promptly and effectively will gain a competitive advantage over their competitors. This advantage is made possible by the use of AI in data capture.