As revealed in a recent Netprospex study covering B2B data records, improving data entry accuracy is truly an example of “easier said than done” – 72 percent of industry information is typically missing and 35-50 percent of data records are “incomplete.” As you might imagine, poor data entry accuracy leads to a series of other serious problems such as poor decisions that in turn cause additional errors and costs. In this article, DataEntryOutsourced sheds some light on the importance of data quality and data entry accuracy.
#1. Understand the Importance of Data Quality
There are several key elements involved in determining the quality of your business data – data accuracy, relevance, legibility, accessibility, completeness and timeliness. Of these dimensions, data accuracy is usually considered “most important” to achieve. Nevertheless, an Experian study revealed that American companies now believe that 32 percent of their data is not accurate (this figure is up from 25 percent in 2014). In other words, the problem is getting worse in most cases.
In order to fix the problem and achieve a more acceptable data entry accuracy rate, data professionals have their work cut out for them. For example, data values must reflect “the right value” as well as represent forms that are not ambiguous. Without corrective action, businesses will be faced with a familiar data quality problem known as “GIGO” – garbage in, garbage out.
#2. Set Your Overarching Goals for Data Entry Accuracy
Improving data entry accuracy standards starts with setting achievable data entry accuracy goals. Three suggested broad objectives for accurate data entry are the following:
- Data capture – effective and efficient data capture and entry
- Coding – accurate and effective coding
- Quality – assure quality via validation
Companies with the “best” results for data entry accuracy typically include “quality” in their company’s mission statement. Planning ahead for quality assurance is part of a prudent strategy for improving data entry accuracy. Management must take an active role that includes developing realistic and understandable goals for all users and producers of data.
#3. Fix a Data Entry Accuracy Rate for Your Business Data
Whatever data entry accuracy rate you decide to adopt, results must be regularly verified. Methods of verification for data entry accuracy include sight verification, double-key data entry verification, field validation, program edits and post-processing reports.
Data entry operators need to know what will be viewed as an error and when an error becomes an error. Errors are often categorized by types such as application interpretation, field and keystroke.
Overall data accuracy goals should be reflected as a percentage of total data entry. While many organizations adopt lower percentages (often 90 percent and below) to make it easier to achieve their data accuracy goals, you can and should adopt higher data entry accuracy standards. For example, data management experts such as DataEntryOutsourced guarantee a minimum data entry accuracy rate of 95 percent.
#4. Identify Internal and External Sources of Data Inaccuracies
When you take a closer look at what is causing data inaccuracies in your organization, don’t be surprised if you end up with a lengthy list of both external and internal sources of errors. Here are three of the major sources you will often find:
- Data Movement – This happens when data moves from one database or system to another and is improperly altered due to “disconnects” between multiple databases.
- Data Decay – Inaccuracies that occur over time as changes take place without being reflected in data records. Common examples include marital status, telephone numbers and addresses.
- Incorrect Values – If the “wrong” value is entered at the very beginning and never corrected. While the cause can be as simple as a typographical error, the end result is still data inaccuracy.
#5. Adopt Robust Data Entry Accuracy Standards
Improving your data entry accuracy is a never-ending quest because recurring problems can occur simply due to the lack of ongoing maintenance and monitoring. For example, it is estimated that about 2 percent of contact information data goes “bad” if it is left unmanaged for only 30 days. The solution? – adopt robust data quality tools such as the following that will help you to get ahead on the data accuracy curve:
- Data Entry Accuracy Training – As skilled as your employees are, they typically must be trained and retrained in how to improve data quality.
- Data Monitoring – This includes auto-correcting variations based on pre-defined business rules.
- Matching and Linking – A comparison of data that aligns similar but slightly different data records.
- Geocoding – Automated pattern-matching tools for fixing name and address data.
- Data Standardization – Ensuring that data conforms to pre-defined data quality tools.
- Data Profiling – Regularly assessing the data.
#6. Act with Speed
The dilemma between speed and accuracy of data quality is one of the most important issues faced on a daily basis by big and small businesses alike. Businesses that attack the problem more quickly and effectively will place their companies in a favorable position relative to competitors. One of the most timely and cost-effective methods for taking on this vital challenge is by outsourcing data entry requirements to trusted business partners like DataEntryOutsourced.
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