Ensuring Accuracy in E-commerce

Lack of data entry accuracy can result in inconsistent and incorrect data that leads to poor decisions. Poor decisions in turn can lead to ineffectiveness, errors, additional costs or loss.

The inability to successfully manage data quality may also result in fines or other regulatory actions. In this article, we bring you important tips to avoid all these eventualities and improve data entry accuracy.

Understanding The Importance of Data Quality

In the simplest sense, accurate data is data that is fit for use. In order to achieve data entry accuracy, data professionals should understand the fundamentals of data quality and accuracy. A data value must be both the right value and be represented in an unambiguous form. For example, data is accurate when the address of a customer in the customer database is the real address. This data must be complete, such as it should have all aspects of the address, namely; the First Name and the Last Name of the customer, the Street Name, State Name, Country Name, and the Zip Code. If one of these is missing, then the data quality takes a hit. The other dimensions of data entry quality include timelines, completeness, accessibility, legibility, and relevance. Of these dimensions, Data Accuracy is the most important as it represents all business activities, entities and events.

Setting Your Overall Goals For Data Entry Accuracy

Ensuring client-desired data accuracy depends on management’s ability to develop realistic goals for accuracy, stated in terms that all producers and users of the data can understand and visualize. Often times, quality are a significant part of the company’s mission statement. Then this becomes the mission of the data processing organization when converting and processing information. As broad goals are developed to assist in the deployment and the attainment of the company’s mission, specific goals are used at the task and operational levels of the organization to carry out the mission. Accurate data entry is achieved through four broad objectives:

• Effective coding
• Effective data capture
• Efficient data capture and entry
• Assuring quality quality through validation

If your company takes the time to plan for quality assurance and make it an integral part of your general business practices, you can save yourself a lot of time spent down the road, and make your data more reusable and more accessible.

Fixing Data Entry Accuracy Rate For Your Business Data

Data accuracy goals should be stated as a percent of accurate data converted. This might look like the data accuracy goals for non verified characters is 97% of all the characters converted or for verifying character, it is 98.5%. All accurate rates are based on some method of verification. Methods can range from double key data entry verification, sight verification, program edits, field validation, and post processing results reports. The method of verification may vary between data conversion applications, but data entry operators should know what constitutes an error. Errors should be defined by types, keystrokes, field or application interpretations. For example, does a transposition of two numbers count as one or two keystroke errors? Do three  characters keyed incorrectly in the same field count as one field or three key stroke errors? It is also important to define when an error becomes an error, in case where an operator corrects their own error before the verification process occurs.

Identifying Internal and External Sources of Data Innacurracies

There are several causes and sources of data accuracy, the predominant being:

Wrong Values

The most common of these causes come from the initial data entry users. In simple terms, it means that the user entered wrong value. This could also be that typographical errors were committed. This is an aspect that can be overcome by having skilled and trained personnel to do the data entry.

Data Decay

Data decay can lead to inaccurate data. Many values which are inaccurate can become inaccurate through time, hence data decay. For example, people’s addresses, telephone numbers, number of dependants and marital status can change and if not updated, the data decays into inaccuracy.

Data Movement

Data movement is another cause of inaccurate data. As data moves from disparate system to another, it could be altered to some degree especially if the software running the data base is not very robust

Adopting Robust Data Entry Accuracy Standards

Data entry accuracy is a continuous process that requires strategy, and it involves ongoing monitoring and maintenance of data quality. Cleaning up data is one thing – but estimates indicate that as much as 2.1% of contact information goes bad left untended for just one month. Data quality initiatives often begin at a local level when a business unit realizes that a particular application or database is fraught with errors or inconsistencies. Most data quality tools offer a series of techniques for improving data, including the following:

Data Profiling

This is accessing the data to understand its overall degree of accuracy.

Data Standardization

This is utilizing a business rules engine to ensure that the data conforms to pre-defined quality rules.

Geocoding

It has automated pattern matching tools for fixing name and address data and applying postal standards.

Matching and Linking

This is comparing data to align similar but slightly different records.

Monitoring

This is keeping track of data quality and auto correcting the variations based on pre-defined business rules.

 

Acting With Speed

Lines of business across organizations collect, keep, and apply data in different ways – and with varying levels of success. The customer, however, doesn’t care about the inner workings of the business. Wherever they are, whatever their question, issue or concern, they expect the business as a whole to know them, know their relationships across the enterprise, and be able to address their needs regardless of product, solution or channel. Act with speed to satisfy your customers by providing accurate data.