Skip to main content

Building a plan for a comprehensive assessment of data quality

Rachel Wheeler Archive

Today, data quality is an all-important value for almost any organization that cares about customers or business-to-business clients. For anyone that needs to keep track of anyone, the key is to have accurate information in house that can help.

It's important that companies do more than just spot-check their data once or twice for quality. Mistakes in accuracy happen all the time, and not just at the initial point of collection. Data is being moved from place to place frequently, often by hand, and mistakes happen. There's also the risk of entries being duplicated at any time, which can bring a whole host of problems for any business.

According to Enterprise Apps Today, a regular assessment of data quality is essential for any organization. Michael Collins of BackOffice Associates explained that conducting such tests can be useful, as it might reveal serious issues that are creating unnecessary hassles and costs.

Why this matters
Collins explained that often, there's a disconnect between companies' long-term goals and what they're capable of, given the limits of their data. If business leaders are unable to rely on their information in their databases to help them succeed, they need to do more.

"By leveraging technology and data experts to review all levels of source data at the master, operational and transaction levels, executives can break down the behemoth effort and take a critical step in understanding whether their organization's data meets established standards and where the right skills and data resources can be applied to support a well-oiled data operation," he stated.

This begins with organizations identifying the key problems with their data and doing everything they can to make amends for them.

Identifying the key problems
Perhaps the most common problem with the typical company's data supply is the duplication of records. Having multiple entries for one client or individual can lead to massive logistical problems, often involving wasted time and money.

This only gets more confusing when dealing with entries that are misaligned across multiple systems. If a sales database has one record of a person, and a customer service department has another, but one has a misspelled name or a wrong address, that can be a nightmare to rectify.

There are also other miscellaneous issues that arise during the data management process. Among them are incorrect payment terms and HR mistakes. Data quality of all kinds is important, and companies should seek to eliminate as many imperfections as possible.

Comments