Improve Your Data Quality Management Today

Improve Your Data Quality Management Today

The Problems That Stem from Poor Data Quality Management 

Companies fail for all sorts of reasons.

Poor leadership can result in a company never establishing any kind of identity that distinguishes it in the mind of consumers. Unqualified employees can bring about the downfall of a company from the inside. Low quality products and services will fail to attract customers.

One more factor that can play into the demise of a business is the mishandling of customer data. If companies don’t value the importance of data quality, they will run into numerous problems that can sink them in a hurry. 

What Is Data Quality and Why Is Preserving It Essential?

Prior to listing the many issues that can emerge from the mismanagement of data, it will be best to first define what is data quality refers to in this context.

The quality of a particular bit of data essentially relies on two different factors, with those being accuracy and relevance.

If a line of data is inaccurate, then it is obviously incapable of offering any kind of value to a company given that it will only mislead those who reference it.

Relevance matters because a correct address will still present no value to a company if the customer it is linked to has long stopped paying for products or services. At that point, the line of data is just taking up important space in the database.

Notably, the problems caused by low quality data go beyond just confusing employees or clogging up databases. They can have real world effects that can be devastating as well. 

The Problems Presented by Poor Data Quality Management

The issues caused by poor data are wide-ranging. Some of the more serious ones are listed below. 

Bad Data Can Clog Up the Creation of Products

Let’s say that your company is in the business of producing a mass good of some kind. Given the level of demand for your product, simply relying on manual production will likely not cover it. Automation has to be baked into the way your company operates.

The potential issue here is that automation relies heavily on accurate data. A single field containing inaccurate data can lead to the creation of numerous useless products. 

Bad Data Can Lead to Inefficient Operations

Lines of data don’t have to be inaccurate to be detrimental to a company’s daily operations. Data being outdated is enough to deal plenty of damage.

If the database fails to account for new hires who can be assigned to handle easier jobs that will then free up more experienced employees to take on harder tasks, then the system your company is using is just woefully inefficient.

Instead of the workforce additions serving to make operations smoother, they may be unable to have a positive impact because they are not being deployed in the best way possible. 

Bad Data Can Cause Marketing Nightmares

Does your company have a new product or service it wants to market to customers? Then, the data in your company’s possession must be completely accurate to ensure the marketing efforts work as intended.

Nothing can annoy a customer faster than constantly receiving ads from a company. If your database is unable to correctly reflect marketing moves, then count on numerous complaints coming in. 

How the Quality of the Data Can Be Improved and Maintained

For the purposes of improving the quality of data, companies need to do a few things.

First off, they need to gather data for the purposes of analysis, suggests During this part of the process, the lines of data can be analyzed to see if they are accurate and relevant.

With that out of the way, the company can focus its efforts on creating general guidelines that must be referenced whenever new entries are being created or old fields are repopulated.

After the entries have been created or edited, they must be inspected for accuracy and just overall completeness. Data matching tools will help determine if the values included in the entries sync up with their real life counterparts.

Regular maintenance of the databases must also be carried out to ensure that the quality of the data is preserved.

Maintaining data quality is by no means an easy process. Considering how costly the adverse effects of using poor data can be though, companies must still commit fully to the cause of protecting their data. 


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