Best Practices for Improved Data Quality Management

Best Practices for Improved Data Quality Management

There are many aspects of a business that is needed to perform efficiently to sustain a company’s reputation and increase their bottom line. One such component that is very important is proper quality management. Data quality can affect a business in numerous ways, and optimal management is needed to ensure proper functioning in many areas.

What Is Data Quality?

Data quality is essentially an assessment of the quality of information that a company has. The quality of the data is measured by the ability to serve its purpose. There are a variety of factors that influences the quality of data, including completeness, accuracy and consistency. Proper data has the capacity to influence decision-making, planning and other operations that are needed for a company to perform correctly. Most of the major decisions that companies make for their future hinges on proper data quality management. In fact, the leading cause for failure in most companies can be related to the reliance of low quality data.

Components of Data Quality Management

There are a number of components that help create high-quality data that can be reliably used. The first, and perhaps the most important component, are the people responsible for the data. Data and technology are only as reliable as those who implement it. The quality of data can often be subjected to human error, and if mistakes are made and not caught before use, it could produce devastating results. Everyone from the business analyst to the organization change manager play a crucial role.

Another component for proper management is profiling. Profiling with data management is a multi-faceted process that is essential in practical application of the data. This process involves reporting the quality, making a comparison to the metadata, reviewing the data thoroughly and running statistics. This can help develop further insight to the data that presently exists, and will help provide a launching point for how it will be utilized. Most importantly, this will set the standard for how future pieces of data can be used.

“Quality rules” are also established during this process. These are the rules with which the data has to abide by in order for the data to be deemed credible. These rules are based on the goals and requirements of the business it is being used for. Other areas in ensuring high quality data include reporting data and data repair, a process in which the data is remediated.

How To Improve Management

While data management is a meticulous process, it is also important to preserve a company’s reputation. There are some important steps to be taken in order to improve management and always ensure that the data being used is viable.

One of the first steps that a company can take is to prevent duplicate records. These can typically be identified via email addresses. However, duplicates can often be difficult to find. This can also prove to be an expensive problem if it is not rectified quickly. If duplicate records are found, getting rid of them immediately should be a priority action. This is because it can eventually have activity related to it.

Normalizing the data is also crucial because if the data collected is from a variety of sources, automation programs may have trouble discerning the data. Creating a standard to establish a lone approach will help make the data as clear as possible.

The sales team also plays a crucial role in quality management because they are the ones who update records with new information. Checks for consistency and accuracy must always be checked for.

Alex huge

I am Professional Blogger and Writer