Guide to Better Data Quality Management

There are many components that are necessary for a business to be successful.

23:08 18 June 2019

but if there is one specific factor that is indispensable, it is data quality and how to manage it(DQM). There are a litany of reasons as to why managing the quality of data is important, but to understand what this means for businesses, a deeper understanding of the concept should be understood.


What is Data Quality Management?


As the name suggests, data quality is fitness of specific data to serve its purpose in the context of how the organization will utilize it. With external factors such as reliability, relevance and accuracy being put into play, this aspect of a business can influence numerous functions and have a great deal of potential rewards and consequences depending on how it is managed.


Data quality management to the practices that are aimed at having a maintenance of high quality information. DQM is essential to any analysis of data. If there is any sense to be made out of data that an organization wishes to use, quality management is essential to helping a bottom line. There are a series of components as to what proper DQM means for an organization.


First, any initiative that a business proposes requires a foundation, and managing the quality of data serves as that foundation. This is because data is often at risk for being unreliable or outdated. This can lead to critical mistakes which can cost a business profit or ruin their reputation. That is why a thorough quality management program will help establish not only a framework, but expectations for departments in the organization so that there is a determined standard that must be reached.


Also, updated and relevant information will also give potential consumers and business partners a great indication of what the operations of the business is. This increases confidence how the data can be applied to practical solutions and decrease the number of unnecessary costs that a business may encounter.


DQM is invaluable when it comes to establishing a framework for how data is not only obtained, but for how it is used. For a company to succeed, and meet certain objectives, there must be a clear line of communication and procedures. Many businesses often wonder what specific ways they can improve their management of data.


How to Improve Data Management


According to a benchmark report by Experian, most organizations believe most quality issues involving data come from reports by customers and employees. Therefore, many organizations are not being prudent enough in having a plan of action for managing data. Fortunately, there are specific strategies that proven to be useful in this context.


One tip that organizations should follow is being privy to how the sales team enters data. The sales team updates company records with new information. The data entry here needs to be examined for completion, consistency and accuracy. If left unchecked, the data entered may not be relevant or may be misleading.


In addition, duplicate records should be fixed. They are usually identified through email addresses, but it is not always enough to find duplicate records. Duplicates can cost a business money, break the automation process and interfere with potential sales. If you happen to notice a duplicate, it needs to be addressed on the spot.


Finally, normalizing data is another concept businesses should master. Data can be collected from a variety of sources, but one standard should be established.


If businesses truly understand what is data quality and how important it is, it can save them not only money, but their reputation.