DATA QUALITY – WHAT, WHY, HOW, 10 BEST PRACTICES & MORE – Enterprise Master Data Management • Profisee
If you have poor data quality, your information’s credibility suffers. With bad information quality, you lack actionable knowledge in business operations and are unable to apply that knowledge or do it incorrectly resulting in risky business outcomes.
There are several definitions of data quality. The two predominant ones are:
- Data is of high quality if it is fit for its intended use or purpose.
- Data is of high quality if it correctly represents the real-world construct it describes.
These two possible definitions may contradict each other. If, for example, a customer master data record is fit for issuing an invoice for receiving payment, it may be fit for that purpose. But if that same customer master data record is also incomplete or incorrect for customer service, because the data incorrectly describes the who, what and where of the real-world entity of having the customer role in that business operation, we have a business problem.
Master data must be suitable for multiple purposes. You can achieve that by ensuring real-world alignment. On the other hand, it might not be profitable and proportionate to strive for the perfect real-world alignment in order to have data fit for the intended purpose within the business objective where a data quality initiative is funded. In practice, it is about striking a balance between these two definitions.
One of the biggest contributing factors for data inaccuracy is simply human error. Avoiding or eventually correcting low quality data caused by human errors requires a comprehensive effort with the right mix of remedies concerning people, processes and technology.
Other top reasons for data inaccuracies are a lack of communication between departments and inadequate data strategy. Solving such issues calls for passionate top-level management involvement.
