As businesses embrace the use of data-driven processes to transform virtually every aspect of their operations, the integrity of that data and the data management infrastructure have come under closer scrutiny. Data is more abundant for businesses than ever before, but organizations are facing greater limitations in regard to data quality.
According to Gartner, the average enterprise organization believes that poor data quality cost their company roughly $11.8 million in 2018 alone. Effective data management is one way to implement procedures and infrastructure that improve data quality, but this business function isn’t without potential pitfalls and challenges of its own.
Here’s a look at some of the common challenges your own data management project may face—and how to take corrective action.
Data Isn’t ‘Clean’
If the data maintained in your data management system isn’t reliable, your overall data management is doomed from the start. Data can be filled with inaccuracies or duplicate information, or it can be structured in a way that your management system isn’t set up to properly manage. A lack of standardization of data can also make it difficult to consolidate data from different sources, which hurts your overall dataset.
What you can do: Use a business rules and workflow solution to implement validation processes that clean data before it’s used in any business processes. Forms and addresses can be validated through data verification tools after they’ve been supplied by a user. Install an AI-powered solution that identifies duplicate or inconsistent data, as well as other data anomalies, and either resolves these inconsistencies or flags them for human review.
