Establishing a culture of quality throughout the enterprise is mandatory for maximum benefits. Data quality is everyone's responsibility, but requires the company to establish the proper foundation through continuous training, proper communication channels, effective collaboration, and an efficiently adaptive model that in spite of constant changes can continue to deliver results quickly.
As companies mature, their data quality focus moves from reactive to proactive. Anticipating issues is critical to minimizing risks and maximizing opportunities. But data quality is not a single project. It must be an ongoing and self-feeding effort focused on continuous improvement of data and the process itself.
The first step to a successful data quality program is to understand the data well and how it fits the business. Good quality does not necessarily indicate 100-percent compliance. Good quality is compliance sufficient to attend the business properly, which could be 60 or 80 percent, for example. It is not data quality for the sake of data quality, but it is about serving a business purpose.
Creating a data quality baseline helps companies to understand their data as well as to identify and prioritize areas for improvement. The first requirement to correct business decision is accurate information, which can only be achieved with measurement, business alignment, and fitness assessment.
Data quality management is likely the most pervasive MDM component and the one that requires the highest IT and business collaboration. There is a fine line separating technical and business aspects of data, which challenges the traditional separation of duties. A team of people with suitable skills to bridge the cross-functional gap is a coveted asset that can further advance the discipline inside the company.