Improving Enterprise Data Quality Management Creates Opportunity
“Once the purpose of data quality management has been clearly defined, the next step is to establish the data quality lifecycle with a specific focus on collaboration with IT and business areas. It’s a long and extremely iterative process, with the notion that with each iteration the number of anomalies and errors generated are reduced to the point where the goal has been met. “Defining the requirements for data quality provides the framework for the entire effort. It is during this phase that the entire team meets in work sessions to establish thresholds for acceptable data quality. This phase also defines the mandatory activities that must be completed in order for the data quality effort to be successful. These activities include reviews of documented business functions and/or use cases; identification of candidate data sources; methods for handling rejected data; classification of data elements as mandatory and optional; metrics to measure data quality and related progress. The great part about this phase is the genesis of the business subject matter experts morphing into the roles of the data stewards.”
By 2032, the enterprise data management market is anticipated to reach USD 531 Billion in revenue – According to Market.us
