Home →
Data Management →
Improving Enterprise Data Quality Management Creates Opportunity
- On Way to Omni-Channel, Retailers Try Beacons and More
- Chief Data Officer Role Continues to Evolve
- What Is Sales Enablement Software and Do You Need It?
- 5 Ways to Use Virtual Reality in the Enterprise
- 8 Open Source BPM Software Options
- How HTAP Database Technology Can Help You
- 9 Apps Empowering Finance
Data quality management is an opportunity to improve the data going into the enterprise data warehouse and business intelligence applications.
A good way to have a clear definition of data quality management is to understand its purpose. According to this report on DatabaseJournal.com, it is primarily the creation of an overall implementation strategy to improve data quality with a focus on data conformance. Also noted in the article is that data quality management facilitates the integrating of data sources into a consolidated view of the enterprise data warehouse, such that all silo-ed applications with its own data rules now has to conform to a single version of the truth; all data rules now must integrate and conform to one set of rules for data of specific types and formats.
"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."
This article was originally published on August 26, 2010
