​Data Quality

Data quality has been defined as “the quality of data’s content and structure (according to varying criteria), plus the standard technology and business practices that improve data, such as name-and-address cleansing, matching, house-holding, de-duplication, standardization, and appending third-party data” (TDWI, 2006).  In simpler terms, data quality is ensuring that information consumed by the end users is in alignment with their expectations. The most sophisticated information platform is useless if the underlying data cannot be trusted or is considered unreliable. Inaccurate and incomplete data causes end-users to first distrust and then refuse to use centralized data environments.

Edgewater’s EIM Practice recognizes that data quality is a foundation of any EIM project and is essential for the success of information management and business intelligence (BI) initiatives.  As such, data quality is a key part of Edgewater’s implementation methodology, and two distinct services:

• A Data Readiness Assessment will define the quality of your data, and develop detailed plans for improvement
• A Data Quality Roadmap charts a course for cleaning and improving data to insure confidence in business intelligence and other data driven initiatives

Master Data Management

• The plan and tools to maintain the quality of your mission critical data
• Master Data Management sets business rules and practices for data import and use that will mantain quality into the future