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, it is ensuring that the information consumed by the end users is in alignment with their expectations. As such, data quality plays a major part in any EIM implementation. The old adage of “garbage in, garbage out” still holds true today. The most sophisticated information platform is useless if the underlying data cannot be trusted or is considered to be 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 EIM/BI initiatives. As such, data quality is a key part of Edgewater’s implementation methodology. Data Quality must be achieved, then maintained. Therefore, we offer two distinct services:
Edgewater’s Data Quality Framework
