The market for data quality tools has become highly visible in recent years as more organizations understand the impact of poor-quality data and seek solutions for improvement. Traditionally aligned with cleansing of customer data (names and addresses) in support of CRM-related activities, the tools have expanded well beyond such capabilities, and forward-thinking organizations are recognizing the relevance of these tools in other data domains. Product data — often driven by MDM initiatives — and financial data (driven by compliance pressures) are two such areas in which demand for the tools is quickly building.

Data quality tools are used to address various aspects of the data quality problem:

 Parsing and standardization — Decomposition of text fields into component parts and formatting of values into consistent layouts based on industry standards, local standards (for example, postal authority standards for address data), user-defined business rules, and knowledge bases of values and patterns

 Generalized “cleansing” — Modification of data values to meet domain restrictions, integrity constraints or other business rules that define sufficient data quality for the organization

 Matching — Identification, linking or merging related entries within or across sets of data

 Profiling — Analysis of data to capture statistics (metadata) that provide insight into the quality of the data and aid in the identification of data quality issues

 Monitoring — Deployment of controls to ensure ongoing conformance of data to business rules that define data quality for the organization

 Enrichment — Enhancing the value of internally held data by appending related attributes from external sources (for example, consumer demographic attributes or geographic descriptors)

The tools provided by vendors in this market are generally consumed by technology users for internal deployment in their IT infrastructure, although hosted data quality solutions are continuing to emerge and grow in popularity. The tools are increasingly implemented in support of general data quality improvement initiatives, as well as within critical applications, such as ERP, CRM and BI. As data quality becomes increasingly pervasive, many data integration tools now include data quality management functionality.