Informatica White Paper Sample

Traditionally, Data Quality processes were introduced to organizations in ways such as cleansing customer data for marketing campaigns, standardizing data prior to a data migration project, or providing matching functionality as part of a business application customer-screening process. As business requirements have changed, organizations today recognize that a proactive approach to Data Quality can enable a wide range of business imperatives, including compliance, improved decision-making, increased operational efficiency, and cost-reduction initiatives. Business intelligence and data governance applications require Data Quality scorecards and Data Quality monitoring processes. Supply chain management processes require Data Quality business rules for all master data types, including customer, supplier, product, asset, and financial data. Fraud, security, and screening applications require high-precision matching to avoid high-risk mistakes. Data entry applications require high-performance, real-time search and match capabilities to achieve the response times required by the business processes. Master Data Management (MDM) and Customer Data Integration (CDI) applications depend on an end-to-end Data Quality process.

Similarly, Identity Resolution processes have evolved to assist organizations in dealing with the challenges of handling identity data: the information that specifically and accurately identifies a client, a prospect, a supplier, a taxpayer, a criminal suspect, a product. By its very nature, identity data is subject to unavoidable error and variation -- spelling and typographical errors, transliteration differences, nicknames, abbreviations -- that can compromise the performance of basic search and match processes, generating false positives or missing matches entirely.