Most organizations, large and small, recognize that better quality data makes good business sense. Business owners of operational systems, however, often don’t have the experience or the solutions to fix broken data. Often times, data quality becomes a data warehouse-led project. What about operational data that lives in ERP, CRM and other critical applications that falls outside the warehouse? Best practices, such as a data quality audit, establish sets of “problem data” and set the stage for leveraging the required skills and tools to improve and monitor that data, as well as related business rules and metadata. By analyzing existing documents and artifacts, conducting tests to assess value ranges and relationships, the process of operationalizing data quality first identifies metrics, details and anomalies. This process also supports the identification, tracking, management and resolution of individual data quality issues with the key attributes identified.

Join Evan Levy, DataFlux Vice President of Consulting and Professional Services, as he outlines the solutions required to “operationalize” data quality – throughout the enterprise. Whether you are beginning an important new initiative and need to establish processes for data correction, or launching a sweeping data governance program, Evan will share his years of experience and best practices for implementing the processes and tools required for quick integration of key data, as well as data profiling and business rules management. You’ll learn that operational data quality is the key to uncovering problematic subject areas and avoiding data surprises.