Because Blazent is a leading provider Data Intelligence and Integrity solutions, I felt compelled to read Philip Russom of TDWI Research’s white paper outlining his top priorities for data quality solutions. This top list is a subset of Philip’s, with a focus on the data that typically supports the IT Service Management (ITSM) functions in an organization. ITSM functions rely heavily on a foundation of dependable data. This makes improving data quality a critical requirement for the successful delivery of IT services.
Priority #1: Broader Scope for Data Quality We say data quality as if it’s a single, solid monolith. In reality, data quality is a family of eight or more related techniques. Data standardization is the most commonly used technique, followed by verification, validation, monitoring, profiling, matching, and so on. These techniques are applicable to data that is used by any business function including IT, Operational Technology (OT), Finance, Sales, and Marketing. Don’t make the mistake of limiting the benefits of data quality management to just IT.
Priority #2: Real-Time Data Quality TDWI’s survey revealed that real-time data quality is the second-fastest-growing data management discipline, after master data management and just before real-time data integration. Applying real-time data quality techniques as data is created and streamed means data can be make ITSM more responsive to real-time business needs.
Priority #3: Data Quality Services Data quality techniques need to be generalized so they are available as services that can be called from a wide range of tools, applications, databases, and business processes. Data quality services enable greater interoperability among tools and modern application architectures as well as reuse and consistency.
Because ITSM processes rely so heavily on data accuracy and completeness, data quality services have a tremendous value potential to drive operational efficiencies.