This list is a subset of the white paper Philip Russom of TDWI Research wrote, outlining his top priorities for data-quality solutions, 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 quality for dependable data intelligence. This makes improving data quality a critical requirement for the successful delivery of IT services.
Priority #1: Broader Scope for Data Quality: Data quality is not a single 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 any business function uses, 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 cleansed and standardized, shortening the time needed to deliver value and making ITSM more responsive to real-time business needs. Data must be ingested and analyzed by a data-quality solution to provide approximate real-time insights.
Priority #3: Data Quality Services: Data-quality techniques must be generalized, so they are available as services 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 for reuse and consistency. Because ITSM processes rely so heavily on data accuracy and completeness, data-quality services have tremendous potential value to drive operational efficiencies.
During recent years, data intelligence solutions have increased in sophistication, adopting big-data technologies, supporting unstructured data sources and employing advanced machine-learning and predictive-analytics techniques. Even with the latest analysis and normalization techniques, the basic requirement is to deliver insights that are based on data that is as verified and complete as possible. The only way to increase accuracy is to draw from and correlate against as many data sources as possible.
Blazent provides a SaaS data intelligence and integrity service, which many leading organizations use to improve their informational and operational productivity. As an exemplary use case, you can learn how Pacific Life improved data quality in their ITSM function in the video here.
You can read TDWI’s E-Book on Data Quality Challenges and Priorities here.