DQ Metrics
Knowledge Integrity’s approach for monitoring and reporting on the quality of organizational data relies on an underlying infrastructure that supports automating the way that data controls and metrics are defined, managed, monitored, reported, presented, and how results are communicated to the appropriate data owners for review, validation, and analysis. Knowledge Integrity will customize our reference architecture for a repository for the data collection, analysis, and reporting of defined data quality metrics and provide a roadmap for designing and developing processes and tools to facilitate the population, management, and presentation of a Data Quality Scorecard.
The objective of this data quality metrics repository (DQMR) design project is to collect requirements for, and then evaluate and define the appropriate level of detail for data and process models in preparation for implementing the components to support the automation of the collection, analysis, and presentation of data quality metrics within a Data Quality Scorecard. Knowledge Integrity principal consultants work with the client in reviewing and evaluating current techniques for the collection, presentation, and validation of data quality metrics in preparation automating selected repeatable processes to simplify the data quality scorecard data collection and analysis process.
As part of this consultation, KII principal consultants will work with the client to survey and evaluate existing technologies in place in preparation for deploying automation of selected aspects of the data quality metrics process. The objectives for the framework design incorporate:
1. Standardizing and socializing business processes for automatically populating selected metrics into a repository
2. Collecting requirements for an appropriate level of design for a repository for capturing data quality metrics
3. Standardizing a reporting template for reporting and presenting data quality metrics
4. Developing the appropriate level of design for automating the extraction of metric data from the repository
5. Developing the appropriate level of design for automating the population of the reporting and presentation template.
For more information on how Knowledge Integrity can help your organization design and implement data quality metrics, contact David Loshin at 301-754-6350.
