Project Topic

Conceive, develop and implement processes to assess and monitor data quality in an evolving information system used by the National Institute for Occupational Safety and Health.

Project Background

An office of the National Institute for Occupational Safety and Health [NIOSH] was given a mandate from the U.S. Congress and the Centers for Disease Control to implement a program of compensation for employees of atomic weapons manufacturers as well as individuals employed in various nuclear energy-related jobs. ATL teamed with several companies selected to provide support to NIOSH in this effort. My initial charter as a staff member of ATL was to devise an Information Systems Quality Assurance Plan to promote processes and procedures for their utility in identifying vulnerabilities in data quality and to assure that data and calculations were accurately maintained in the project’s data repositories.

Actions Taken

In order to gain familiarity with the project and establish a more detailed understanding of the requirements, I consulted with stakeholders. that is, those who were given the responsibility to organize the overall project direction and who were to implement the early start-up phases of the project. These included the IT director, a software development manager and a quality assurance lead on the project. I proceeded to develop a comprehensive plan which focused on a few core objectives and a strategy that allowed for the development of Data Quality Profiles. These profiles allowed for targeted assessments of various types of information that were planned for or were already being stored in key databases (elements of which were already in production). The profiles provided a range of quantitative views on the quality of information, from general historical trends to a “drill-down” detail at the record and field level for a particular circumstance.

Results

The plan, which allowed for a gradual, but continuing evolution of key quality indicators, was implemented, and resulted in the development of 37 unique data quality profiles. The implementation of this data quality plan included the development and maintenance of a data quality management system. Use of this system provided several ways to view historical trends and to detect emerging issues in the quality of the data being collected. The use of this system resulted in an overall improvement in data quality of as much as 90% over the first year of the project. For some examples of this data quality trending see: http://dqlrafales.blogspot.com/