PDQ was founded in 1993 and is focused on providing hosted analytic application solutions for the Fortune 500, state and federal agencies, and leading universities.

PDQ's initial focus was to help researchers, scholars, policy makers, and the public at large access and make effective use of very large public data sets. PDQ received significant support from the National Institutes of Health to develop the PDQ-Explore technology for the processing of large census and survey data sets at the National Institute for Child Health and Human Development (NIHCD) and the National Institute on Aging (NIA). PDQ also developed supporting tutorial and instructional materials to aid researchers.

To date, PDQ has received a total of 16 Small Business Technology Transfer (STTR) and Small Business Innovation Research and supplemental awards. These awards include eight Phase I small business awards, six Phase II awards, and two supplemental awards, totaling more than $4,000,000.

Building upon its work with the NIH, PDQ has expanded its client base to include the U.S. Census Bureau, General Motors, Pulte Homes, the London Economist, the University of Michigan, the University of Costa Rica, Wayne State University, University of Waterloo (Canada), Ingham County, the National Science Foundation (NSF), and Lawrence Livermore National Laboratory.

Grant History

PDQ's first two grants were for STTR projects from NICHD to fund joint work with the University of Michigan's Population Studies Center to develop the PDQ-Explore technology, data and metadata resources, as well as tutorial/instructional materials. Subsequent SBIR grants supported work on:

  • Optimizing the performance of PDQ-Explore by investigating technical hardware, system, and software issues related to applying parallel processing to data management and analysis;
  • Adding larger and more complex data sets to our database;
  • Developing a graphical user interface to integrate access to data, documentation, and analytic tools;
  • Developing user support materials in the form of tutorials, integrated help, and the application of expert system and intelligent agent concepts;
  • Investigating re-sampling;
  • Investigating issues related to confidentiality and disclosure avoidance in public microdata;
  • Extending the analytic capabilities of the system;
  • Developing a Web-front end that enables 1000 or more concurrent users accessing PDQ over the Internet.