1. Data Science for Agency Initiatives 2015 Dr. Brand Niemann Director and Senior Data Scientist/Data Journalist Semantic Community http://semanticommunity.info/ http://www.meetup.com/Virginia-Big-Data-Meetup/ http://www.meetup.com/Federal-Big-Data-Working-Group/ http://www.meetup.com/Northern-Virginia-Semantic-Web-Meetup/ http://semanticommunity.info/Data_Science/Federal_Big_Data_Working_Group_Meetup May 16, 2015 1

2. Government Leadership in the Data Age Linda F. Powell, Chief Data Officer, Consumer Financial Protection Bureau: – Consumer Complaint Database Tony Summerlin, Special Advisor to the CIO, FCC: – FCC Data (150?) Dr. Joah Iannotta, Assistant Director, Government Accountability: – GAO Government Data Sharing Community of Practice Niall Brennan, Chief Data Officer, Centers for Medicare & Medicaid Services: – How CMS is Using Big Data to Spur Healthcare Transformation (Readmissions) Data Science for Agency Initiatives 2015 2

3. Data Science for Agency Initiatives 2015: MindTouch Knowledge Base Data Science for Agency Initiatives 2015 3

4. CFPB Consumer Complaint Database 4 http://www.consumerfinance.gov/complaintdatabase/ My Note: See 62 MB CSV File and See Data Dictionary: Field reference)62 MB CSV FileField reference)

5. FCC Data 5 https://www.fcc.gov/data

6. FCC Datasets Download 6 https://www.fcc.gov/data/download-fcc-datasets

7. Data Science for Agency Initiatives 2015: Spreadsheet FCC Data Knowledge Base 7 http://semanticommunity.info/%40api/deki/files/33720/FCCData.xlsx?origin=mt-web

8. GAO Government Data Sharing Community of Practice 8 http://www.gao.gov/aac/gds_community_of_practice/overview Open data is the official policy of the U.S. government. A 2013 executive order required federal agencies to publish their information as standardized, searchable data, and the 2014 Digital Accountability and Transparency Act (DATA Act) mandated similar standards for the federal government's spending information. Even with these changes, the transformation to open data is not an easy one. Where it has been successfully achieved, the transformation required cultural change, specifically a shift from documents-based to data-centric thinking.

9. GAO Government Data Sharing Community of Practice: MindTouch Knowledge Base 9 GAO Government Data Sharing Community of Practice My Note: Knowledge Base for Data Extraction.

10. CMS.gov Data Navigator: Start 10 https://dnav.cms.gov/

11. CMS.gov Data Navigator: Search 11 https://dnav.cms.gov/Views/Search.aspx My Note: See Navigator, Catalog, & Glossary in Spreadsheet.

12. Data Science for Agency Initiatives 2015: Spreadsheet CMS Data Knowledge Base 12 http://semanticommunity.info/%40api/deki/files/33721/DatabaseContentReport_05-16-2015_063838.xlsx?origin=mt-web

13. Data Science for Agency Initiatives 2015: CFPB Consumer Complaints-Spotfire Cover Page 13 Web Player

14. Data Science for Agency Initiatives 2015: CFPB Consumer Complaints-Spotfire Counts by State 14 Web Player

15. Data Science for Agency Initiatives 2015: FCC Data-Spotfire Cover Page 15 Web Player

16. Data Science for Agency Initiatives 2015: FCC Data-Spotfire Analytics 16 Web Player

17. Data Science for Agency Initiatives 2015: CMS Data-Spotfire Cover Page 17 Web Player

18. Data Science for Agency Initiatives 2015: CMS Data-Spotfire Analytics 18 Web Player

19. Data Science for Agency Initiatives 2015: CMS Data-Spotfire Visualizations 19 Web Player

20. Conclusions and Recommendations The Government Leadership in the Data Age Event, featured four excellent government data age leadership speakers, and prompted me to do data science products on their datasets for a future meetup. My question to all the panel members was given all of this getting the data out, how much actual looking at the data is being done? – The GAO answer was they are looking at the data outliers to save taxpayers money. I found a limited amount of looking at the data was being done based on very limited published data science products, except for CMS. This conclusion is supported by a recent National Academy of Sciences Meeting on Drawing Causal Inference from Big Data, March 26-27, 2015, which concluded: Although we are producing and storing ever greater amounts of data, we have just begun to figure out ways to analyze and understand what the data show. 20 Drawing Causal Inference from Big Data