1. U.S. Department of the Interior U.S. Geological Survey Next Generation Data Integration Challenges National Workshop on Large Landscape Conservation Sean Finn, USFWS; Jim Strittholt, CBI; Tim Kern, USGS October 2014

2. Data Integration Now and Tomorrow  Not just a technical concept  Interaction of  People  Policy  Technology  Critical at every scale – local, regional, and national

3. Data Integration Challenges

4. Human Factors Challenges  Data collected for different missions may have custom and mission-specific vocabularies  Expensive to deal with varied data formats and types (extraction, transform, and load)  Inconsistent spatial representations can confuse visualization and analysis efforts  Complex schemas can make it hard to pull out project-appropriate data  Inadequate metadata and unclear provenance

5. Human Factors Approaches  Provide support for efforts at all scales  Establish best practices and standards for cooperating data catalogs and repositories  Establish connections across organizational bounds  Develop “share spaces” for tools and analysis methods  Provide examples of different integration techniques and approaches (“successes”)

6. Success Stories

7. Policy and Economic Challenges  Metadata policy contradicts data.gov policy  Data release policies inconsistent with Project Open Data  Skills mismatches  Infrastructure needs, funding  Resource plans rarely consider long-term expenses due to new security policies  User expectations

8. Policy and Economic Approaches  Infrastructure  Commercial and government clouds  Open compute cluster access  Staff  MOOCs  Communities of interest  Resources  Open source projects  Free or lower cost collaboration sites

9. Policy and Economic Approaches  User Expectations  Agile project development  involve scientists and not tech staff  Infrastructure availability  More ready-to-use toolkits available  Policy  Current retirement rate  Increased reliance on external sources

10. Technical Challenges  Big Data  Capture  Analysis  Presentation  Access  Discovery  Data lifecycle and long-term protection  New sources and types of data

11. Technical Approaches  Parallelization and federation  Bring tools to data, not data to tools  Design for cloud  New software frameworks

12. Technical Approaches Repo 1 Repo n

13. Integrated Data Management Network  Delivered a Roadmap to Interoperability, user support, and training  Provide project tracking and sharing options  Established open-source projects  Cataloged tools  Demonstrated and shared data visualization, processing, and access capabilities  Built a community that can help address these future challenges