Analytics and reporting context linkedin final


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Analytics and reporting context linkedin final

  1. 1. Enterprise Information Architecture Analytics and Reporting Context Dennis Crow Enterprise Information Architect Kansas City, MO March 17, 2013
  2. 2. Copyright, Dennis Crow, 2013 2
  3. 3. Enterprise Information Architecture Information Architecture Systems• Is a synthesis of analytical requirements and the • Account for and anticipate the needs for data capabilities of data management. elements and formats needed by the intended users• is the result of data, not data itself. Information is the outcome data users’ methods and interpretation. • Support an information supply chain plus the Information can be used as data for other operations. data management life cycle.• Recognizes that the stakeholders of the information, • Anticipate that decisions about systems are not the systems, are the paramount audience. not just decision support systems, they are components of a decision that has perhaps• Acknowledges the Business Intelligence audience’s already been harmed by the choice of needs may be significantly different from the data technology. analyst’s needs. • Articulate how technology chosen is not a• Accounts for any presentation of data must convey the neutral contributor to the information type of information sought, not just raw data. desired.• Assumes that stakeholders interest, sense of • Understand that geospatial data and importance, and involvement will vary by the technology is not a separate discipline or complexity end product, technology, and cost. practice from analytics and evaluation and general.• Understands that stakeholders readiness for analytics depends on their overall maturity to use information. • Foresees that the deployment of geospatial technology must fit with the overall enterprise architecture of a solution. Copyright, Dennis Crow, 2013 3
  4. 4. Copyright, Dennis Crow, 2013 4
  5. 5. Simplified view of relationshipsamong Analytics stakeholders Copyright, Dennis Crow, 2013 5
  6. 6. Data Warehousing, Analytics, and Performance Measurement Copyright, Dennis Crow, 2013 6
  7. 7. 1. Interpretation of action required: •Make improvements actually for 4 million acres •Create quantitative method to measure Performance Objective improvements Transformation into analytics capability •Create and implement method and metrics to assess improvements. Accelerate the protection of clean, abundant •For 2-4 Pilot (anywhere, not matter what water resources by implementing targeted conditions?) practices through ….on 4 million acres within •What is required of agency cooperation • What is expected to define “outcome” critical and/or impaired watersheds. By 2. Data requirements: …(date)………. quantify improvements in water •What laws or regulations govern the HIT practices now? quality by developing and implementing an •Existing data on conditions of water resources, what 4 interagency outcome metric… watersheds, what sampling method for pilot? Spatial or quality or both? •Define “protection ” •Get spatial data on watersheds (already exists) •Reconcile existing standards data from agencies •What existing metrics are there against which to measure “accelerate” •What databases and data must be reconciled and formatted and shared for analysis3. Process Requirements: •What is the nature of the collaborative process? •What database and analysis tools are available in a standard way? •What collaborative tools are commonly available?4. Review and Reporting Requirements •What agency has the lead for reporting? •What is the unique process for the 3 agencies •Narrative, tables, maps would be the content? • What is the process for review b y the three agencies? Copyright, Dennis Crow, 2013 7
  8. 8. Generalized View of Analysis Process Copyright, Dennis Crow, 2013 8
  9. 9. Information Presentations and Data SourcesReport Types * BI Application Data Linked Snapshots, etc. (OBIEE;Cognos; Analytic Tool (SAS – Excel) Business Objects;, ect.) (SAS – OBIEE-R; Cognos-SPSS)Summary x x xQuantitative x xResearchCase Studies xMetadata x x x * Geospatial data can be used in any of these contexts Dashboard; Data Warehouse, Normalized, Cube, Aggregated summary dataSystem Complexity Dashboard; Data Mart, Cube, Aggregated summary data Report: Mart. Cube, Snapshot, Disaggregated detail data Analytical Complexity Copyright, Dennis Crow, 2013 9
  10. 10. With regard to geospatial data, systems, and analysis, leadership’s interest in and support fortechnology may vary according to their competency in non-traditional uses of GIS. The traditionalearth or land based approach to GIS solutions may be more familiar, but is not adequate to place-based evaluation. Place-based evaluation requires additional knowledge of statistics and socialscience. Conversely , the use of GIS requires more than traditional conceptions of social science. Copyright, Dennis Crow, 2013 10
  11. 11. Geographic Information System Readiness for LeadershipLeadership is going to view the importance of geospatial solutions in placed-based evaluationdepending on the competency of the organization as a whole for GIS and program evaluation. It israre that geospatial solution developers and social science trained analysts communicate aboutinformation architecture’s dependence on both. Social science oriented research has been the sinequa non of public and business evaluation perhaps now combined with simple geocoded addresses ofclients or customers. Copyright, Dennis Crow, 2013 11
  12. 12. Geographic Information System Overarching Decision Matrix Overall, Enterprise Architecture that embraces the complexity of technology and information, GIS and research methods, data management and information delivery will be successful with analytics. Enterprise Architecture and Strategy Earth Geometry and Positioning and SolutionCompetency, Complexity, Cost Geodesy Location Architecture Programming and Data Data Production and Software Acquisition Management Development GIS System Photogrammetry and Analysis and Configuration Remote Sensing Modeling Technology Information Copyright, Dennis Crow, 2013 12
  13. 13. Measuring analytical maturity must take into account the breadth of data management and information deliveryor, said differently, how analytical capability leads the needs for data management. This entails the inclusion ofstructured, unstructured, and geospatial data together in all phases. Copyright, Dennis Crow, 2013 13
  14. 14. Contact:Dennis G. Crow, Ph.D., PMPIndependent WritingEmail: dcrow1953@gmail.comPhone: 816.214.8738Address: 4768 Oak Street, #526Kansas City, MO 64112Dennis Crow is the Enterprise Information Architect for USDA’s Farm Service Agency. Theviews expressed here are his own and not of USDA. This is an independent scholarlycomposition. Copyright, Dennis Crow, 2013 14