This is an overview of the context of information architecture

1. Enterprise Information Architecture
Analytics and Reporting Context
Dennis Crow
Enterprise Information Architect
Kansas City, MO
March 17, 2013

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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.
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5. Simplified view of relationships
among Analytics stakeholders
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6. Data Warehousing, Analytics, and Performance Measurement
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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 analysis
3. 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?
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8. Generalized View of Analysis Process
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9. Information Presentations and Data Sources
Report Types * BI Application Data Linked Snapshots, etc.
(OBIEE;Cognos; Analytic Tool (SAS – Excel)
Business Objects;,
ect.) (SAS – OBIEE-R;
Cognos-SPSS)
Summary x x x
Quantitative x x
Research
Case Studies x
Metadata x x x
* Geospatial data can be used in any of these contexts
Dashboard; Data Warehouse, Normalized,
Cube, Aggregated summary data
System Complexity
Dashboard; Data Mart, Cube,
Aggregated summary data
Report: Mart. Cube, Snapshot,
Disaggregated detail data
Analytical Complexity
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10. With regard to geospatial data, systems, and analysis, leadership’s interest in and support for
technology may vary according to their competency in non-traditional uses of GIS. The traditional
earth 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 social
science. Conversely , the use of GIS requires more than traditional conceptions of social science.
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11. Geographic Information System Readiness for Leadership
Leadership is going to view the importance of geospatial solutions in placed-based evaluation
depending on the competency of the organization as a whole for GIS and program evaluation. It is
rare that geospatial solution developers and social science trained analysts communicate about
information architecture’s dependence on both. Social science oriented research has been the sine
qua non of public and business evaluation perhaps now combined with simple geocoded addresses of
clients or customers.
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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 Solution
Competency, 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
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13. Measuring analytical maturity must take into account the breadth of data management and information delivery
or, said differently, how analytical capability leads the needs for data management. This entails the inclusion of
structured, unstructured, and geospatial data together in all phases.
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14. Contact:
Dennis G. Crow, Ph.D., PMP
Independent Writing
Email: dcrow1953@gmail.com
Phone: 816.214.8738
Address: 4768 Oak Street, #526
Kansas City, MO 64112
Dennis Crow is the Enterprise Information Architect for USDA’s Farm Service Agency. The
views expressed here are his own and not of USDA. This is an independent scholarly
composition.
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