analytics and reporting context linkedin final

Enterprise Information Architecture Analytics and Reporting Context

Dennis Crow

Enterprise Information Architect

Kansas City, MO

March 17, 2013

2 Copyright, Dennis Crow, 2013

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Enterprise Information Architecture • Is a synthesis of analytical requirements and the

capabilities of data management.

• is the result of data, not data itself. Information is the outcome data users’ methods and interpretation. Information can be used as data for other operations.

• Recognizes that the stakeholders of the information, not the systems, are the paramount audience.

• Acknowledges the Business Intelligence audience’s needs may be significantly different from the data analyst’s needs.

• Accounts for any presentation of data must convey the type of information sought, not just raw data.

• Assumes that stakeholders interest, sense of importance, and involvement will vary by the complexity end product, technology, and cost.

• Understands that stakeholders readiness for analytics depends on their overall maturity to use information.

Information Architecture Systems • Account for and anticipate the needs for data

elements and formats needed by the intended users

• Support an information supply chain plus the data management life cycle.

• Anticipate that decisions about systems are not just decision support systems, they are components of a decision that has perhaps already been harmed by the choice of technology.

• Articulate how technology chosen is not a neutral contributor to the information desired.

• Understand that geospatial data and technology is not a separate discipline or practice from analytics and evaluation and general.

• Foresees that the deployment of geospatial technology must fit with the overall enterprise architecture of a solution.

Copyright, Dennis Crow, 2013

Simplified view of relationships among Analytics stakeholders


Data Warehousing, Analytics, and Performance Measurement


1. Interpretation of action required: •Make improvements actually for 4 million acres •Create quantitative method to measure improvements •Create and implement method and metrics to assess improvements. •For 2-4 Pilot (anywhere, not matter what conditions?) •What is required of agency cooperation • What is expected to define “outcome” 2. Data requirements: •What laws or regulations govern the HIT practices now? •Existing data on conditions of water resources, what 4 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

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?

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?

Accelerate the protection of clean, abundant water resources by implementing targeted practices through ….on 4 million acres within critical and/or impaired watersheds. By …(date)………. quantify improvements in water quality by developing and implementing an interagency outcome metric…

Performance Objective Transformation into analytics capability


Generalized View of Analysis Process


Report Types * BI Application (OBIEE;Cognos; Business Objects;, ect.)

Data Linked Analytic Tool (SAS – OBIEE-R; Cognos-SPSS)

Snapshots, etc. (SAS – Excel)

Summary x x x

Quantitative Research

x x

Case Studies x

Metadata x x x

* Geospatial data can be used in any of these contexts

Information Presentations and Data Sources

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Syst

em C

om

ple

xity

Analytical Complexity

Dashboard; Data Warehouse, Normalized, Cube, Aggregated summary data

Report: Mart. Cube, Snapshot, Disaggregated detail data

Dashboard; Data Mart, Cube, Aggregated summary data


Copyright, Dennis Crow, 2013 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.


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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Geographic Information System Overarching Decision Matrix

Earth Geometry and Geodesy

Positioning and Location

Data

Management

Data Production and Acquisition

Programming and Software

Development

Photogrammetry and Remote Sensing

GIS System Configuration

Solution

Architecture

Analysis and Modeling

Co

mp

eten

cy, C

om

ple

xity

, Co

st

Technology Information

Enterprise Architecture and Strategy


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.


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.


Contact: Dennis G. Crow, Ph.D., PMP Independent Writing Email: [email protected] 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.

mailto:[email protected]

analytics and reporting context linkedin final

Technology

existing data

data requirements

data warehousing

result of data

presentation of data

raw data

geospatial data andimportance

existing standards data