Answering Where and Why: Location
intelligence in Enterprise Data Mining
Tim Pletcher pletc1ta@cmich.edu
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Overview
• Business Intelligence as an Umbrella concept
• An example of why including spatial/location
from the start leads to more actionable answers
• The road to the Enterprise Service Bus
• The business intelligence competency center
• It’s time to fully engage the IT organization
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Analytical Convergence: Business Intelligence
• CMU-RC uses the Data Warehousing Institute’s
definition of Business Intelligence (BI) to gain insight
from data for the purpose of taking action.
• This definition encompasses the broad suite of
business analytics: predictive modeling, data or text
mining, geographic information systems, statistical
analysis, operations research, systems dynamics,
simulation, and advanced data visualization.
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Return On Investment & Value
Value Creation
Future
Impact
Analysis
Predictive
Modeling
Trend
Statistics
Ad hoc
Reports
& OLAP
Raw
Data
Data
Standard
Reports
Information
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Time ÆWhen
Spatial Æ Where
Knowledge
Insight
Action
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Predictive Modeling
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Common Applications for BI
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One Example
Scenario:
A large company wanted to understand their
risk related to warranty on a product.
• Previous attempts using traditional analysis continued to
miss the mark each quarter (by many millions of $).
• There was a physical driver for the defect (moisture, soil
permeability, temperature, etc.)
• There was a people driver for the claim rate (once it
started there was a claim “fad”)
Result:
A robust forecast using neural networks to
score the data and predict the amount of
claims that would occur during the warranty
period.
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Model Results
The company had three groups do modeling. All
produced the bottom line result with fairly close
estimates.
Example:
$ XXX,XXX,XXX of future warranty expenses can
expected to occur during the remaining warranty
period for the product.
This result has a 98% confidence interval within
$ YYY,YYY,YYY and $ ZZZ,ZZZ,ZZZ
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Combining Data Mining and GIS
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Actual Claims History
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Predictions
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Results
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Unique Spatial Techniques
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•
•
•
•
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Market Area Boundaries
Drive Times
Desire Lines
Market Penetration
Site Selection
Gravity Models
ETL for spatial data (Soils volumes/zip to census)
Spatial Queries
– E.g. based on Demographic or Household Data
• Spatial Statistics
• Networks and Process Maps
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GEO Information
•
•
•
•
•
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Census/Postal
StreetGeography
and Cartographic Data
Street Networks
Demographics
Aerial/Imagery Data
Spatial Segmentation
Aerial Photos and Land Use Data
Census Geography and Data
GPS & RFID captured/fed updates
–
–
–
–
–
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Consumer Expenditure Data
Customer
Data
Retail
transactions
Market Potential Data
Shipping volumes
Competitor Data
Utility usage
Traffic Counts
Store Location Data
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Advanced Visualization
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The Road to the Enterprise Service Bus
Web Services
Networks
Client/Server
Systems
Desktop Tools &
Data
Departmental
Enterprise
Platform
Projects
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Mash Ups
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What's business layer services will be on
the bus?
Customer
Analytics?
Site Selection?
Supply Chain
Logistics?
Performance
Management?
Enterprise Service Bus
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Who and How Will These Services Be
Managed?
Data access &
Report Viewing
Database &
Services
Authoring & Analysis
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Enter the BI Competency Center
• A BI Competency Center is a group chartered to
advocate and bolster the adoption of BI in the
enterprise.
• Some specific charters
– Generate awareness for executives and line managers about
the competitive advantage and ROI
– Inter Silo-data sharing
– Establish standards and methodologies
– Raise the alarm about the need for data quality
– Ensures that quality analytics are applied
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Models/Homes for a BI Competency Center
• Possible Structures or Organization Homes
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Project management offices
Six Sigma & Continuous Quality Improvement
Repurposed Operations Research Teams
Newly constructed teams at strategic level or in IT
• Key Team Characteristics
– Understands the business drivers
– Can work with a process and get results
– Ability to apply technology, but recognizes it is not about
technology
– Quantitatively competent.. Including spatial analysis
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Enterprise Technology Adoption
Economies of
Scale
Enterprise
Business Unit
BI
Well
Understood
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Emerging
Technology
GEO
BI
Not Well
Understood
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Contact Information
THANK YOU!
Timothy A Pletcher
Director of Applied Research
Central Michigan University Research Corporation
Phone: (989) 774-2424
tim.pletcher@cmich.edu
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