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Business Insight Services
Where Does Location Intelligence Fit in An Enterprise Data Mining/BI Strategy?
Tim Pletcher [email protected]
2Business Insight Services 2005 © CMU Research Corporation
A Broad Definition of 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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Common Applications for BI
HEALTHCARE
MARKETINGMANUFACTURING
$$
FRAUD DETECTION
$
BANKING & FINANCERESEARCH & DEVELOPMENT
Food Mart
RETAIL
LOGISTICS & SUPPLY CHAIN
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Value Creation
Data Information Knowledge Insight Action
Ret
urn
On
Inve
stm
ent
& V
alu
e
StandardReports
Ad hoc Reports& OLAP
Trend Statistics
Predictive Modeling
Future Impact
Analysis
Raw Data
Time When
Spatial Where
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Reporting
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LI Inspired Data for Business Intelligence
Aerial/Imagery Data
Street and Cartographic Data
Census Geography and Data
Customer Data
Competitor Data
Store Location Data
• Census/Postal Geography
• Street Networks
• Demographics
• Spatial Segmentation
• Aerial Photos and Land Use Data
• GPS & RFID captured/fed updates– Consumer Expenditure Data
– Retail transactions
– Market Potential Data
– Shipping volumes
– Utility usage
– Traffic Counts
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Unique Spatial Techniques
• 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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Advanced Visualization
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Location Intelligence is Evolving with BI
Desktop Tools & Data
Client/Server Systems
Projects
Departmental
EnterprisePlatform
Web Services Networks
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Web Browser
Web Server
Application ServerWeb Server
Application Server
Database Server
Connectivity
LayerHTTP/SOAP
Presentation Layer
•JSP/Java Servlets/JSP Tag Libraries•BSP/BSP Extensions
Business Layer
ABAP/J2EE
Persistence
LayerJDBC/Open SQL
Integration Layer
• Java Connector• .NET Connector• GBC• BC• XML• SOAP• BAPI/RFC• XI• Other ……..
Tier 3
Tier 2
Tier 1
SAP Web Application Server
Embedded Solutions: e.g. SAP Integration
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Multiple Solutions That Span the Enterprise
ViewsViews
AnalysisAnalysis
Mission CriticalMission CriticalApplicationsApplications
ProductsProducts
Updates &Updates &TransactionsTransactions
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Enterprise Technology Adoption
Well Understood Not Well
Understood
Economies ofScale
Emerging Technology
Enterprise Business Unit
BI LI
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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 and applied
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Models/Homes for a BI Competency Center
• Possible Structures or Organization Homes– 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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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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Predictive Modeling
Past Future
3 6 9 1212 9 6 3
Trained Modelfrom historical
data
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Combining LI and BI
Weather
CENSUS
SOILSTRANSACTION
DATA
GISManipulated
DATA
ProcessedTransaction
Data
SCORED DATA
NewInsight
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Actual Claims History
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Predictions
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Results
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Contact Information
THANK YOU!
Timothy A Pletcher
Director of Applied Research
Central Michigan University Research Corporation
Phone: (989) 774-2424
