Answering Where and Why: Location intelligence in Enterprise Data Mining

Transcription

1 Answering Where and Why: Location intelligence in Enterprise Data Mining Tim Pletcher

2 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 2

3 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. 3

4 Value Creation Return On Investment & Value Raw Data Standard Reports Ad hoc Reports & OLAP Trend Statistics Predictive Modeling Time When Future Impact Analysis Spatial Where Data Information Knowledge Insight Action 4

5 Predictive Modeling 5

6 Common Applications for BI 6

7 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. 7

8 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 8

9 Combining Data Mining and GIS 9

10 Actual Claims History 10

11 Predictions 11

12 Results 12

13 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 13

14 GEO Information Census/Postal Street Geography 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 Consumer Expenditure Data Customer Data Retail transactions Market Potential Data Shipping volumes Competitor Data Utility usage Traffic Counts Store Location Data 14

15 Advanced Visualization 15

16 The Road to the Enterprise Service Bus Client/Server Systems Web Services Networks Desktop Tools & Data Departmental Enterprise Platform Projects 16

17 Mash Ups 17

18 What's business layer services will be on the bus? Customer Analytics? Site Selection? Supply Chain Logistics? Performance Management? Enterprise Service Bus 18

19 Who and How Will These Services Be Managed? Database & Services Data access & Report Viewing Authoring & Analysis 19

20 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 20

21 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 21

22 Enterprise Technology Adoption Economies of Scale Enterprise Business Unit Emerging Technology BI GEO BI Well Understood Not Well Understood 22

23 Contact Information THANK YOU! Timothy A Pletcher Director of Applied Research Central Michigan University Research Corporation Phone: (989) tim.pletcher@cmich.edu 23