Introduction to Location Intelligence and Geospatial Analysis, presented at TDWI Boston in July 2015.

2. TDWI Boston - July 2015 2
@gregbonnette
linkedin.com/in/gbonnette
Greg Bonnette
Vice President, Strategy & Innovation

3. TDWI Boston - July 2015 3

4. TDWI Boston - July 2015 4
Enterprise Information Advisory
Professional Education
On-Shore Remote Development
Business
Intelligence
Information
Management
Advanced
Analytics
Performance
Management
Enterprise Information Platform Hosting & Management

5. 1999
Our Founding Year
75+
Employees in 7 Regions
Across North America
#1
IBM BA Partner in
North America 2013
#546
2014 Inc. 5000
#20
2014 Best Places to
Work in IT
500+
Happy Clients
5TDWI Boston - July 2015

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Source: DUPress.com

8. TDWI Boston - July 2015 8
Location
Intelligence
Spatial Reference
•Geography (GIS)
•Streets
•Parcel Boundaries
•Floor & Building Plans
Enterprise
Data
•Reference
•Transaction
•Sensor
External
Data &
Services
•Demographic
•Environmental
•Social
Business
Intelligence
Predictive
Analytics
Prescriptive
Analytics

9. TDWI Boston - July 2015 9
 Geographic features
 Down to the parcel level
 Established technology with
specialized skills
 Pitney Bowes, ESRI
 Location-based features
 Floor plans, venue maps, store
layouts
 Often paired with BI
 Pitney Bowes, Google, IBM Presence
Insights, OpenStreetMaps
Geographic Information Systems (GIS) Indoor/Outdoor Location Mapping
Source: Geographic Information System Basics v1.0 Source: IBM

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 Master Data (Customer Address, Location Address)
 Transactions (Location Address, IP Address)
 Sensor Data (Bluetooth, In-vehicle GPS, M2M, IoT)
Other Enterprise
Systems of Record

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Address Validation Geocoding
Economic &
Market Index Data
Census
Geodemographics
Demographic
Actuals and
Projections
Consumer
Expenditures
Purchasing Power
Points of Interest
Routing and Drive
Times
Tax & Parcel
Boundaries
Social Data (Both
Check-In and
Profile Data)

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John C. Smith, Jr.
1852 Main Avenue
Downey, CA 90240-3245
John Smith Jr.
1852 Main Street, Suite
205
Downey, CA 90240-3245
John Smith III
1852 Main ST
Downey, CA 90241
John C. Smith, Jr.
1852 Main Street, Suite
205
Downey, CA 90240-3245John C. Smith, Jr.
1852 Main Street
Downey, CA 90240-3245

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Street-Level Geocoding Parcel-Level Geocoding
Be sure to select the appropriate level of accuracy for your use case
Going from Addresses to Coordinate Points (e.g. Lat. and Lon.)
VS.

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Demographic
What is it?
Raw or Integrated Census
Data
Sample Data Count of 18-25 Year Olds
Example Usage Retail Site Selection
Geodemographic
Consumer Segmentation
based on types of
individuals in a
neighborhood
Category 1A High Society
Families
Customer & Marketing
Analytics

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Retail Destinations
Consumer Expenditure
Purchasing Power

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Retail Telco Pharma Insurance Banking
Manufacturing Health Care Utilities Oil & Gas Education
Marketing Sales
Customer
Service
Risk Finance
Human
Resources
Supply Chain
Product
Design

20. TDWI Boston - July 2015 20
MAC: 00:0a:95:9d:68:16 MAC: 00:0a:95:9d:68:16

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$145.8MM
GROSS WRITTEN PREMIUM
$26.8B
TOTAL INSURED VALUE
236.3K
POLICIES
470K
SQUARE MILES
600
400
200
$50K $5MM
POLICY INSURED VALUES
PRODUCT MIX
Personal
Home
25%
Personal Auto
25%
Commercial
50%
APPLY RESET
MAP FILTER JULY 1, 2015 – JULY 14, 2015 EXPORT DETAIL

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RISK STATEEVENT DATE & TIME
APPLY RESET
MAP FILTER JULY 1, 2015 – JULY 14, 2015 EXPORT DETAIL
EVENT INFORMATION
RISK TYPE
JULY 1, 2015 JULY 14, 2015
T-STORM TORNADO FLASH FLOOD HAIL FIRE EARTHQUAKE
WARNING STORM REPORT STORM SIGNATURE
17 MATCHING EVENTS
INSURANCE LINE
PERSONAL HOME
PERSONAL AUTO
COMMERCIAL
SPECIALTY RISK
OK AK OH IL
RISK EVENT POLICY

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Opportunity
 Forecast crime occurrence down to the street
segment by shift
 Proactively plan patrol routes through hot spots at
the patrol grid level to prevent crime
Data & Analytics
 Near real-time prediction based on history,
geography, weather and more
 Planned integration within GPS-enabled patrol
mobile data terminal (MDT) for ease of use
 IBM SPSS Modeler 17 with Spatio-temporal
Prediction

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• Last item browsed
• Discount response
• Passion for Eco-friendly
products
• SMS offer response rate
• PZ focused shopper-N
10% Sample Predict Persona Next
PERSONA_NEXT_BEST_AC
Predict Persona Next Format Scored Data PERSONA_NEXT_BEST_AC
Opt-in
Receive 10% off select Bamboo cutting
boards. Valid today only!
Receive 10% off select
Bamboo cutting boards.
Valid today only!
Product, Promotion
Availability
Powered by IBM Predictive Customer Intelligence

25. TDWI Boston - July 2015 25
Opportunity
 Optimize show content for audience demographics
and interest to drive membership and viewership
Data & Analytics
 Integrate viewing behaviors for 25M members with
other transactional data such as ratings and
searches
 Combine with geolocation, time and viewing device
info
 Integrate ratings and social media sources
Results
 Plot lines, set design and casting influenced by
results of cohort analysis
 Critically acclaimed series with over a dozen awards
& 3MM opening weekend views
Source: Gartner

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Opportunity
 Improve asset efficiency and driver safety
Data & Analytics
 Telematic sensors in 46k delivery vehicles capturing operational
data and characteristics
 Integrated Navigation and Driving Recommendation System
Results
 Saving 8.4M gallons gasoline / year, 85M fewer miles traveled
 $50M in annual savings
 Increased productivity by 35%
 Doubled driver wages
Source: Gartner

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Internal Drivers
 Age
 Gender
 Tenure
 Recent Usage
 Inactivity
 Price
 Plan Amenities

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Demographic
• Age
• Gender
Behavioral
• Tenure
• Recent
Usage
• Inactivity
• Price
• Plan
Amenities &
Features
Lifestyle
• Social Media
• 3rd Party
Consumer
Data
Geographic
• Address
• Drive Time
• Competitive
POI’s
• Geodemo.

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Cleanse &
Standardize
Address
Geocode
Calculate
Driving
Routes
Calculate
Drive Time
Count
Competitor
Points of
Interest
Look-up
CAMEO
IRONSIDE
10 MAGUIRE RD
BLDG 4
LEXINGTON MA 02421
IRONSIDE
10 MAGUIRE RD STE 400
LEXINGTON MA 02421-3135
POINT(42.471497,-
71.2625469)
3 Routes
Average 17
Minutes With
Weekday
Afternoon Traffic
3 Competitors
Closer by Drive
Time
4C – Metro Success

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Mapping
• GIS
• Local / Indoor
• CAD
• Multi-Medium
Internal Data
• Master Data
• Transactions
• Behavioral
IoT/Device Data
• WiFi, Cell, BLE
• Sensors
• GPS & LPS
• Tracking & Fencing
External Data
• Demographic
• Geodemographic
• POI
• Social
• Weather
Spatial Data Warehouse
Spatial ProcessingComplex Event / Stream
Processing Address Cleansing, Geocoding, Routing
Map Layers & Theming
Advanced Analytics Business Intelligence Operational Intelligence
Data Mining, Prediction,
Data Science
Visualization, Exploration &
Reporting with Embedded Mapping
REST APIs
Business Process Integration,
Automation & Optimization

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MappingInternal DataIoT/Device Data External Data
Spatial Data Warehouse
Spatial ProcessingCEP / Stream Processing
Advanced Analytics Business Intelligence Operational Intelligence
REST APIs
• Flat Files
• ERP, CRM, POS
• Relational
Databases

32. TDWI Boston - July 2015 32
https://www.census.gov/geo/maps-data/data/tiger-line.html
https://www.census.gov/acs/www/data/data-tables-and-tools/
https://www.census.gov/2010census/data/Shapefiles and Spatial
Reference Data
Point in time (10 year) population
measurement for the purpose of
Congressional apportionment
Continuous measure of the
changing social and economic
characteristics of the population

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Internal Data
CUSTOMER_ID
CUSTOMER_NAME
CUSTOMER_ADDRESS
CUSTOMER_LAT
CUSTOMER_LON
Census/TIGER Shape
BLOCK_ID
BLOCK_GROUP_ID
TRACT_ID
BLOCK_GEOMETRY
Census/ACS Data
TRACT_ID
MEDIAN_HH_INCOME
CUSTOMER_ID TRACT_ID MEDIAN_HH_INCOME

35. TDWI Boston - July 2015 35
SELECT
SUM(POLICY.TOTAL_INSURED_VALUE)
FROM
POLICY,
WX
WHERE
ST_WITHIN(POLICY.POINT, WX.EVENT_GEO)
AND WX.EVENT_NAME = ‘TORNADO_123’

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Forecast Property Values by Census Tract
Work directly with spatial and shape data, perform spatio-
temporal predictions and visualize results
Example: IBM SPSS Modeler 17

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 Brainstorm use cases for geo-spatial analysis and data
innovation
 Inventory sources of spatial or spatial-ready data in the
enterprise
 Identify low hanging fruit – opportunities to enrich
existing applications with LI
 Inventory and assess existing technology to support an
LI enterprise architecture
 Consider business ethics and privacy implications of
misapplications of LI in the enterprise

38. Spatial Analysis & Visualization
•GIS Map Management & Customization
•Intelligent BI Integration
Address Standardization
•Best-in-Class, Global, Multi-Database Resolution
Enterprise Geocoding
•Industry’s most accurate geocoding capabilities
Enterprise Routing
•Drive Time (with Traffic)
•Competitive Route Analysis
Consumer Data
•Over 5,000 Global Data Sets
•Demographics & Geodemographics
•Purchasing Power
•Consumer Expenditure
•Retail Destinations & POIs
TDWI Boston - July 2015 38
Geodemographic
Data for 1.5B
Consumers in 37
Key Markets
Geospatial
Mapping
Capabilities for
Cognos BI