Case Study EMPOWERING BUSINESS DECISIONS WITH FME WORKBENCH

Case Study: EMPOWERING BUSINESS DECISIONS WITH FME WORKBENCH Eric Klein Senior GIS Consultant, Critigen Bill Bodinson Senior GIS Analyst, Critigen

Agenda § § § § Introductions Project Background Defining Business Drivers Solution Design Unique Approaches Challenges Benefits

Introductions § Critigen came from CH 2 M HILL, a 63 -year old, $6 B global engineering and construction business § After 10 years as CH 2 M HILL’s sole information solutions provider EMS (Critigen) grew to more than 650 consultants worldwide § Three primary business competencies: Spatial Information Management, Managed IT Services and Systems Integration

Project Background § Large landholding customer § Looking to maximize owners return on assets § Divest underperforming assets § Acquire undervalued assets § Looking to diversify revenue stream

Define Business Drivers § Series of workshops § Identify stakeholders § Identify business opportunities/strengths § Decision analysis tools Contributions to Business Opportunity from Level: Criteria 0. 9 0. 8 0. 7 0. 6 0. 5 0. 4 0. 3 0. 2 0. 1 0. 0 Successful Business Model Leadership Level Client Relationships Related to Core Business Competitive Advantage Barrier to entry Others

Define Business Drivers § Identified over 100 opportunities § Screened down to 40

Solution Design § Worked with industry experts to define criteria for all 40 opportunities Factor Proximity to Major Population Area Proximity to Military Base Proximity to Major Highway High Potential Indicator Proximity to High Tech or Media Savvy Users High Potential Indicator Criterion Within 6 miles of a population center of 500, 000 or more Within 6 miles Within 100 miles of population center of 500, 000 or more High Potential Indicator Within 6 miles of a town with more 20, 000 or more people, but over 150 miles from a population center with 500, 000 or more 2 Medium Potential Indicator Within 6 miles of a town with 1, 000 to 10, 000 people, but over 150 miles from a population center of 500, 000 or more 1 Medium Potential Indicator Within 6 miles of an affluent census track, as defined by the US Census Bureau 1 Medium Potential Indicator Within 5 miles of a coast, or 6 miles of a National Park Service park, monument, or battlefield 1 Proximity to Area with Many Entrepreneurs and Small Businesses without Access to Broadband Proximity to Small Towns that are Far from Metropolitan Areas Proximity to Affluent Users Proximity to Major Tourism Area Criterion Usage High Potential Indicator - Applicable Proximity to Existing Towers only if 1 of the other 8 are met. No existing tower within 6 miles. Note: Total possible score is 15. Five goodness of fit categories (0, 1 -3, 4 -7, 8 -11, 1215) 0 -Poor 1 -3 - Fair 4 -7 - Good 8 -11 - Better 12 -15 - Best Red Yellow Light Green Medium Green Dark Green Points 2 2 2

Solution Design § End result was a very large, complex model

Solution Design § Used FME Workbench capabilities to simplify and navigate the model § Linked tables § Extensive bookmarking § Modular Design State Alabama Alaska Arizona Arkansas Net Hunting Economi Hunting Days c Value Per Day Number of Days per Hunting Square for Deer State Days Mile Hunting Area (Sq. Miles) (2006) RANK (2001) 52, 419. 02 8, 649, 000 663, 267. 26 854, 000 113, 998. 30 1, 509, 000 165 7 1 50 13 46 67 148 11 36 21 42 23 92 263 39 25 2 3, 769, 000 57 32 8, 228, 000 420, 000 138 38 14 34 2, 117, 000 25 38 4, 688, 000 81 27 53, 178. 62 7, 882, 000 163, 695. 57 3, 376, 000 104, 093. Colorado 57 2, 376, 000 Connecticut 5, 543. 33 509, 000 Delaware 2, 489. 27 654, 000 California Florida Georgia Hawaii Idaho Illinois 65, 754. 59 59, 424. 77 10, 930. 98 83, 570. 08 57, 914. 38 110 49 30 54 Net Economi Number of c Value Hunters Per Day Number of (2006) Expenditu for Deer includes Expenditure res for Hunters Hunting (2006) res and s for Hunting (2001) includes res non-res Hunting (2006) RANK and non-res RANK (2006) RANK $678, 024, 00 391, 000 11 1 0 10 $125, 112, 00 71, 000 44 $322, 739, 00 159, 000 36 11 0 24 $788, 575, 00 354, 000 12 33 0 8 $813, 239, 00 281, 000 18 0 7 $444, 061, 00 259, 000 23 0 18 24 38, 000 48 $68, 530, 000 47 42, 000 47 $41, 381, 000 48 $377, 394, 00 236, 000 27 0 22 $677, 762, 00 481, 000 9 37 0 11 18, 000 49 $21, 098, 000 49 $259, 718, 00 187, 000 30 0 31 $381, 937, 00 316, 000 14 16 0 21

Solution Design § Among the 1000+ transformers were many of FMEs powerful ETL capabilities

Solution Design § Built individual modules in FME Workbench to apply rules for each business

Unique Approaches § Modules can be run somewhat autonomously based on documented dependencies

Unique Approaches § Extensive annotation within each module § Highlighted parameters to quickly adjust rules and inputs to perform scenario and sensitivity analysis

Unique Approaches § Where possible, results were compared against known outcomes using statistical analysis

Challenges § Focusing effort on high yield opportunities § Availability of input data § Rules that varied by geography § Always looking to test outcomes § Data maintenance

Business Benefits § Ability to quickly screen existing assets for utilization – adjust management or divest § Quickly screen potential acquisitions for undervaluation § Ability to identify data gaps, capture that data and improve the tool

Technical Benefits § Repeatable, testable toolset that the customer can maintain § Flexible architecture: § Simple updates of input data § Adjustable rules as economic conditions change § Ability to identify data gaps, capture that data and improve the tool

Thank You! QUESTIONS? More information: Eric Klein: eric. klein@critigen. com www. critigen. com