Gregory Seltzer
Business Analytics Partner Manager
Agenda
What is Big Data? What is Business Analytics?
Four Pillars
5 Strategies
Importance of Analytics
Internal Proce...
It is all about InsightDimensions of big data
Every day, we create 2.5 quintillion bytes of data — so much that 90% of the...
CIO Survey – Key Drivers
 In a recent survey, these were the common levers that the most successful
companies used to dep...
Four Pillars of Analytical Competition
Four Pillars of Analytical Competition
 Analytical Competitors. “Analytical nirvana”
Use analytics across the enterprise ...
Competing on Analytics Stages
Stage Distinctive
Capability/Level of
Insights
Questions asked Objective Metrics/Measure/v
a...
Internal Processes
 Financial
 Dashboards & balanced scorecards
 Cost management & allocation
 Manufacturing
 Profit ...
External Processes
 Customer
 CRM
 Dynamic pricing
 Churn
 Econometric analysis for advertising & brand
 Google web ...
Hospitality Case Studies
 Harrah’s
 Strategic focus; Loyalty plus Service
 CEO: Gary Loveman – constantly pushes
entire...
Roadmap to Becoming an Analytic
Competitor
Stage
1
Analytically
Impaired
An organization has some data
and management inte...
Choosing a Strategic Focus
 Harrah’s: Loyalty plus service
 New England Patriots: Player
selection plus fan experience
...
Where to focus resources
 How can we distinguish ourselves in the marketplace?
 What is our distinctive capability?
 Wh...
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Competing on analytics

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Thomas Davenport has written numerous books, articles, and delivered presentations on "Competing on Analytics". He is considered by many the leading authority on the subject. I created this presentation to articulate many of the concepts he established in his book with the same title.

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Competing on analytics

  1. 1. Gregory Seltzer Business Analytics Partner Manager
  2. 2. Agenda What is Big Data? What is Business Analytics? Four Pillars 5 Strategies Importance of Analytics Internal Processes External Processes Case Studies
  3. 3. It is all about InsightDimensions of big data Every day, we create 2.5 quintillion bytes of data — so much that 90% of the data in the world today has been created in the last two years alone. This data comes from everywhere: sensors used to gather climate information, posts to social media sites, digital pictures and videos, purchase transaction records, and cell phone GPS signals to name a few. This data is big data. Volume Data at rest Velocity Data in motion Variety Data in many forms Veracity Data in doubt
  4. 4. CIO Survey – Key Drivers  In a recent survey, these were the common levers that the most successful companies used to deploy Big Data Analytics solutions.  Their key common themes of the leading companies leverage analytics as a component of their competitive advantage
  5. 5. Four Pillars of Analytical Competition
  6. 6. Four Pillars of Analytical Competition  Analytical Competitors. “Analytical nirvana” Use analytics across the enterprise as a competitive advantage.  Analytical Companies. “Good at analytics.” Highly data oriented, have analytical tools, and make wide use of analytics. Lack commitment to fully compete or use strategically  Analytical Aspirations. “Se the value of analytics.” Struggle mobilizing the organization and becoming more analytical  Localized Analytics. “Use reporting.” And analytics or reporting is in silos.  Analytically Impaired. “Not data-driven.” Rely on gut feel and plan to keep doing so. They aren’t asking analytics questions and/or lack the data to answer them.
  7. 7. Competing on Analytics Stages Stage Distinctive Capability/Level of Insights Questions asked Objective Metrics/Measure/v alue 1. Analytically Impaired Negligible, “flying blind” What happened in our business? Get accurate data to improve operations None 2. Localized analytics Local and opportunistic – may not be supporting company’s distinctive capabilities What can we do to improve this activity? How can we understand our business better? Use analytics to improve one or more functional activities ROI of individual applications 3. Analytical aspirations Begin efforts for more integrated data and analytics What’s happening now? Can we extrapolate existing trends? Use analytics to improve a distinctive capability Future performance and market value 4. Analytical companies Enterprise-wide perspecive able to use analytics for point advantage, know what to do to get to next level, but not quite there How can we use analytics to innovate and differentiate? Build broad analytic capability – analytics for differentiation Analytics are an important driver of performance and value 5. Analytic competitor Enterprise-wide, big results, sustainable advantage What’s next? What’s possible? How do we stay ahead? Analytical master – fully competing on analytics Analytics are the primary driver of performance and value
  8. 8. Internal Processes  Financial  Dashboards & balanced scorecards  Cost management & allocation  Manufacturing  Profit InSight  Manufacturing quality – Minitab & Spotfire DecisionSite  Configuration - FordDirect  R&D  Hypothesis testing, control groups, statistical  Vertex Pharmaceutical  Entelos – computational testing  Test & Learn – CapitalOne  Healthways improve health outcomes  Human Resource  HRIS – analytics for hiring  Sports Team management  Typical Analytical Applications  Activity-based costing (ABC)  Bayesian inference  Biosimulation  Combinatorial optimization  Constraint analysis  Experimental design  Future-value analysis  Monte Carlo simulation  Multiple regression  Neural network  Textual analysis  Yield
  9. 9. External Processes  Customer  CRM  Dynamic pricing  Churn  Econometric analysis for advertising & brand  Google web analytics  Tesco clubcard  Samsung M-Net  Anheuser-Busch - BudNet  Best Buy – customer interactions into sales  Jill Stores  Barry Stores – audiophile and video file - convenience  Supplier  Wal-Mart requires Retail-Link to track movement of products  Modular Category Assortment Planning  Amazon developed proprietary inventory modeling using non-stationary stochastic optimization  Optimize supply constraints: integral min-cost flow problem with side constraints.  Typical Analytical Applications in Marketing  CHAID  Conjoint analyisis  Lifetime value  Market experimentation  Multiple regression analysis  Price Optimization  Time series experiments  Typical Analytical Applications in Supply Chains  Capacity planning  Demand-supply matching  Location analysis  Modeling  Routing  Scheduling
  10. 10. Hospitality Case Studies  Harrah’s  Strategic focus; Loyalty plus Service  CEO: Gary Loveman – constantly pushes entire executive team to use testing and analysis, fact-based decisions.  Newly legalized gaming jurisdictions in the mid-1990s ground to a halt, Harrah’s managers realized that growth could no longer come from new casino’s  Customer loyalty and Service  Data to improve customer experience while streaming casino traffic.  Waiting customer is not spending. Bottlenecks occur at certain slot machines, they offer a customer a free game at a slot machine located at another part of the casino  Ho wling they sit at a different gaming tables, optimize the range, configuration of their games  Marriott’s  Revenue Management – optimal price for their rooms  Power to override the automated systems, example was Hurricane Katrina evacuees  Enterprise wide revenue management system called One Yield  Marriott rewards deploying a sophisticated Web analytics capability. Constantly doing tests to understand changes to their website.  Analytic group reports to office of the CIO
  11. 11. Roadmap to Becoming an Analytic Competitor Stage 1 Analytically Impaired An organization has some data and management interest in analytics Stage 2 Functional management builds analytics momentum and executives’ interest through appli8cation of basic analytics Managerial Support: Prove-it path Executives commit to analytics by align resources and setting a timetable to build a broad analytical capability Enterprise-wide analytics capability under development; top executives view analytics capablity as a corporate priorty Organization routinely reaping benefits of its enterprise-wide analytics capability and focusing on continuous analytics renewal Terminal stage: some companies’ analytics efforts never receive management support and stall here as a result Analytically Aspirations Analytically Companies Analytically Competitors Stage 3 Stage 4 Stage 5 Top management support: full- steam-ahead path
  12. 12. Choosing a Strategic Focus  Harrah’s: Loyalty plus service  New England Patriots: Player selection plus fan experience  Dreyfus Corporation: Equity analysis plus asset attrition  UPS: Operations plus customer data  Wal-Mart: Supply chain plus marketing  Owens & Minor: Inernal logistics plus customer cost reduction  Progressive: Pricing plus new analytical service offerings  Key Elements in Analytical Capability  Organization  Insight into performance drivers  Choosing a distinctive capability  Performance management and strategy execution  Process redesign and integration  Human  Leadership and senior executive commitment  Establishing a fact-based culture  Securing and building skills  Managing analytical people  Technology  Quality data  Analytic technologies
  13. 13. Where to focus resources  How can we distinguish ourselves in the marketplace?  What is our distinctive capability?  What key decisions in those processes, and elsewhere, need support from analytical insights?  What information really matters to the business?  What are the information and knowledge leverage points of the firms performance?

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