competing on analytics
DESCRIPTION
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.TRANSCRIPT
Gregory Seltzer
Business Analytics Partner Manager
Agenda
What is Big Data? What is Business Analytics?
Four Pillars5 StrategiesImportance of AnalyticsInternal ProcessesExternal ProcessesCase Studies
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
VarietyData in many
forms
Veracity
Data in doubt
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
Four Pillars of Analytical Competition
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.
Competing on Analytics Stages
Stage Distinctive Capability/Level of Insights
Questions asked Objective Metrics/Measure/value
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
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
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
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
Roadmap to Becoming an Analytic Competitor
Stage
1
Analytically
Impaired
An organization has some data and management interest in analytics Stag
e
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
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
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?