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Competing on Analytics: The New Science of Winning
Thomas H. Davenport, Jeanne G. Harris · 2007
In a sentence
A definitive guide showing how companies turn sophisticated data analysis into a distinctive, hard-to-copy capability that drives superior competitive performance.
Competing on Analytics reveals how a small but growing set of organizations have made data analysis the centerpiece of their strategy, outthinking and outexecuting rivals in industries from gaming to logistics to professional sports. Updated to cover big data, data science, machine learning, the Internet of Things, and cloud computing, Davenport and Harris lay out a five-stage maturity model, a four-pillar definition of analytical competitors, and the DELTA framework (Data, Enterprise, Leadership, Targets, Analysts) for building analytical capability. Through rich examples—Netflix, Capital One, Caesars, Marriott, UPS's ORION, Google, and the Oakland A's—the book demonstrates that extracting value from data is less about technology and more about leadership commitment, an enterprise-wide approach, a distinctive strategic focus, and the scarce human talent that makes analytics work. It is both a strategic argument and a practical road map for any executive seeking lasting advantage through fact-based decision making.
The four lenses
- Science
- Statistics
- Systems
- Strategy
The model
A causal model in which organizational design levers (leadership commitment, enterprise approach, data/technology infrastructure, analytical talent, strategic targeting) build an analytical capability that develops a distinctive capability and fact-based decision making, which in turn drive superior business performance and competitive advantage.
Senior Management Commitment to Analyticsdesign lever
The degree to which the CEO and senior executive team are passionate advocates for fact-based, analytical decision making, allocate resources, set the tone, and personally model analytical behavior across the organization.
Enterprise-Wide Approach to Analyticsdesign lever
The extent to which analytics resources (data, technology, analysts) are managed in a coordinated, enterprise-level fashion rather than in disconnected functional or individual silos, ensuring a single version of the truth and shared standards.
Data Quality and Technical Infrastructurecontextual condition
The availability of integrated, accurate, complete, current, consistent, and accessible data combined with a robust analytical and big data technical architecture that supports enterprise-wide analysis.
Analytical Talent and Peopledesign lever
The presence and effective management of skilled analytical professionals, data scientists, capable amateur analysts, and decision makers who can perform, interpret, and act on quantitative analysis.
Strategic Targeting of Analyticsdesign lever
Focusing scarce analytical investment on high-value business areas and ultimately on the organization's distinctive capability and most strategic, ambitious objectives.
Distinctive Analytics-Supported Capabilitybehavioral pattern
An integrated business process or capability—such as customer loyalty, pricing, supply chain optimization, or player selection—that is differentiated from competitors and serves as the organization's strategic formula for success, refined through analytics.
Fact-Based Decision Making and Analytical Culturepsychological state
The pervasiveness of decisions and actions grounded in data, statistical and quantitative analysis, predictive and prescriptive models, and a culture of experimentation and objectivity rather than intuition.
Analytical Maturity Stagebehavioral pattern
The organization's overall stage of analytical competition (1 analytically impaired through 5 analytical competitor) reflecting cumulative development of capability elements.
Business Performance and Competitive Advantageoutcome metric
Superior, sustainable financial and competitive outcomes including profit, revenue growth, shareholder return, market share, cost savings, and the ability to outthink and outexecute competitors.
How they connect
- senior management commitment → predicts fact based decision making
- senior management commitment → predicts analytical maturity
- enterprise approach → predicts analytical maturity
- data quality infrastructure → influences fact based decision making
- data quality infrastructure → predicts analytical maturity
- analytical talent → predicts fact based decision making
- analytical talent → predicts analytical maturity
- strategic targeting → predicts distinctive capability
- fact based decision making → influences distinctive capability
- distinctive capability → predicts business performance
- analytical maturity → correlates business performance
- fact based decision making → mediates business performance
A candidate measure
Competing on Analytics: The New Science of Winning — derived measurement candidates
Senior Management Commitment to Analytics
Frequency of analytics mentions in earnings calls/annual reports; Share of leadership communications referencing data; Multi-year analytics investment levels
self-report suitability: medium
Enterprise-Wide Approach to Analytics
Presence/scope of CDAO or analytics hub; Percent of analytics use rated organizational/global; Number of conflicting data definitions
self-report suitability: medium
Data Quality and Technical Infrastructure
Data error/redundancy rates; Data latency; Architecture maturity stage; Percent of IT devoted to data issues
self-report suitability: low
Analytical Talent and People
Number/ratio of analysts and data scientists; Workforce analytical skill assessments; Training participation rates
self-report suitability: medium
Strategic Targeting of Analytics
Share of analytics initiatives aligned to strategy; Expected outcome magnitude of initiatives; Number of strategic targets
self-report suitability: medium
Distinctive Analytics-Supported Capability
Relative process performance benchmarks; Number of proprietary analytical metrics; Competitive differentiation ratings
self-report suitability: medium
Fact-Based Decision Making and Analytical Culture
Annual number of experiments; Proportion of decisions using analytics; Cultural survey scores on data orientation
self-report suitability: high
Analytical Maturity Stage
Maturity assessment score; DELTA factor ratings; Stage descriptors met
self-report suitability: medium
Business Performance and Competitive Advantage
Profit margin, revenue growth, shareholder return; Market capitalization; ROI of analytics initiatives
self-report suitability: low
The story
The reader A senior executive or manager who wants to build lasting competitive advantage for their organization.
External problem
Traditional sources of differentiation (geography, regulation, proprietary technology, product innovation) are eroding while competitors and data proliferate.
Internal problem
They feel uncertain whether their gut-based decisions and scattered data efforts are good enough, and anxious about falling behind more analytical rivals.
Philosophical problem
It is simply wrong to leave value on the table by deciding on intuition when data and analysis could reveal a better, fact-based path.
The plan
- Assess your current analytical maturity and identify a distinctive capability to support with analytics.
- Secure committed senior leadership and adopt an enterprise-wide approach.
- Build the DELTA elements: quality data, enterprise coordination, leadership, targets, and analytical talent.
- Choose a full-steam-ahead or prove-it path and progress through the five stages.
- Embed analytics into processes and continually renew advantage with new data, metrics, and techniques.
Success
- You out-think and out-execute competitors with fact-based decisions.
- You acquire and retain the best customers, optimize pricing, and run ultra-efficient supply chains.
- Your organization becomes a high performer with a renewable, hard-to-copy advantage.
- Analytics become embedded in your culture, products, and processes.
At stake
- You remain complacent like the film, newspaper, and video-rental firms that became case studies.
- Rivals capture your best customers and markets while you decide by guesswork.
- Your scattered, low-quality data and political infighting stall any analytical progress.
- You miss the shift to predictive, prescriptive, and autonomous analytics.
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