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Analytics at Work: Smarter Decisions, Better Results
Thomas H. Davenport, Jeanne G. Harris, Robert Morison · 2010
In a sentence
A practical, implementation-focused guide showing how any organization can build the capabilities to put analytics to work in everyday decisions and processes to make smarter decisions and get better results.
Most companies are awash in data yet make 40 percent of major decisions on gut instinct, leaving money and competitive advantage on the table. In this sequel to Competing on Analytics, Davenport, Harris, and Morison move beyond the rare analytical competitors to the broad base of organizations that simply want to become more analytical—one decision at a time. The book organizes the prerequisites for analytical success under the memorable DELTA framework (accessible high-quality Data, an Enterprise orientation, analytical Leadership, strategic Targets, and Analysts), pairs it with a five-stage maturity model, and then explains how to sustain analytical capability by embedding analytics in business processes, building an analytical culture, and continually reviewing models and assumptions. Illustrated with examples from Best Buy, Progressive, Humana, 1-800-Flowers, Capital One, and many others, it offers frameworks, assessment tools, and pragmatic advice—a compass rather than a rigid map—for managers who want to unleash the value buried in their data.
The four lenses
- Science
- Statistics
- Systems
- Strategy
The model
A capability model in which five organizational design levers (Data, Enterprise orientation, Leadership, Targets, Analysts) plus sustaining practices (embedded analytics, analytical culture, continuous model review) drive fact-based decision making, which in turn improves business performance and results. Analytical maturity moderates the strength of these relationships.
Accessible High-Quality Datadesign lever
The degree to which an organization has clean, integrated, accessible, well-governed, and where possible unique/proprietary data structured and managed for analytical (not just transactional) use.
Enterprise Orientationdesign lever
The extent to which data, technology, analysts, and analytical decisions are coordinated holistically across organizational silos rather than fragmented into local, self-serving fiefdoms.
Analytical Leadershipdesign lever
The presence of leaders at any level who passionately manage by fact, push for data and analysis, set example and performance expectations, hire smart analysts, and build an analytical ecosystem.
Strategic Analytical Targetsdesign lever
The degree to which analytical efforts are focused on high-value, high-impact business processes and distinctive capabilities that drive performance and differentiation rather than scattered low-value projects.
Analyst Talent Capabilitydesign lever
The supply, mix, skills, engagement, organization, and deployment of analytical talent (champions, professionals, semiprofessionals, amateurs) building and applying models across the enterprise.
Embedded Analytics in Business Processesbehavioral pattern
The degree to which analytical models and decisions are integrated into core operational processes and workflow so that insights are routinely and automatically acted upon.
Analytical Culturepsychological state
A shared set of attitudes and behaviors—searching for truth, seeking data not just stories, valuing negative results, pushbacks for unsupported claims, and acting on analysis—that makes fact-based decisions the norm.
Continuous Review and Model Managementbehavioral pattern
The systematic practice of reviewing and renewing strategy, targets, competitors, customers, technology, and analytical models/assumptions to keep them valid as conditions change.
Fact-Based Decision Makingbehavioral pattern
The use of objective data and rigorous analysis as the primary guides to decisions across strategic, tactical, and operational levels, with intuition employed only where appropriate.
Analytical Maturity Stagecontextual condition
The organization's position on the five-stage continuum from Analytically Impaired to Analytical Competitor, reflecting how developed and balanced its DELTA capabilities are.
Business Performance and Resultsoutcome metric
The downstream outcomes—better/faster/more consistent decisions, improved efficiency, risk management, profitability, growth, and competitive differentiation—achieved by putting analytics to work.
How they connect
- data quality and access → predicts fact based decision making
- enterprise orientation → influences fact based decision making
- analytical leadership → predicts analytical culture
- analytical leadership → influences strategic targets
- strategic targets → predicts business performance results
- analyst capability → predicts fact based decision making
- data quality and access → predicts embedded analytics
- embedded analytics → mediates fact based decision making
- analytical culture → mediates fact based decision making
- fact based decision making → predicts business performance results
- continuous model review → moderates business performance results
- analytical maturity stage → moderates fact based decision making
- continuous model review → correlates continuous model review
The process
The book's playbook provides a comprehensive guide for organizations to systematically build and leverage analytical capabilities to make smarter decisions and achieve better results. It introduces the DELTA model as the central framework, outlining five essential pillars for success: accessible, high-quality Data; an Enterprise orientation to break down silos; analytical Leadership to drive change; strategic Targets to focus effort; and skilled Analysts to execute the work. The overall process involves assessing an organization's current analytical maturity across these five dimensions—on a scale from 'Analytically Impaired' to 'Analytical Competitor'—and then methodically advancing each DELTA element in rough proportion to move up the maturity stages. The playbook extends beyond building foundational capabilities to putting them into daily practice. This operational phase involves three key activities: embedding analytics directly into core business processes to industrialize decision-making; fostering an analytical culture where a 'test and learn' philosophy and fact-based reasoning become the norm; and continuously reviewing business assumptions, competitor activities, and analytical models to stay relevant in a changing environment. The ultimate goal is to transition from ad-hoc, 'craft' analytics to a sustainable, enterprise-wide capability where data and systematic reasoning guide decisions at every level, from strategic planning to daily operations, leading to sustained high performance.
Advancing Analytical Maturity Using the DELTA Framework
To systematically build and scale an organization's analytical capabilities by assessing its current state and methodically improving the five core elements of Data, Enterprise, Leadership, Targets, and Analysts (DELTA) to progress through stages of maturity.
When to use: When an organization decides to embark on a journey to become more analytical, or when it needs a structured framework to guide its ongoing improvement efforts.
Step 1Assess the organization's current analytical stage (1-5) across each of the five DELTA dimensions.
Entry: A commitment from leadership to evaluate the organization's analytical capabilities.
Exit: A clear understanding of the organization's current analytical stage and its strengths and weaknesses across the DELTA framework.
In: Organizational knowledge, DELTA framework and maturity model · Out: Analytical maturity assessment
Step 2Establish a vision for a more analytical future and create a high-level plan to advance capabilities.
Entry: The analytical maturity assessment is complete.
Exit: A shared vision and a high-level roadmap for analytical improvement are established.
In: Analytical maturity assessment, Business strategy · Out: Analytical vision, High-level improvement plan
Step 3Execute stage-appropriate improvements across all five DELTA elements.
Entry: A vision and plan are in place.
Exit: The organization has successfully implemented the capabilities characteristic of the next maturity stage.
In: High-level improvement plan, DELTA Transitions guide · Out: Enhanced data infrastructure, Improved analytical processes, Developed analyst talent
Step 4Sustain momentum and embed the new capabilities.
Entry: New capabilities have been developed.
Exit: Analytical practices are integrated into the organization's standard operating procedures and culture.
In: Newly developed analytical capabilities · Out: Sustained analytical performance
Targeting Analytical Initiatives
To systematically identify, evaluate, and select the business problems and opportunities where applying analytics will create the most significant business value and competitive differentiation.
When to use: When starting a new analytical initiative, planning the portfolio of analytics projects, or seeking to align analytical resources with strategic priorities.
Step 1Find opportunities for analytical application.
Entry: A need to identify where to apply analytical resources.
Exit: A list of potential analytical opportunities is generated.
In: Strategic plans, Industry analysis, Business process maps · Out: List of candidate analytical targets
Step 2Establish the ambition for each opportunity.
Entry: A list of candidate targets has been generated.
Exit: Each candidate target is scored for potential value and feasibility.
In: List of candidate analytical targets, DELTA capability assessment · Out: Evaluated list of analytical targets
Step 3Prioritize and select the target.
Entry: Candidate targets have been evaluated.
Exit: One or more high-priority targets are selected for action.
- Which target offers the best combination of high business impact and high feasibility?
In: Evaluated list of analytical targets · Out: Selected analytical target(s)
Step 4Pilot and refine the selected target.
Entry: A high-priority target has been selected.
Exit: The pilot is complete, and a business case for a full-scale initiative is developed.
In: Selected analytical target · Out: Pilot results, Refined business case
Embedding Analytics into a Business Process
To integrate analytics directly into the workflow of a core business process, moving from ad-hoc analysis to automated, consistent, and efficient decision-making.
When to use: When an organization wants to industrialize a specific analytical application to improve the performance of an ongoing business process.
Step 1Identify key decision points within the process.
Entry: A business process has been selected as a target for analytical improvement.
Exit: A clear map of the process and its key decision points is created.
In: Process documentation · Out: Process map with identified decision points
Step 2Design the decision logic and level of automation.
Entry: Decision points are identified.
Exit: A decision logic design is specified for each key decision.
- Should the decision be fully automated, semi-automated (with overrides), or human-assisted?
In: Process map with identified decision points · Out: Decision logic specification
Step 3Implement the complete analytical solution.
Entry: Decision logic is specified.
Exit: The analytically-enabled process is built, tested, and ready for deployment.
In: Decision logic specification, Data feeds, IT systems · Out: New analytically-enabled process, Trained users
Step 4Deploy and monitor the embedded analytics.
Entry: The new process is built and tested.
Exit: The process is live and its performance is being actively monitored.
In: New analytically-enabled process · Out: Improved process performance, Performance monitoring data
Conducting a Comprehensive Analytical Review
To ensure the ongoing relevance, accuracy, and strategic alignment of the organization's analytical models, targets, and capabilities in a dynamic business environment.
When to use: On a periodic basis (e.g., annually) or when triggered by significant changes in the business environment, strategy, or performance.
Step 1Review strategic alignment.
Entry: A scheduled review or a significant business change has occurred.
Exit: The alignment of analytics with strategy is understood and documented.
In: Current business strategy, Portfolio of analytical projects · Out: Strategic alignment assessment
Step 2Review the competitor landscape.
Entry: A need to understand the competitive environment for analytics.
Exit: A competitive intelligence report on analytical capabilities is produced.
In: Market intelligence, Industry reports · Out: Competitive analytics assessment
Step 3Review customers, partners, and technology horizons.
Entry: A need to update understanding of the external environment.
Exit: An assessment of external trends and their impact on analytics is complete.
In: Customer data, Technology trend reports · Out: External environment assessment
Step 4Conduct systematic model management.
Entry: The organization relies on a portfolio of analytical models.
Exit: The status and performance of all key analytical models are known, and an action plan for updates is created.
In: Model inventory, Model performance data · Out: Model management report, Model update plan
A candidate measure
Analytics at Work: Smarter Decisions, Better Results — derived measurement candidates
Accessible High-Quality Data
number of conflicting/duplicate data sources; data integration coverage (% of transaction systems in warehouse); count of proprietary/unique data assets used; data quality error rates in key domains
self-report suitability: medium
Enterprise Orientation
number of redundant data marts/tools; percent of analytics projects that are cross-functional; existence of enterprise governance bodies
self-report suitability: medium
Analytical Leadership
frequency of data-based pushbacks; number of analytical hires sponsored; visible analytics communications by leaders
self-report suitability: medium
Strategic Analytical Targets
ratio of high-impact to low-value projects; number of cross-functional/strategic targets; ladder rung achieved per major process
self-report suitability: medium
Analyst Talent Capability
headcount by analyst type; skill proficiency ratings; analyst engagement/satisfaction scores; retention/turnover rates
self-report suitability: medium
Embedded Analytics in Business Processes
share of decisions automated vs human; number of embedded vs standalone applications; insight-to-action latency
self-report suitability: medium
Analytical Culture
frequency of 'use data' pushbacks; instances of decisions reversed by analysis; perceived norm of fact-based decisions
self-report suitability: high
Continuous Review and Model Management
existence of model-validation function; review cadence frequency; number of models retired/updated per period
self-report suitability: low
Fact-Based Decision Making
percent of major decisions based on facts vs gut; decision-process audit scores; decision quality tracked over time
self-report suitability: medium
Analytical Maturity Stage
composite DELTA-by-stage assessment score; number of elements at each stage
self-report suitability: medium
Business Performance and Results
profit margin change; cost savings from analytics; market share; forecast/decision accuracy; decision cycle time
self-report suitability: low
The story
The reader A manager or executive whose organization holds massive data but underuses it, and who wants to make more fact-based decisions and get better business results.
External problem
The company collects and stores data but doesn't analyze it to inform decisions, leaving money and competitive advantage on the table.
Internal problem
The reader feels they are managing on autopilot or going with their gut, unsure whether their decisions are right and frustrated by missed opportunities.
Philosophical problem
It's just plain wrong to make important decisions based on unaided intuition, bias, or 'because that's how it's always been done' when facts and analysis are available.
The plan
- Get accessible, high-quality, and where possible unique Data in order.
- Adopt an Enterprise perspective rather than fractured local silos.
- Build analytical Leadership at every level, setting example and expectations.
- Pick strategic Targets where analytics will make the biggest difference.
- Acquire, organize, and develop Analysts as a scarce, valuable workforce.
- Embed analytics in business processes, build an analytical culture, and continually review models and assumptions.
Success
- Smarter, more consistent, faster, fact-based decisions; better problem solving and business processes; the ability to anticipate market shifts; improved efficiency, risk management, and profits; and a sustainable enterprise-wide analytical capability.
At stake
- Continuing to manage on autopilot and gut feel, leaving money on the table, falling behind analytical competitors, being unable to push back on poorly understood risks (as in the 2007–2009 financial crisis), and stagnating while rivals improve.
Questions this book answers
- What does it take to put analytics to work in everyday business decisions and processes?
- What capabilities and assets (the DELTA factors) does an organization need to succeed with analytics?
- How does an organization progress through stages of analytical maturity?
- How do you sustain analytical capability over time through processes, culture, and model review?
- When are analytics appropriate, and when should intuition prevail?
Glossary
- Accessible High-Quality Data
- The organization's stock of clean, integrated, accessible, well-governed, and ideally unique data structured for analytical use.
- Enterprise Orientation
- The degree of holistic, coordinated management of analytical resources and decisions across organizational boundaries.
- Analytical Leadership
- Leader presence and behaviors that drive an organization toward fact-based decision making at any level.
- Strategic Analytical Targets
- The focusing of analytical effort on high-value, high-impact, differentiating business processes and decisions.
- Analyst Talent Capability
- The strength of the organization's analytical talent pool and how well it is organized, engaged, and deployed.
- Embedded Analytics in Business Processes
- The integration of analytical models and decisions directly into operational processes and workflow.
- Analytical Culture
- The shared norms and behaviors that make fact-based, data-seeking decision making the default.
- Continuous Review and Model Management
- The systematic, recurring practice of reviewing and renewing strategy, targets, competitors, customers, technology, and analytical models/assumptions.
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