What is PeopleAnalyst?

PeopleAnalyst is the front door for people-analytics research: 205+ works indexed and profiled, 40+ citation-grade findings extracted, and peer-reviewed behavioral science translated from academic to actionable — the missing manual for the people analytics you always meant to do.

What is people analytics?

People analytics is not a dashboard. It is behavioral science and statistical inference applied to workforce decisions — a discipline with its own methodology, spanning measurement, organizational design, talent, leadership, and analytics craft.

Why does AI in HR need measurement science?

AI is being deployed in high-stakes people decisions — hiring, performance, attrition — without the measurement science to evaluate whether it works or whom it harms. Construct validity, effect sizes, and criterion validity are the vocabulary for asking an AI vendor the right questions.

How is the research made accessible?

The evidence is indexed and searchable: 205+ works, 40+ citation-grade insight cards, and 8 research arcs, so the right finding reaches the right decision at the right time.

What separates good people measurement from assertion?

Good measurement has a method: construct validity, reliability, and effect-size interpretation are not optional — they are what separates evidence from assertion.

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Competing on Analytics: Updated, with a New Introduction

Thomas H. Davenport, Jeanne G. Harris · 2017

In a sentence

A field-defining guide arguing that organizations can build durable competitive advantage by systematically using data, statistical and quantitative analysis, and fact-based decision making as a distinctive strategic capability.

Competing on Analytics, revised and updated, makes the case that in an era when products, geography, and technology are easily copied, the last durable source of advantage is executing business processes and decisions better than rivals through analytics. Davenport and Harris draw on hundreds of company examples (Netflix, Capital One, UPS, Caesars, Marriott, Google, Amazon, Walmart) and sports teams to define what makes an analytical competitor, lay out the four pillars (distinctive capability, enterprise approach, senior management commitment, and large-scale ambition), and chart a five-stage maturity road map plus the DELTA model (Data, Enterprise, Leadership, Targets, Analysts) for building capability. The updated edition tracks the rapid evolution through four analytics eras—from descriptive 1.0 to big-data 2.0, mainstream 3.0, and autonomous/AI-driven 4.0—and shows how human, organizational, and cultural factors, not just technology, separate winners from also-rans.

The four lenses

Science
Statistics
Systems
Strategy

The model

A causal model in which design levers (senior management commitment, enterprise-level approach, analytical talent, data and technology architecture) and contextual conditions (analytical maturity stage) build an analytics-based distinctive capability, which drives improved decision making and embedded operational analytics, ultimately yielding superior business performance and sustainable competitive advantage. An analytical, fact-based culture mediates and reinforces these effects.

Senior Management Commitment to Analyticsdesign lever

The degree to which top executives passionately advocate for, model, invest in, and act upon fact-based, analytical decision making, setting the cultural tone and committing resources for analytical competition across the enterprise.

Enterprise-Level Approach to Analyticsdesign lever

The extent to which an organization manages data, technology, and analysts in a coordinated, enterprise-wide fashion rather than in disconnected functional silos, ensuring one version of the truth and broad access to analytical resources.

Analytical Talent and Workforce Capabilitydesign lever

The availability, quality, and management of analytical professionals and data scientists plus the data literacy of analytical amateurs and decision makers, enabling design, interpretation, and use of analytics throughout the organization.

Data and Technology Architecturedesign lever

The quality, integration, accessibility, and sophistication of an organization's data and analytical technology environment, including data management, repositories, analytical tools, visualization, and deployment processes.

Analytical Maturity Stagecontextual condition

The organization's overall position along the five-stage continuum of analytical competition (from analytically impaired to full analytical competitor), conditioning how quickly and effectively capability-building investments translate into outcomes.

Analytical, Fact-Based Culturepsychological state

The shared norms and behaviors emphasizing experimentation, evidence-based decisions, objectivity, and willingness to act on data, which translate analytical capability into actual decisions and behaviors across the organization.

Analytics-Based Distinctive Capabilitybehavioral pattern

A strategically central business capability (e.g., revenue management, customer loyalty, supply chain optimization) that the organization performs better than rivals by applying extensive data, analysis, and fact-based decision making.

Fact-Based Decision Making and Embedded Analyticsbehavioral pattern

The degree to which decisions and operational processes are guided, supported, or automated by data and analytics—including real-time, embedded, and autonomous decisions—rather than intuition.

Business Performance and Competitive Advantageoutcome metric

Organizational outcomes such as profitability, revenue growth, shareholder return, market share, customer loyalty, cost savings, and sustained competitive advantage attributable to analytical competition.

How they connect

senior management commitment → influences analytical culture
senior management commitment → influences enterprise level approach
enterprise level approach → predicts distinctive analytical capability
analytical talent → predicts distinctive analytical capability
data technology architecture → predicts fact based decision making
analytical culture → mediates fact based decision making
distinctive analytical capability → influences fact based decision making
fact based decision making → predicts business performance
distinctive analytical capability → predicts business performance
analytical maturity stage → moderates business performance
analytical talent → influences analytical culture

A candidate measure

Competing on Analytics: Updated, with a New Introduction — derived measurement candidates

Senior Management Commitment to Analytics

Frequency of analytics mentions in annual reports/analyst calls; Presence of analytics in stated strategic competencies; Number of analytically oriented senior hires