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.

library / libde3ec448fb1c2be5

Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

Foster Provost, Tom Fawcett · 2013

In a sentence

A conceptual guide that distills the fundamental principles underlying data science so that business people and aspiring data scientists can think data-analytically about extracting useful knowledge from data to improve business decisions.

Data Science for Business is the definitive primer for understanding data science not as a grab-bag of algorithms but as a coherent set of fundamental principles that structure data-analytic thinking. Provost and Fawcett—both seasoned practitioners and researchers—argue that beneath the dizzying array of data mining techniques lies a relatively small set of concepts (treating data as a strategic asset, framing problems with expected value, finding informative attributes, fitting models while controlling overfitting, measuring similarity) that unify the field. Organized around the CRISP data mining process and richly illustrated with real-world business cases—customer churn, targeted marketing, fraud detection, charity solicitation, whiskey recommendation, text mining of news—the book teaches readers to decompose business problems into solvable data science tasks, to evaluate solutions in business terms, and to communicate across the technical/business divide. It is the rare book that equips managers to evaluate data science proposals and equips data scientists to align their work with business value, making both better at extracting competitive advantage from data.

The four lenses

Science
Statistics
Systems
Strategy

The model

A causal/path model expressing how organizational design levers (investment in data, data science talent, data-analytic management, sound process) and analytical practices (informative attribute selection, expected value framing, complexity control, proper evaluation) produce psychological/behavioral states (data-analytic thinking, model generalization) and ultimately business outcomes (decision quality, competitive advantage). Inferred from the book's recurring themes.

Investment in Data Assetsdesign lever

The deliberate organizational decision to acquire, generate, and curate data (including incurring costs to obtain otherwise unavailable data) as a strategic asset rather than treating data only as a byproduct of operations.

Data Science Talent Qualitydesign lever

The quality, depth, and breadth of the organization's data scientists and their professional networks, recognizing the large variance in data scientist ability and the importance of apprenticeship and connections.

Data-Analytic Management Capabilitydesign lever

The degree to which management understands fundamental data science principles, can ask probing questions, anticipates project outcomes, and creates a culture where data science thrives, bridging technical and business teams.

Adherence to Sound Data Mining Processdesign lever

The extent to which the organization follows a structured, iterative data mining process (CRISP-DM) with proper business understanding, data preparation, modeling, evaluation, and deployment stages.

Informative Attribute Selectiondesign lever

The practice of identifying and selecting descriptive attributes (variables/features) that reduce uncertainty about a target of interest, measured via information gain, entropy reduction, or variance reduction.

Expected Value Problem Framingbehavioral pattern

The practice of structuring business problems using the expected value framework—decomposing them into probabilities (estimable from data) and values (from business knowledge) weighted across possible outcomes.

Model Complexity Controldesign lever

The deliberate management of model complexity (via tree pruning, feature selection, regularization, cross-validation) to find the trade-off between fitting data and generalizing, thereby avoiding overfitting.

Proper Evaluation Practicedesign lever

The use of evaluation methods aligned with the business goal—holdout testing, appropriate metrics (expected profit, ROC/AUC, lift), and meaningful baselines—rather than simplistic accuracy on training data.

Data-Analytic Thinkingpsychological state

The cognitive disposition and capability among managers and analysts to view business problems from a data perspective, assess whether and how data can improve performance, and reason systematically about analytics opportunities and threats.

Model Generalization Performancebehavioral pattern

The degree to which a model's discovered patterns apply to previously unseen data drawn from the same population, as opposed to memorizing idiosyncrasies of the training data.

Decision Qualityoutcome metric

The improvement in business decision-making—accuracy, profitability, and effectiveness—achieved by basing decisions on data analysis rather than intuition alone, including at massive automated scale.

Competitive Advantage from Data Scienceoutcome metric

The sustained business advantage a firm achieves when its data assets and data science capability are valuable, aligned with strategy, and difficult for competitors to replicate.

How they connect

investment in data assets → influences model generalization
data science talent → predicts competitive advantage
data analytic management → influences data analytic thinking
sound data mining process → influences model generalization
informative attribute selection → influences model generalization
complexity control → predicts model generalization
complexity control → moderates model generalization
proper evaluation practice → influences decision quality
expected value framing → mediates decision quality
data analytic thinking → predicts expected value framing
data analytic thinking → predicts decision quality
model generalization → influences decision quality
decision quality → influences competitive advantage
investment in data assets → influences competitive advantage

A candidate measure

Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking — derived measurement candidates

Investment in Data Assets

Annual data acquisition/curation expenditure; Number of deliberate data-generating experiments; Count and uniqueness of integrated data sources; Ratio of data investment to total analytics budget