YakDataProof and Track Record

Evidence before promises

The operating claim is backed by systems, decisions, and published work.

YakData is built around a repeated pattern: enter an ambiguous, consequential problem, define the decision, build the first usable system, and transfer an operating capability. This page separates current delivery proof, career proof, published methods, and teaching evidence.

Request a 20-minute fit callSee forecasting systems

01 · Production systems

Selected work tied to real operating decisions.

Career proof supports the claim. Current client-approved case studies and measurable outcomes remain the highest standard.

30 projects

National television forecasting

Forecasting, competitive intelligence, audience modeling, buy validation, and media-allocation decisions across the Canadian television market over two years.

$1B+

Pharmaceutical acquisition simulation

Designed and built the stochastic revenue simulation engine used by an Investment Committee to validate the valuation of a pharmaceutical product-line acquisition.

First system

Netflix subscriber analytics

Built the subscriber lifetime-value and retention frameworks supporting marketing, operations, and finance decisions during Netflix's early growth.

TB scale

Navy Cyber architecture

Designed first-system analytical architecture for consequential cyber-defense data at terabyte scale.

3× ROI

Fortune 50 retail analytics

Led analytics work across four teams, including one team whose measured return reached three times the investment.

Production AI

Governed analytical applications

Built automated analytical systems with multi-model review, human checkpoints, explicit assumptions, and client-controlled data boundaries.

Proof rule

Historical prestige supports the claim. It does not substitute for current systems, measurable outcomes, or visible artifacts.

02 · Published methodology

Three books translating technical work into operating judgment.

The books matter because YakData must bridge executives, domain experts, analysts, engineers, and operators without losing technical precision.

Cover of The Accidental Analyst

The Accidental Analyst

A practical framework for turning data into business questions, analysis, visualization, and executive action. Recognized by Tableau in its recommended data-visualization reading.

Cover of SAS For Dummies

SAS For Dummies

Two editions translating a broad statistical and analytical platform into usable workflows for working analysts.

Cover of Rapid Graphs with Tableau

Rapid Graphs with Tableau

An early dedicated guide to Tableau, focused on fast visual analysis and communicating evidence clearly.

03 · Industry evidence

Work that influenced how analytics was taught and practiced.

Pat Hanrahan keynote slide referencing The Accidental Analyst
Archival keynote material showing Pat Hanrahan's discussion of The Accidental Analyst at the Tableau Conference.

Pat Hanrahan keynote

Pat Hanrahan, Stanford professor, Tableau co-founder, Pixar founding employee, and Turing Award laureate, built part of his 2012 Tableau Conference keynote around The Accidental Analyst.

The relevance is not celebrity association. It is evidence that the book's framework changed how a major technical founder explained analytics and how it should be taught.

Commercial implication: YakData's differentiator is not merely building models. It is defining the decision system around them and making the reasoning usable by accountable operators.

04 · Research and analytical foundations

Customer value, segmentation, visualization, and decision framing.

Customer segmentation and lifetime value

The archival methodology work shows the same pattern used in current deployments: define the business decision, identify the behavioral and economic drivers, build a transparent analytical structure, and communicate the result so the organization can act.

That foundation later supported work in retention, acquisition economics, scenario systems, media allocation, and executive forecasting.

Archival customer segmentation and lifetime value methodology paper
Archival methodology material on visualizing customer segmentation and lifetime value.

05 · Teaching and executive communication

Technical depth tested in rooms where clarity matters.

Teaching is secondary proof, but it demonstrates the ability to transfer difficult methods without hiding behind jargon.

INFORMSFaculty for data exploration, visualization, forecasting, and operations-research audiences.
TDWIEnterprise data and analytics instruction connecting architecture to business value.
American Marketing AssociationCustomer lifetime value, cohort analysis, segmentation, and marketing decision systems.
UniversitiesGuest lectures at Princeton, Brown, Chicago Booth, and the University of Washington.
Selected handwritten student evaluations
Selected archival participant evaluations from analytics instruction.
Archival Tableau Public launch page featuring Freakalytics work
Archival Tableau Public launch material featuring Stephen's public visualization work under Freakalytics.

Next decision

Proof is useful only when it reduces the risk of the next system.

Bring one consequential decision, stalled AI initiative, unreliable forecast, or high-value manual workflow. YakData will give you a direct fit or no-fit answer.

Request a fit callReview engagements