First-touch diagnostic
Executive Number Risk Self-Score
The Self-Score asks the buyer to pick one forecast, dashboard metric, AI-written board narrative, or deal-critical figure and score it against ten risk statements.
Proof and review assets
Review the proof pattern before you buy anything.
The proof stack shows the same commercial system across product-scale forecasting, capital-scale acquisition work, and enterprise-value data architecture.
Public proof shows the pattern. Confidential company diagnosis belongs in a scoped review.
Buyer-forwardable review assets
Two assets now carry the site funnel.
The Self-Score makes the buyer identify the risk. The Sample Ledger shows the product they would receive.
Sample deliverable
Analytics Debt Ledger Sample
The Sample Ledger shows an anonymized review output with summary judgment, anomaly ledger, severity, confidence, recommended actions, and two findings in depth.
Case studies
Forecast discipline in the real world.
These are not generic analytics examples. Each case supports the same assurance claim: serious leadership decisions need numbers that can be inspected, challenged, and tied to enterprise value.
Product scale
Case Study: Executive Forecasting Built Into Tableau
A leading business intelligence platform needed to make forecasting usable inside the dashboards executives and operators already relied on.
Stephen led and architected the forecasting feature set created and implemented in Tableau while serving as Director of Analytic Product Management. Instead of forcing business users to export data into separate statistical tools or fragile spreadsheets, the work brought forward-looking analysis directly into the normal analytics workflow.
The challenge
Most executive dashboards were backward-looking. They showed what happened, but they did not help leadership understand what was likely to happen next. Finance teams needed forecasts more defensible than spreadsheet trend lines. Revenue leaders needed earlier visibility into growth patterns, seasonality, and momentum shifts. Executives needed forward-looking dashboards that could support operating decisions.
What was built
- Time-series forecasting capabilities inside Tableau dashboards
- Automatic detection of trend and seasonality
- Forecast results visible beside historical business performance
- Model-fit and forecast-quality information for practical review
- A workflow that moved forecasting closer to executive operating cadence
Business impact
The work helped move forecasting from a specialist-only statistical function into a mainstream executive dashboard capability. CFOs, CROs, CEOs, and investors could compare actual history against projected future results inside the same environment used for business review.
What this proves
- Forecasting becomes more valuable when it is inspectable inside the decision workflow.
- Executive dashboards need trend, seasonality, uncertainty, and model quality visible beside historical performance.
- Forecast discipline is not just the number. It is the ability to challenge the number before it becomes an operating story.
Primary offer supported: Analytics & AI Spend Rationalization Review.
Capital scale
Case Study: Advanced Forecast Simulations for a $1B+ Pharma Acquisition
A Global 50 pharmaceutical company needed to evaluate the growth potential of a major acquisition with more than $1B in strategic value at stake.
The work helped build an advanced forecast simulation approach. Instead of relying on static projections, the model separated baseline revenue momentum from upside that could come from active commercial execution. The result was a clearer view of acquisition value, revenue risk, and post-close growth potential.
The challenge
The asset operated across many international markets. Revenue potential varied by product line, channel, country, and packaging format. Finance needed defensible valuation support, commercial leadership needed to see where growth could come from, and executives needed a way to test different expansion assumptions before committing capital.
What was built
- A baseline revenue model across markets and product categories
- A separation between organic momentum and execution-driven growth
- Scenario logic for new countries, channels, and product configurations
- Interactive assumptions for market-share capture, rollout timing, and growth rates
- A leadership view of baseline value, likely upside, and maximum strategic potential
Business impact
The work turned a high-stakes acquisition forecast into a structured executive decision tool. Leadership could see which assumptions mattered, which growth levers created value, and where the acquisition thesis depended on post-close execution.
What this proves
- High-stakes acquisition forecasts need scenario architecture, not static spreadsheet optimism.
- Baseline momentum must be separated from execution-dependent upside.
- Leadership needs to know which assumptions create value, which assumptions carry risk, and which actions must happen post-close.
Primary offer supported: Analytics QoE / Live-Deal Diligence.
Enterprise-value scale
Case Study: Asset-Light International Expansion for TV Intelligence
A TV intelligence company wanted to expand its advertising data platform into Canada without spending months building duplicate infrastructure from scratch.
Stephen was the main builder of the expansion approach. Instead of treating Canada as a costly new infrastructure project, the entry was structured around a strategic data and technology partnership with an established Canadian media intelligence provider.
The challenge
The company needed reliable Canadian TV and advertising data quickly. Building local monitoring infrastructure would have required significant upfront investment, while sales teams needed a credible Canadian data story immediately and investors wanted international growth without a long capital-heavy payback cycle.
What was built
- Integration of Canadian TV and advertising data into the core platform
- Standardized regional data so customers could analyze US and Canadian activity in one place
- Avoidance of redundant infrastructure buildout
- A commercial model that let both companies sell into their strongest customer bases
Business impact
The expansion turned a complex international buildout into a faster, lower-risk path into a new market. It created new cross-border revenue opportunities and strengthened the North American platform story for customers, executives, and investors.
What this proves
- Data infrastructure choices are capital allocation choices.
- Asset-light expansion can beat duplicate buildout when speed, cost, and commercial access matter.
- Analytics architecture only creates enterprise value when it is tied to revenue strategy.
Primary offer supported: Analytics Debt Ledger and enterprise-value architecture credibility.
Public proof memos
Worked examples of common defect patterns.
These remain useful as pattern-recognition content, below the real proof stack.
ARR Dashboard Overstatement
Mixed grain across CRM, subscription, opportunity, renewal, and churn logic.
Read memoLTV / CAC Misallocation
Misallocated spend and retention assumptions that distort payback and channel decisions.
Read memoBoard Forecast Fragility
Forecast confidence built on hidden overrides, weak driver logic, or unsupported narratives.
Read memoBring one number
Public proof can show the pattern. Private review tests the actual risk.
Keep the first note non-confidential. Scope and access boundaries come later.