Inputs
Mock JSON or monthly benchmark rows shaped into a validated interface, events, and dictionary contract.
← Case Studies
Data product / Benchmark intelligence framework
Benchmark Intelligence Framework
Who is gaining ground, why, and what should we do next?
The project evolved from a visual dashboard into a reusable frontend intelligence framework: data contract first, calculation logic separated from presentation, and portfolio-ready validation around every public artifact.
Demo mode only: synthetic entities, synthetic values, public-safe labels — no real client, competitor, logo, or private source data.
Case Snapshot
The strategic brief
The problem, the system response, the available proof, the strategic value, and the intentional boundary.
- 01Problem
- Competitive data is difficult to compare when sources, metric definitions, validation, and executive questions are disconnected.
- 02System
- An intelligence framework with a data contract, adapters, validation, benchmark logic, executive views, and forecasts.
- 03Proof
- Public-safe repository, synthetic datasets, interface screenshots, validation logic, benchmark views, and forecast labeling.
- 04Value
- Helps answer who is gaining ground, why, and what decision should follow from the comparison.
- 05Limitation
- The public case uses synthetic companies and values; the framework requires source-specific adapters for real deployments.
Business Context
The workflow problem behind the project
Competitor research often becomes a mix of disconnected screenshots, manually assembled tables, and hard-to-repeat analysis. This project explores how structured benchmark data can become a reusable intelligence surface for clearer market context and positioning decisions.
System / Solution
How the workflow is bounded
The framework uses a defined payload contract, adapters, schema validation, benchmark calculations, view-model builders, and a React/Vite dashboard. It is deliberately public-safe: demo data is synthetic, private sources are excluded, and validation checks protect the portfolio artifact.
Workflow
Adapter, schema validation, benchmark engine, view-model generation, dashboard rendering, and release checks.
Processing logic
Ranks, share, growth, efficiency, forecasts, player profiles, and comparisons are calculated outside the UI layer.
Output
Executive dashboard views for market overview, profiles, head-to-head comparison, forecasts, and strategic signals.
Guardrails
Synthetic-data discipline, no real client or competitor data, and public-readiness validation before publishing.
What changed
From dashboard surface to reusable intelligence framework
The current product is not just a static dashboard. It separates data logic from presentation logic so benchmark rows can move through validation, calculations, view-model builders, and then into a polished executive interface.
1Explicit data contract
3Release validation checks
0Private data exposed
Benchmark engine
Calculates share, rank, growth, efficiency, aggregations, and executive comparisons outside the UI layer.
Source adapters
Converts mock JSON or simpler monthly rows into the same benchmark payload contract.
View models
Builds chart, table, profile, event, and summary structures before React renders the interface.
Public discipline
Keeps the demo portfolio-safe with synthetic data, validation scripts, and public-readiness checks.
Architecture
A framework pipeline, not a one-off screen
The framework keeps ingestion, validation, benchmark calculation, view-model preparation, and interface rendering as distinct layers. That makes the demo replaceable without changing the dashboard experience.
Monthly data / mock JSON→Adapter→Schema validator→Benchmark engine→View models→React + Vite dashboard→Vercel deployment
Data source
Mock JSON or user-provided monthly benchmark rows.
Adapter
Transforms simple rows into the framework payload shape.
Schema validator
Checks that interface, events, and dictionary data satisfy the contract.
Benchmark engine
Normalizes rows and calculates ranks, shares, growth, efficiency, and aggregations.
View models
Prepares chart-ready, table-ready, profile-ready, and summary-ready data.
Dashboard UI
Renders the executive React/Vite interface and deploys safely on Vercel.
Visual proof
Executive views from one benchmark contract
The updated interface now reads like a benchmark operating room: view selectors, period availability, rankings, momentum, player profiles, head-to-head comparison, and forecast paths generated from the same structured payload.
The interface is organized around executive modes: market overview, player profiles, head-to-head battle, and forecast workspace.
The context panel makes the selected period explicit and shows which metrics are available before the benchmark engine renders a view.
Period ranking and distribution from the interface rows, useful for executive comparison without exposing real company data.
Separates added volume from relative growth so the measured market stays a reference, not a second dashboard.
Profile-level readout for a selected entity, with executive summary, KPI cards, rank, share, and efficiency context.
A focused graph workspace lets the user compare a player against market average or selected peers across historical metrics.
Head-to-head comparison turns benchmark rows into a clear player-versus-player reading across scale, share, growth, and efficiency.
Forecast workspace separates projected periods from observed history and keeps forecast values clearly labelled.
Player-level projection shows observed history, the last observed date, and a dashed forecast path for future periods.
Data contract
One payload shape powers the framework
`data.interface` is the source of truth. The public dashboard can run from mock JSON or user-provided monthly rows routed through an adapter, as long as the payload validates before it reaches the benchmark engine.
{
"ok": true,
"meta": {
"source": "Your connector name"
},
"data": {
"interface": [],
"events": [],
"dictionary": []
}
}interface- Source-of-truth rows for dates, entities, markets, revenue, visits, ranks, shares, growth, and forecast fields.
events- Optional public-safe annotations for launches, changes, or context overlays.
dictionary- Optional definitions and metadata used to explain the interface layer.
Public-safe build discipline
Synthetic data is part of the product design
The public repo is intentionally demonstrative: generated mock companies, synthetic values, empty public env examples, and no private source URLs. That constraint makes the case study publishable without weakening the engineering story.
pnpm testpnpm validate:datapnpm audit:public- No real client data, competitor data, logos, or private API URLs.
- Adapters support replaceable inputs while preserving the same validated contract.
- Public-readiness checks make synthetic-data discipline visible before release.
Result
A reusable, portfolio-safe intelligence framework
The outcome is a reusable executive dashboard framework for competitive intelligence prototypes, analytics UX, and data-product storytelling.
Build
- React/Vite executive interface supported by schema validation, benchmark calculations, source adapters, and view-model builders.
- Centralized configuration for focus company, benchmark company, enabled views, currency, locale, and defaults.
- Synthetic mock-data policy and release checks before public publishing.
Outcome
- Reusable framework for rankings, market share, growth, forecasts, event overlays, profiles, and executive signals.
- Portfolio-safe demo that shows the product without exposing real clients, competitors, or private infrastructure.
- Clear data contract for mock JSON or user-provided benchmark rows.
Why It Matters
Reliability beats novelty
Competitive intelligence becomes more valuable when the research logic is repeatable. This framework defines inputs, calculations, view models, and public-safety constraints so benchmark analysis can move beyond one-off decks and into a reusable operating surface.
Client Relevance
Where this becomes useful
A client-facing version could help brand, ecommerce, marketing, or strategy teams compare markets, structure competitor research, monitor positioning signals, and produce clearer executive readouts without rebuilding the analysis from scratch.
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