How the Technology Radar Works — A Brief Guide

Why I Built a Data-Driven Radar

Technology moves faster than any individual — or advisory board — can track. New frameworks launch weekly. AI tools go from zero to production in months. What's "emerging" in January is table stakes by June. Traditional technology radars, published semi-annually or annually, simply can't keep pace.

Having spent years on both sides — leading engineering teams that build products and advising PE/VC firms that evaluate them — I saw the same gap from every angle. Vendors pitch optimistic roadmaps. Analyst firms publish subjective opinions behind paywalls. Open-source hype inflates GitHub stars. The people making technology decisions had no unbiased, data-driven signal they could trust weekly.

That's why I built this radar: to be more objective than subjective. To combine multiple independent data sources into a transparent, reproducible score. To update every Monday, not twice a year. And to be free — because technology intelligence shouldn't be a luxury.

This page provides a brief overview of how the scoring works. Every score is computed by a deterministic algorithm — no manual overrides, no editorial bias.

Data Sources

Each tool is scored using four independent data sources, collected weekly:

1. Google Trends (Weight: 25%)

Search interest data via the DataForSEO API: current interest level (0-100), year-over-year change, and month-over-month momentum. This captures public awareness and mindshare — a tool with rising interest is entering more conversations and searches.

2. GitHub Activity (Weight: 25%)

For open-source tools: total stars (log-normalized), stars growth, commit frequency, and active contributors over the last 30 days. Proprietary tools without a GitHub repo have their composite score re-normalized across the remaining sources.

3. Expert Network Signal (Weight: 30%)

The radar's key differentiator. Aggregated, anonymized mention counts from expert network call logistics across Tegus/AlphaSense, Office Hours, Third Bridge, Arbolus, Capvision, and Guidepoint. Higher mention counts indicate PE/VC firms are actively evaluating a technology — a leading indicator that often precedes public adoption by 6-12 months.

Privacy note: Only aggregated counts and network names are stored. No email content, subjects, senders, or confidential information is stored or published.

4. Search Volume (Weight: 20%)

Monthly search volume and keyword difficulty from DataForSEO. Search volume indicates market interest; keyword difficulty indicates how established the tool is in search.

Composite Score

The composite trend score is a weighted average of the four component scores:

trend_score = (trends x 0.25) + (search x 0.20) + (github x 0.25) + (expert x 0.30)

When a source is unavailable (e.g., no GitHub repo for a proprietary tool), its weight is redistributed proportionally to the remaining sources. Scores are smoothed with EWMA to reduce week-to-week noise and movement is classified using 12-week deltas with hysteresis rules to prevent churn.

Movement Classification

Movement	Criteria
Rising	Score >= 65 AND (12-week delta >= +7 OR expert mentions >= 3)
Emerging	Score 40-65 AND (12-week delta > 0 OR expert mentions >= 2)
Stable	Score 30-70 AND |12-week delta| < 5
Declining	Score < 30 OR 12-week delta <= -7 (and mentions not increasing)
New	Fewer than 4 weeks of data available

Known Limitations

• Open-source bias — GitHub signals are only available for open-source tools. Proprietary enterprise software may be under-represented.
• English-centric — Google Trends and search volume data is US-market focused. Regional trends may differ.
• Expert network scope — Expert mentions reflect PE/VC and consulting interest, which skews toward enterprise and growth-stage technologies.
• New tools — Recently added tools start with "New" classification and require 4+ weeks before meaningful movement detection.
• Name collisions — Some tool names overlap with common words. The alias system helps, but false matches are possible.

For the full technical deep-dive — normalization formulas, EWMA parameters, hysteresis thresholds, confidence scoring, and the weekly pipeline architecture — see the complete methodology on wetheflywheel.com.