Deep analysis of the $11–15B data visualization market. Why FT charts are unbeatable and how their internal tools work.
Every major publication’s graphics team mapped (Bloomberg’s 30-person team, WSJ’s 30+ editors, Reuters, Axios).
Chart newsletters: Chartr (500K subs, acquired by Robinhood), Torsten Slok, Adam Tooze (178K Substack subs), Kyla Scanlon (1M+ across platforms).
Full tool landscape: Datawrapper ($2.9M revenue, 50K users), Flourish (Canva), TradingView ($323M revenue, 100M+ users), Highcharts, D3.js, Observable.
10 market gaps including “FT Charts for Everyone,” “TradingView for the Economy,” AI Chart Narrator, Chart-as-a-Service API.
Complete bootstrapper playbooks for newsletter, charting tool, platform, and course businesses.
1. The Thesis — Beautiful Charts Are a $15B Gap
The world splits into three tiers of data visualization:
- Tier 1: World-class but paywalled. FT (£468/year, up 26.8% in Jan 2026), Bloomberg ($32K/year), The Economist, WSJ. Their charts are stunning, insightful, and utterly inaccessible to normal people.
- Tier 2: Free but ugly or limited. FRED (powerful but hideous), TradingView (financial-only), Google Charts (basic), Excel screenshots everywhere. Our World in Data is excellent but grant-funded and focused on development/health, not financial markets.
- Tier 3: The gap. Where is the service that makes FT-quality economic charts accessible to everyone? Where’s the tool that lets a financial advisor, a podcast host, a newsletter writer, or a curious person create or consume beautiful economic charts without paying £468/year or learning D3.js?
The market in numbers
| Data visualization software market (2025) | $10.9–$14.9B, growing to $18–$34B by 2030–2033 (CAGR 8.6–11%) |
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| Business intelligence market (2025) | $35–$38B, growing to $56–$108B by 2030–2035 |
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| FT subscription price | £468/year (up 26.8% in January 2026) |
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| Bloomberg Terminal | $32,000/year |
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| Datawrapper revenue | ~$2.9M, 29 employees, self-funded, ~50,000 registered users |
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| TradingView revenue | $323M (2023), 100M+ users |
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| Statista revenue | ~€167M (2024), up from $62.6M in 2021 |
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| Chartr at acquisition | 500K subscribers, acquired by Robinhood (Dec 2023) |
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| Freelance data viz rates | $75–$150/hour. Dashboard projects: $3K–$35K |
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2. The Gold Standard — How the Best Publications Make Charts
Major publication graphics teams
| Publication | Team size | Internal tools | What makes them distinctive |
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| Financial Times |
Visual journalism team (est. 15–25) |
Nightingale (2015), rebuilt as FastCharts (post-2017). Partially open-sourced as nightingale-charts on GitHub. |
Coral/salmon palette. Clean typography. Data-ink ratio obsession. John Burn-Murdoch’s COVID log-scale charts became a global standard. Every chart tells one story. |
| Bloomberg |
~30 people (NY, DC, London, Hong Kong) |
Toaster (internal, for reporters). Also use bqplot (open-source Grammar of Graphics library). Training journalists in Python. |
~100 quick charts/month + ~35 deep standalone pieces. Dark theme option. Dense data, financial audience assumed. Distinctly “professional.” |
| The Economist |
Data journalism team |
Internal, following their public style guide |
Publicly available visual style guide: ITC Officina Sans, neutral greys and blues, filled red rectangle logo. 80%+ simple chart types. Restrained, elegant. Widely studied across the industry. |
| Wall Street Journal |
30+ graphics editors |
Reorganized into Core and Enterprise Visuals teams |
Enterprise team works outside the news cycle with freedom to choose projects. Focus on storytelling from the start. Strong use of annotation and labeling. |
| Washington Post |
Graphics embedded per department |
35+ tools publicly shared: ai2html, Observable, QGIS, Python, R |
Transparent about tooling. Visual journalism explicitly cited as a subscriber driver. Progressive, exploratory approach. |
| Reuters Graphics |
Global team |
Active open-source GitHub presence |
Progressive loading techniques for economic data. Wire-service efficiency — charts must work everywhere. Clean, reproducible approach. |
| Axios |
Grew from 6 to 20 |
Datawrapper (externally). Uses DatawRappr (R wrapper) + Bash to batch-automate charts via Datawrapper API. |
“Almost everything in Datawrapper.” Batch-automate local city newsletter charts. Smart brevity applied to visuals: one chart, one insight, done. |
The design principles that make great charts
- One chart, one story. FT charts never try to show everything. Each chart makes exactly one point.
- Annotation over legend. The best charts label the data directly instead of using color legends that force the eye to jump.
- Data-ink ratio. Edward Tufte’s principle: maximize the share of ink devoted to data. Remove gridlines, borders, backgrounds, 3D effects.
- Color with purpose. FT uses coral/salmon as their signature. The Economist uses red sparingly. Color highlights the story, not decorates.
- Context always. Great charts show “compared to what?” — historical average, peer comparison, target. A number without context is meaningless.
- Mobile-first. Every major publication now designs charts for phone screens first. This constrains complexity and forces clarity.
3. FT Deep Dive — Nightingale, FastCharts, and Why They’re Unbeatable
The tooling evolution
| 2015 | Nightingale built as internal charting tool. Named after Florence Nightingale (pioneer of statistical graphics). |
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| 2017 | Internal workflow audit revealed it took 22 hours and 5 people to create a simple chart. Triggered a rebuild. |
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| Post-2017 | FastCharts rebuilt to let reporters self-serve. Goal: a journalist should be able to make a chart in minutes, not hours. |
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| Open source | nightingale-charts library partially open-sourced on GitHub. FT Visual Vocabulary (chart chooser) freely available. |
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The FT Visual Vocabulary
The FT published a “Visual Vocabulary” — a decision tree for choosing chart types based on the relationship
you want to show (deviation, correlation, ranking, distribution, change over time, part-to-whole, spatial, flow).
This single resource is used by data teams worldwide. It’s free. It’s brilliant.
And it highlights the gap: the FT gives away the thinking but keeps the execution tools locked inside.
John Burn-Murdoch’s influence
Chief data reporter. His COVID-19 log-scale trajectory comparison charts (Jan–March 2020) became
the most replicated data visualization in journalism history. Governments, health agencies, and newsrooms
worldwide copied his format. He single-handedly proved that one person with a clear chart can shape
global understanding of a crisis.
He is the single best argument for “great charts are worth paying for.”
The opportunity the FT created
The FT proved that beautiful economic charts drive subscriptions, social sharing, and reader trust.
Then they raised prices to £468/year. They built the demand and then priced out 99% of the audience.
Whoever serves that 99% with FT-quality visual economic content at an accessible price has an enormous market.
4. Chart Newsletters & Individual Creators
| Creator/Newsletter | Subscribers/Reach | Model | Key insight |
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| Chartr (acquired by Robinhood, Dec 2023) |
500K subscribers |
Sponsorships (70% from cold outbound). ~10 person team. |
Built outbound sponsor systems generating “literally hundreds of leads” in 6–9 months. Robinhood acquired to lower CAC and increase LTV through content. Proof that a chart newsletter is acquirable. |
| Adam Tooze (Chartbook) |
178K+ Substack subscribers |
Paid Substack (#2 in History). Potentially $1M+/year. |
Columbia historian mixing economic charts with geopolitical analysis. Charts + intellectual depth = massive audience. |
| Kyla Scanlon |
145K+ newsletter, 1M+ across platforms |
Newsletter + videos + NYT bestselling book |
Visual-first economic explainers. Coined “vibecession.” Hand-drawn-feeling charts + casual tone. |
| Torsten Slok (Apollo) |
Massive institutional reach |
Free via Apollo Academy “The Daily Spark.” Prior: OECD Paris, IMF. |
One chart per day. No commentary beyond a headline. Pure signal. The “1 chart/day” format works at institutional level. |
| Charlie Bilello |
~37K posts on Twitter/X, YouTube channel |
CEO of Compound Capital Advisors. Charts = marketing. |
Uses YCharts templates. Weekly breakdowns on YouTube. Charts as lead-gen for financial advisory. |
| Visual Capitalist |
Millions monthly visitors |
Sponsored infographics + advertising |
Infographic-style data viz as a media company. Shared millions of times. |
| FlowingData (Nathan Yau) |
Membership-based, since 2007 |
Free blog + paid members (courses, tutorials, newsletter) |
19 years of sustainable indie data viz business. One person. Proof the subscription model works. |
What the landscape tells us
- Chartr proved the model and the exit. 500K subs, acquired. Playbook validated.
- There is no “Chartr replacement.” Chartr absorbed into Robinhood. The seat is empty.
- The 1-chart-per-day format works. Torsten Slok and Charlie Bilello prove it.
- Charts + opinion > charts alone. Adam Tooze and Kyla Scanlon build deeper loyalty than pure curation.
7. Market Size & Economics
What people pay for data viz work
| Service | Price range |
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| Freelance data viz (entry) | $75–$95/hour |
| Freelance data viz (senior) | $120–$150/hour |
| Simple dashboard project | $3,000–$8,000 |
| Complex interactive system | $15,000–$35,000 |
| Monthly retainer | $5,000–$15,000 |
| Well-designed infographic | $3,000–$4,000 |
| Newsroom dataviz journalist salary | $70K–$80K median |
| Data viz specialist salary | $92K–$100K average |
Revenue benchmarks
| Datawrapper | ~$2.9M revenue, 29 employees, self-funded |
| TradingView | $323M revenue, 100M+ users |
| Statista | ~€167M revenue, 1,567 employees |
| Observable | $48.1M raised (Sequoia), 36 employees |
| Flourish | Acquired by Canva (800K+ users) |
| Chartr | Acquired by Robinhood at 500K subscribers |
9. AI & the Future of Chart Creation
Current AI chart tools
| Tool | What it does | Limitation |
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| Julius AI | Upload data, describe chart, get output | Generic. No editorial taste. |
| Graphy | AI-powered chart generation | Template-based. Limited customization. |
| ThoughtSpot | Natural language → visualizations | Enterprise BI. Not for creators. |
| Displayr | AI-assisted analysis + viz | Saves ~8 hrs/week but generic quality. |
| ChatGPT/Claude code interpreter | Generate Python/matplotlib charts from prompts | Capable but looks like matplotlib defaults without styling. |
What AI can and can’t do
- Good at: Data exploration, suggesting chart types, generating drafts, writing annotations, summarizing insights.
- Bad at: Editorial judgment (“what story should this tell?”), design taste, knowing what to compare, understanding audience context.
- Opportunity: AI makes charts 5x faster. A human with taste + AI speed beats both humans-without-AI and AI-without-taste.
AI-powered business opportunities
- AI Chart Narrator: Upload chart image → get text description, key takeaways, social caption.
- “Make it look like FT” tool: Upload ugly Excel chart → AI re-renders with FT-quality styling.
- Data-to-story pipeline: Feed economic data → AI identifies trend → generates chart + headline + summary. Automated “Torsten Slok daily chart.”
10. 10 Market Gaps & Business Opportunities
| # | Gap | What exists | What’s missing | Model |
|---|
| 1 | “FT Charts for Everyone” | FT (£468/yr), Bloomberg ($32K/yr) | Beautiful economic charts at $5–$15/month | Subscription + sponsors |
| 2 | “TradingView for the Economy” | TradingView (markets), FRED (ugly econ data) | Interactive economic data with beautiful UI | Freemium SaaS |
| 3 | Chart-as-a-Service API | Datawrapper (manual), Highcharts (dev library) | API: send data + style preset → get beautiful chart | Usage-based API |
| 4 | “Make it look like FT” restyler | Nothing | Upload ugly chart → get FT-quality version | Per-chart or subscription |
| 5 | Post-Chartr newsletter | Chartr absorbed into Robinhood | Independent daily chart newsletter. 500K proven. | Sponsorships |
| 6 | Embeddable chart widgets | TradingView (financial only) | Auto-updating economic chart widgets for blogs | Freemium |
| 7 | Data viz course for non-designers | Tufte seminars ($400+), scattered online | “Make FT-quality charts in Datawrapper” for advisors/writers | Course ($197–$497) |
| 8 | Productized chart agency | Freelance ($75–$150/hr) | “4 beautiful charts/week for your newsletter. $500/mo.” | Productized service |
| 9 | AI Chart Narrator | Nothing | Upload chart → get alt text + insights + social caption | Freemium SaaS |
| 10 | Open-source FT-style library | D3 (complex), Chart.js (basic) | Opinionated library with FT-quality defaults. “Tailwind of charts.” | Open-source + commercial |
11. Playbook #1: Chart Newsletter (“The New Chartr”)
The model
Daily or 3x/week. 1–3 beautiful charts per edition explaining economic trends.
Short commentary (200–400 words/chart). Monetized through sponsorships.
Chartr proved this at 500K subs. The seat is empty.
Differentiation from Chartr
- Higher chart quality. Chartr was good, not great. FT-level design is your moat.
- Deeper analysis. Not “here’s a chart” but “here’s why it matters.”
- Niche then expand. Start economic/financial, then broaden.
Production
| Data sourcing (FRED, World Bank, OECD, BLS, Eurostat) | 20 min/day |
| Chart creation (Datawrapper/Flourish) | 30 min/chart |
| Commentary | 20 min/chart |
| Format + schedule | 15 min |
| Total per edition (2–3 charts) | 1.5–2 hours |
Revenue projection
| Month 6 (10K subs, 2 sponsors/week × $400) | $3,200/month |
| Month 12 (30K subs, 3 sponsors/week × $800) | $9,600/month |
| Month 24 (100K subs, 5 sponsors/week × $1,500) | $30,000/month |
| Year 3 (250K+ subs) | $300K–$1M/year |
14. Playbook #4: Data Visualization Course
“Make FT-Quality Charts in 30 Days.” Practical, tool-specific. For advisors, newsletter writers, analysts, researchers.
Curriculum
- 6 principles of great charts (FT/Economist distilled)
- Choosing chart types (FT Visual Vocabulary, applied)
- Datawrapper masterclass (data to chart in 15 minutes)
- Flourish masterclass (interactive/animated)
- Color, typography, annotation
- Economic data sourcing (FRED, World Bank, OECD)
- Chart storytelling (headline + annotation)
- Sharing and embedding
Pricing: $197 / $397 / $497. Revenue: $100K–$300K/year at 50 students/month.
Nathan Yau has run this model for 19 years. Tufte charged $400+ and sold out for decades.
15. Playbook #5: Productized Chart Agency
| Package | Price | Includes | Target |
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| Newsletter Charts | $500/mo | 4 charts/week (16/mo). 24-hour delivery. PNG + embed. | Substack writers, financial newsletters |
| Advisor Charts | $800/mo | 8 charts/mo for client presentations. White-labeled. | Financial advisors, wealth managers |
| Report Charts | $1,500–$3K/project | 10–30 charts for a report. Consistent styling. | Research firms, think tanks |
How to find clients
Browse Substack finance leaderboard. Find newsletters with ugly charts. DM with before/after:
“Love your analysis. Your charts could be better. Here’s what your last chart would look like. I do this for $500/month.”
10 Newsletter + 5 Advisor clients = $9K/month. Achievable in 3–6 months.
16. Verdict & Best Bets
Ranked by bootstrapper fit
| Business | Startup cost | Time to $ | Year 3 ceiling | Skill needed | Score /25 |
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| Chart newsletter | $100/mo | 2–3 months | $300K–$1M | Low | 22 |
| Productized chart agency | $0 | 1–4 weeks | $200K–$500K | Medium | 20 |
| Data viz course | $500 | 1–2 months | $100K–$300K | Low-Medium | 19 |
| Chart-as-a-service API | $200/mo | 3–6 months | $500K–$3M | High | 18 |
| “TradingView for Economy” | $500/mo | 6–12 months | $1M–$5M | Very high | 17 |
The optimal stack
- Month 1: Chart newsletter. Build audience. Chartr proved 500K is possible.
- Month 2: Productized chart agency. Newsletter showcases your quality. Clients come to you.
- Month 4: Data viz course. Teach what you know. Newsletter audience = student pipeline.
- Month 6+: Chart-as-a-service tool. Productize what you do manually for agency clients.
- Year 2+: “TradingView for Economy” if you want to build something massive.
Each reinforces the others. Newsletter proves taste. Agency proves businesses pay. Course scales knowledge. Tool scales execution.
Year 3 combined: $300K–$1.5M.