Data Services

Data Visualization Services

Turn raw numbers into simple charts and graphs that anyone can understand.

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Delivery focus

Built to move from idea to production.

Numbers are hard to read in tables. Our data visualization services turn raw numbers into simple charts, maps, and graphs that anyone on your team can understand in seconds, not hours. The work sits inside Data Services and is scoped, built, reviewed, and handed over as one engagement.

Interactive charts

Custom dashboards

Data storytelling

Team-friendly visuals

Scope

ClickMasters builds data visualisation systems for B2B companies across the USA, Europe, Canada, and Aralia. Custom D3.js and Recharts dashboards for data-dense B2B products. Interactive React dashboards that let users explore their data without contacting support. Embedded analytics that make your SaaS product feel data-native. And data storytelling that converts raw numbers into decisions.

Recharts vs D3.js vs BI Tool for Data Visualisation

Recharts (and similar libraries Nivo, Victory, Chart.js) are React component libraries they provide pre-built chart types (bar, line, area, scatter) as composable React components that consume data via props. They are the right choice for standard business charts in React applications fast to implement, well-documented, and maintained. D3.js is a low-level data visualisation library it binds data to SVG elements and provides the building blocks (scales, axes, layouts) to construct any possible visualisation. Use D3 when no pre-built chart library has the chart type you need (network graphs, Sankey diagrams, custom geospatial charts, animated data stories). BI tools (Metabase, Looker, Tableau) are drag-and-drop tools for non-developers they are fast for standard charts but cannot produce custom interactive chart experiences or integrate deeply with a React product's design system. ClickMasters uses Recharts/Nivo for product-embedded standard charts, D3.js for custom chart types, and BI tools for internal analytics.

SVG vs Canvas Rendering for Charts

SVG (Scalable Vector Graphics) is the default for most charts: each element is a DOM node (hoverable, interactive, accessible, easy to style with CSS), renders sharply at any resolution, and is the output format of D3.js and most React chart libraries. Use canvas rendering when: the chart has more than 10,000-50,000 data points (SVG performance degrades significantly each data point is a DOM node, and 100,000 DOM nodes make the browser sluggish), real-time updates at 60fps are required (canvas redraws are faster than DOM mutations), or WebGL rendering is needed for 3D or GPU-accelerated visualisation (Deck.gl uses WebGL for millions-of-points geospatial visualisations). ECharts (Apache) and Vega-Lite support automatic switching between SVG and canvas based on data size. ClickMasters uses SVG for standard business charts and canvas/WebGL for high-volume or high-performance visualisation requirements.

What this engagement delivers

React Dashboard Development

Production React dashboards with TypeScript: Recharts (AreaChart, BarChart, ScatterChart, RadarChart primary), Nivo (heatmaps, treemaps, Voronoi for data-dense B2B dashboards), Victory (animation-native good for mobile), Chart.js with react-chartjs-2 (legacy). Dashboard patterns: KPI card row + trend chart + breakdown table, date range picker, dimension filter, drill-down interactions as standard.

D3.js Custom Visualisation

When no charting library has the right chart type: D3.js provides SVG element binding, scales (linear, log, ordinal, time), axes, transitions (smooth data updates), layouts (force simulation for network graphs, treemap, chord diagram, Sankey). ClickMasters uses D3.js for: network/graph visualisations, geospatial visualisations (custom map projections, choropleth), financial charts (candlestick, waterfall, tornado), any chart type not in standard libraries.

Real-Time Dashboards

Dashboards that update as data changes: WebSocket connection (new data pushed in real time no polling), Server-Sent Events (simpler than WebSocket for one-way data push live metrics, leaderboards), TanStack Query with short polling (5-second refresh for near-real-time). Use cases: operations monitoring, live sales performance, real-time customer-facing analytics.

Embedded Chart Components

Customer-facing data visualisation embedded in SaaS products: chart components as React component library (TypeScript props, theming via CSS variables), Recharts or Nivo as rendering engine (no BI tool dependency), data fetching from product's own API, performance optimisation (WebWorker for heavy transformations, canvas rendering for charts with 100,000+ data points via ECharts or Vega-Lite).

Data Storytelling & Scrollytelling

Long-form data narratives with interactive charts: scrollytelling (chart updates as user scrolls through data story annual reports, research presentations), annotated chart design (callouts highlighting key data points, contextual annotations, trend indicators), print-quality chart export (PNG/SVG/PDF from React charts). Libraries: Scrollama (scrollytelling), html2canvas (capture React components as images), jsPDF (generate PDF reports).

Process

  1. Phase 1 · Week 1

    Visualisation Discovery

    Chart type selection, data model review (what fields available, cardinality, granularity), component architecture plan, performance requirements. Deliverable: Visualisation Architecture Plan.

  2. Phase 2 · Week 2-5

    React Dashboard Build

    Recharts/Nivo implementation, KPI cards + trend charts + breakdown tables, date range picker, dimension filters, drill-down interactions, TypeScript types. Deliverable: Production React Dashboard.

  3. Phase 3 · Week 2-4

    D3.js Custom Chart

    Custom SVG layout, D3 scales and axes, transitions, interactions (tooltips, zoom, pan), responsive sizing. Deliverable: D3.js Component + Documentation.

  4. Phase 4 · Week 3-5

    Real-Time Integration

    WebSocket/SSE connection, real-time data feed, chart animations on data update, performance optimisation (throttling, debouncing). Deliverable: Real-Time Dashboard.

Tech stack

React Charting
Recharts (composable primary), Nivo (rich interactive heatmaps, treemaps), Victory (animation-native), Chart.js (legacy)
Custom Visualisation
D3.js (low-level scales, axes, layouts, transitions, force simulation), Visx (Airbnb's D3+React primitives)
High-Volume
ECharts (Apache auto SVG/canvas switching), Vega-Lite (declarative), Deck.gl (WebGL millions of points)
Geospatial
Mapbox GL JS (vector tiles, custom styling), React-Leaflet (OSM-based), Deck.gl (WebGL geospatial)
Real-Time
WebSocket (full-duplex), Server-Sent Events (one-way), TanStack Query (polling), Recharts animate prop
Export
html2canvas (render to PNG/JPEG), SVG export (native chart libraries), jsPDF (multi-chart PDF reports), Scrollama (scrollytelling)

How we work

Recharts vs D3.js vs BI Tool

Clear decision framework: Recharts for standard charts, D3 for custom, BI for internal dashboards | Basic: One tool for everything (suboptimal)

SVG vs Canvas Threshold

10,000-50,000 points threshold canvas for large datasets (performance), SVG for smaller (interactivity) | Basic: SVG only (slow on large data)

Real-Time Stack

WebSocket/SSE + TanStack Query live dashboards without polling | Basic: Polling only (wasteful, laggy)

Scrollytelling

Scrollama + chart updates data narratives for annual reports, investor decks | Basic: Static charts in reports

Export Capabilities

html2canvas + jsPDF PDF report generation from React dashboards | Basic: No export (screenshots only)

Where this is used

Executive Dashboard

React + Recharts dashboard for company KPIs. MRR trend (area chart), churn by cohort (grouped bar), revenue by segment (treemap). Date range picker, drill-down to customer-level data.

Supply Chain Network Map

D3.js force-directed graph of supplier relationships. Nodes = facilities, edges = shipments. Color by region, size by volume. Interactive tooltips, zoom/pan.

Real-Time Operations Board

WebSocket dashboard for warehouse operations. Live order volume (line chart updating every second), queue length (progress bar), exception alerts (real-time notification list). 5-second refresh polling fallback.

Embedded Customer Analytics

React chart components embedded in SaaS product. Each customer sees their own usage metrics. Theming matches product design system. No BI tool dependency.

Pricing guidance

These ranges are planning anchors. The quote names scope, timeline, and what is excluded. Talk through a quote.

Visualisation Discovery

$2,000 - $5,000

1 week

Chart type selection, data model review, component architecture plan

React Dashboard (Standard)

$6,000 - $18,000

3 - 6 weeks

Recharts/Nivo, KPI cards, filters, drill-down, date range, TypeScript

Custom D3.js Chart

$5,000 - $15,000

2 - 4 weeks

Custom SVG layout, transitions, interactions, responsive

Real-Time Dashboard

$8,000 - $22,000

3 - 6 weeks

WebSocket/SSE, live updates, performance optimisation

Embedded Chart Library

$8,000 - $22,000

3 - 6 weeks

React component library, theming, TypeScript, design system integration

Geospatial Visualisation

$8,000 - $22,000

3 - 6 weeks

Mapbox/Leaflet/Deck.gl, custom layers, interactive filters

Data Storytelling / Scrollytelling

$6,000 - $16,000

2 - 5 weeks

Scrollama, annotated charts, PDF export, responsive

Visualisation Retainer

$2,500 - $6,000/mo

Ongoing

New chart types, dataset additions, performance, design refresh

FAQs

What is the difference between Recharts, D3.js, and a BI tool for data visualisation?

Recharts (and similar libraries Nivo, Victory, Chart.js) are React component libraries they provide pre-built chart types (bar, line, area, scatter) as composable React components that consume data via props. They are the right choice for standard business charts in React applications fast to implement, well-documented, and maintained. D3.js is a low-level data visualisation library it binds data to SVG elements and provides the building blocks (scales, axes, layouts) to construct any possible visualisation. Use D3 when no pre-built chart library has the chart type you need (network graphs, Sankey diagrams, custom geospatial charts, animated data stories). BI tools (Metabase, Looker, Tableau) are drag-and-drop tools for non-developers they are fast for standard charts but cannot produce custom interactive chart experiences or integrate deeply with a React product's design system. ClickMasters uses Recharts/Nivo for product-embedded standard charts, D3.js for custom chart types, and BI tools for internal analytics.

When should I use canvas rendering vs SVG for charts?

SVG (Scalable Vector Graphics) is the default for most charts: each element is a DOM node (hoverable, interactive, accessible, easy to style with CSS), renders sharply at any resolution, and is the output format of D3.js and most React chart libraries. Use canvas rendering when: the chart has more than 10,000-50,000 data points (SVG performance degrades significantly each data point is a DOM node, and 100,000 DOM nodes make the browser sluggish), real-time updates at 60fps are required (canvas redraws are faster than DOM mutations), or WebGL rendering is needed for 3D or GPU-accelerated visualisation (Deck.gl uses WebGL for millions-of-points geospatial visualisations). ECharts (Apache) and Vega-Lite support automatic switching between SVG and canvas based on data size. ClickMasters uses SVG for standard business charts and canvas/WebGL for high-volume or high-performance visualisation requirements.

Can I export charts from my React dashboard to PDF or image?

Yes. Chart export from React dashboards uses html2canvas (captures a React component including its styled contents as an HTML5 canvas element, then exports to PNG or JPEG) for screenshot-style exports, and SVG export (many chart libraries Recharts, Nivo can export the chart's SVG element directly scalable, high-quality). For multi-chart PDF reports, jsPDF combines multiple chart exports into a single PDF document. For production-quality publication-ready charts, visx (Airbnb's React + D3 primitives library) produces SVG output that is directly exportable and styleable. ClickMasters implements chart export with correct rendering (chart must be fully visible in the DOM off-screen charts require workarounds) and appropriate resolution scaling for print quality.

What chart type should I use for my data?

Chart type selection depends on what relationship you are communicating. Time series (how does a metric change over time?): line chart or area chart. Comparison (how do values compare across categories?): bar chart (vertical for small category counts, horizontal for many categories or long labels). Part-to-whole (what is the composition of a total?): stacked bar or pie/donut chart (use sparingly humans read bar lengths more accurately than pie angles). Distribution (how are values distributed?): histogram, box plot, or violin plot. Correlation (is there a relationship between two variables?): scatter plot. Flow (how does a quantity move between states?): Sankey diagram. Hierarchy (how is a quantity broken down into nested categories?): treemap or sunburst. Geographic (how does a metric vary by location?): choropleth map. Network (how are entities connected?): force-directed graph. The most common data visualisation mistake is using a pie chart for data with more than 3-4 categories a horizontal bar chart communicates the same information far more accurately.

Related reading

Data Visualization Services is one line inside Data Services. Teams with an adjacent problem often compare it with Data Science & Analytics and Business Intelligence (BI). For a written delivery narrative, read the SaaS booking platform QA case study and the rest of the case studies.

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