Running Datastory as a service, Business Intelligence Tools

Datastory Agency Implementation, White-Label Data Reports

Learn how to deliver branded, interactive data visualizations to clients on retainer using Datastory's chart recommendation engine and AI captions. This course covers uploading client datasets, removing vendor branding, embedding charts across client websites and Notion, and building recurring monthly reporting workflows that scale without custom development.

Open the decision record for Datastory

What does running Datastory for clients commit you to?

Published figures for this service. Blank fields are not published.

Monthly tool cost
Datastory pricing is freemium; the lowest paid tier amount and any setup costs are not published in the supplied data, so vendor cost basis must be confirmed from the pricing page. White-label branding requires a tier that includes custom logo and theming.
Time to first value
Not published
Payback
Not modeled

Is Datastory worth running as a client service?

The supplied data supports Datastory as a white-label data visualization and storytelling layer with a low-complexity setup, AI chart recommendations, grounded captions, and embeddable outputs. It does not publish the lowest paid tier amount, client-acquisition pace, delivery labor rates, or expected volume, so ROI and client pricing cannot be modeled from this evidence alone.

An agency-fit judgement for reselling this service. It is separate from the tool description on the decision record.

Before you start

What has to be in place before the first client engagement.

Tools and subscriptions

  • Datastory account on the appropriate pricing tier (freemium entry tier as published; white-label features depend on the tier that includes custom branding)
  • Client CSV or spreadsheet data, or access to the Datastory open data catalog (World Bank, OECD, WHO, Eurostat)
  • Client CMS, Notion, or Webflow access for iframe embedding
  • A client-facing brand kit (logo and theming) if delivering white-label output

People and inputs

  • Documentation or onboarding for Datastory's AI chart recommendation and caption generation features
  • A repeatable data QA step to verify captions and insights are grounded in the uploaded data before client delivery
  • A test deliverable using one real client dataset to validate the full upload-to-embed workflow

Lessons in this course

7 lessons on running Datastory for clients.

  1. 01Why Datastory Turns Agency Reporting Into a Resellable Retainer AssetStrategy
  2. 02Datastory White-Label Margin ThresholdConcept
  3. 03Datastory Rule: Adopt Only When a Client Deliverable Needs an Embeddable Chart NarrativeEvaluation Rule
  4. 04Datastory: Buy vs Skip (White-Label Client Reporting at $15/User/Month)Decision Framework
  5. 05The Datastory Caption Trust Trap: Why Agencies Ship Unverified AI NarrativesFailure Pattern
  6. 06Datastory White-Label Client Reporting Setup (5-7 days)Implementation Blueprint
  7. 07Datastory Client Workspace Setup (Onboarding)Operating Procedure

Included with the course

7 working documents for delivering this service.

  • Client Data Intake Form for CSV Uploadstemplate
  • White-Label Branding Checklist (Professional Plan)checklist
  • Monthly Data Report Delivery SOPsop
  • Embed Code Snippets for Webflow and Notionguide
  • AI Caption Review and Fact-Check Worksheetworksheet
  • Open Data Catalog Source Library (World Bank, OECD, WHO)guide
  • Retainer Pricing Model for Data Visualization Servicestemplate

Listed by name. These documents are not yet published as individual downloads.