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Value canvas for the DataForge data store

Value
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Fit
Customer
Services
Primary functions
  • Provides a curated, extensible library of synthetic and approved entity data.
  • Enables easy download and local extension to match specific product schemas.
  • Offers internally consistent, relational data to support integration, automation, and test harnesses.
  • Delivers clean, realistic data sets for development, testing, demos, and onboarding.
  • Supports ongoing data generation and versioning for evolving needs.
Pain relief
How pain is reduced
  • Removes the burden of crafting or cleaning production test data from scratch.
  • Eliminates the risk of breaching privacy or handling personal/sensitive datasets.
  • Reduces the need for InfoSec review and compliance overhead for synthetic use cases.
  • Stops teams from relying on gibberish or placeholder data that undermines testing value.
  • Prevents duplication of effort across multiple products trying to mock similar data.
Gain creators
Exciting new benefits
  • Establishes a trusted, reusable base of test-ready entities that can scale across the department.
  • Encourages collaboration around a consistent data model foundation.
  • Accelerates test automation, sandbox seeding, and integration validation.
  • Future-proofs data usage through modular design and versioned datasets.
  • Fosters adoption and contribution via extensible, open-access design.
Jobs
Tasks I need to do
  • Populate development environments or test systems with non-production / non-personal data.
  • Validate APIs, integration points, and automation flows.
  • Prototype features and run demos with believable but non-sensitive data.
  • Support multiple products with flexible data structures.
Pain points
Things that frustrate me
  • No current source of realistic, compliant test data.
  • Inconsistent, ad hoc data creation methods across teams.
  • High effort and uncertainty around masking or sanitising production data.
  • Security or compliance blockers for real-data use.
  • Frustration with brittle or out-of-date data models that don’t align across products.
Gains
Benefits I'd welcome
  • A ready-to-go source of believable, structured test data.
  • Data that "just works" for their test harness, API, or environment seeding.
  • Flexibility to adapt generic base entities to product-specific needs.
  • Confidence that privacy and InfoSec standards. are being met.
  • Alignment across products, teams, and lifecycle stages through shared data standards.
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