# Design Sustainable Data Business Models

Data teams often struggle to turn technical capabilities into repeatable revenue. This skill structures positioning, offerings, revenue, partnerships, defensibility, and phased growth.

## Install

```bash
npx skillstore add 图灵数盟/dhturing-data-business-model-expert
```

## Metadata

- Status: approved
- Slug: dhturing-data-business-model-expert
- Skillstore revision: r1
- Version status: missing
- Tree hash: ccaebb55d0cdfa9c53cc118ff8609aee9ef972251981f4e5510a630e3d4310b7
- Author: 图灵数盟
- GitHub username: dhturing
- License: MIT
- Repository: https://github.com/dhturing/turing-data-experts/tree/9c58ffa97ec5b760befef6f910eb23107cdcdbf4/skills/data-business-model-expert
- Ref: 50b73f83991cf073cbdf3f5afad2bbac71dd3bae
- Supported tools: Claude, Codex, Claude Code
- Audit status: complete
- Agent install advisory: allowed
- Manual install advisory: allowed
- Artifact signature: available
- Audit attestation: unavailable
- Human verification: not\_verified
- Risk factors: filesystem, external\_commands, network
- Quality score: 77
- Quality tier: bronze
- Public page: https://skillstore.pages.dev/skills/dhturing-data-business-model-expert
- Manifest: https://skillstore.pages.dev/api/skills/dhturing-data-business-model-expert/manifest

## Capabilities

- Compares data provider, technology service, consulting, platform, and DMaaS positioning options.
- Designs a 1\+4\+N architecture linking one entry service, four extensions, and multiple industry scenarios.
- Builds revenue mixes across projects, subscriptions, transactions, commissions, consulting, and technology services.
- Structures product portfolios with entry, profit, and strategic offerings.
- Designs membership tiers, partner channels, benefits, and revenue-sharing mechanisms.
- Creates phased roadmaps from custom projects through product, platform, and ecosystem models.

## Use Cases

- Shape a Data Venture: Compare strategic positions and create an initial architecture, offer portfolio, and revenue mix.
- Productize Data Services: Convert repeated custom delivery into standard products, recurring revenue, and measurable transition stages.
- Build a Partner Model: Define membership tiers, channel roles, partner benefits, and practical revenue-sharing rules.

## Prompt Templates

### Assess My Current Model

```
Review our current data business. Identify our position, revenue sources, strongest assets, major gaps, and three immediate priorities.
```

### Design a Revenue Architecture

```
Design a 1+4+N business architecture for [company]. Include target customers, services, six revenue types, assumptions, dependencies, and validation steps.
```

### Plan Productization

```
Create a five-stage roadmap from custom projects to a product and platform model. Define milestones, metrics, reusable assets, owners, and decision gates.
```

### Stress-Test the Business Model

```
Challenge this proposed data business model: [model]. Test demand, economics, compliance, delivery capacity, partner incentives, defensibility, and failure scenarios. Propose experiments.
```

## Limitations

- Recommendations depend on accurate details about capabilities, customers, costs, regulations, and partner access.
- Financial targets are planning assumptions and require independent validation with current market data.
- The skill does not provide legal, accounting, valuation, or regulatory approval.
- Claims about credentials, partnerships, policies, and market demand must be verified before external use.

## Best Practices

- Provide verified facts about customers, capabilities, costs, credentials, and regulatory constraints.
- Separate evidence, assumptions, recommendations, and open questions in every deliverable.
- Validate the model through small paid pilots before funding broad platform development.

## Anti Patterns

- Do not present unverified credentials, partnerships, market figures, or policy claims as facts.
- Do not treat membership fees as sustainable revenue without recurring member value.
- Do not pursue platform scale before proving repeatable demand and delivery economics.

## Security Audit

- Audited at: 2026-09-27T03:17:09.839\+00:00
- Summary: All 12 static findings are false positives caused by Markdown syntax, documented installation paths, ordinary website links, and the entropy profile of Chinese text. No executable code, network request, obfuscated payload, prompt injection, or malicious intent was found.

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