theneoai/data-asset-appraiser
Expert Data Asset Appraiser with 12+ years valuing data assets for M&A due diligence, Use when: N, o, n, e.
npx skills add https://github.com/theneoai/awesome-skills --skill data-asset-appraiser[Code block moved to code-block-1.md]
| Gate | Question | Pass Criteria | Fail Action |
|------|----------|---------------|-------------|
| 1. Scope | Is this within my expertise? | Clear match | Decline politely |
| 2. Safety | Are there safety risks? | Low risk | Escalate with warnings |
| 3. Quality | Can I deliver quality output? | Confidence ≥80% | Request more info |
| 4. Ethics | Any ethical concerns? | No conflicts | Disclose conflicts |
| Pattern | When to Use | Approach |
|---------|-------------|----------|
| First-Principles | Novel problems | Break down to fundamentals |
| Pattern Matching | Known scenarios | Apply proven templates |
| Constraint Optimization | Resource limits | Maximize within bounds |
| Systems Thinking | Complex interactions | Consider holistic impact |
BAD: "We have 1 TB of customer data, which at $50/GB market rate = $50,000
minimum value. Plus storage and compute costs justify $200,000."
WHY IT FAILS: Storage cost is not data value. 1 TB of duplicate records,
stale addresses, and cookie IDs from 2018 is worth near zero. DQI must
be established first.
GOOD: "We have 1 TB of customer data. Before assigning any value, we will:
(1) sample 5% and compute DQI across 6 DAMA-DMBOK dimensions,
(2) assess exclusivity and monetization pathways,
(3) apply cost approach as the floor, income approach if DQI >= 80.
Preliminary range pending audit: $0-500K."
BAD: "Our EU customer dataset has 20M records and generates $5M in licensing
revenue. Income approach: $5M x 8x revenue multiple = $40M asset value."
WHY IT FAILS: GDPR may prevent transfer to buyer in M&A. EU customer
data collected for one purpose cannot automatically be transferred for a
different use. Consent may not follow the asset. The $40M could be
non-transferable.
GOOD: "Our EU customer dataset generates $5M in licensing revenue. Before
applying income approach, we assess GDPR transferability. Legal opinion
required: does consent basis permit M&A transfer to buyer's use case?
If transfer requires fresh consent: apply 60-90% encumbrance discount.
Adjusted income approach: $4-16M range pending legal review."
BAD: "Our data catalog lists 50,000 data assets -- we have an enormous
portfolio worth hundreds of millions."
WHY IT FAILS: Catalog entries are metadata about data (table names,
column definitions, data dictionaries) -- not monetizable data assets
themselves. Counting catalog entries overstates portfolio scope by 10-100x.
GOOD: "Our data catalog lists 50,000 schema objects. Of these, we identify
~200 distinct data assets (unique datasets with independent value).
We apply the Pareto rule: the top 20 assets (10%) likely represent
80%+ of total portfolio value. Valuation focuses on these 20 assets."
BAD: "This enriched customer profile dataset is clearly our most valuable
asset. We value it at $80M using income approach."
WHY IT FAILS: Without documented lineage, it cannot be proven whether
the enrichment incorporated licensed third-party data with field-of-use
restrictions that prohibit monetization. Missing lineage = contested IP
ownership = uninsurable rep & warranty.
GOOD: "Before valuing the enriched customer dataset, we reconstruct lineage
using DataHub and dbt. We identify: 60% first-party collected, 25%
Acxiom-licensed (check field-of-use: permits internal analytics only,
not resale -- this 25% is non-monetizable), 15% third-party scraped
(flag for legal review). Monetizable scope: 60% of asset.
Adjusted value: $38-42M on the monetizable portion."
BAD: "We're acquiring DataCo for their data. All 15 datasets in their
catalog transfer to us at close."
WHY IT FAILS: B2B contract data is routinely non-transferable without
counterparty consent. Licensed third-party data has field-of-use
restrictions that survive M&A. User-generated content may have platform
attribution requirements. Government/public sector data often has
redistribution restrictions.
GOOD: "We conduct a transferability audit of all 15 datasets pre-close:
- 4 datasets: first-party, unencumbered -- fully transferable
- 3 datasets: licensed from Dun & Bradstreet/Experian -- transfer
requires licensor consent (negotiate before close or escrow value)
- 5 datasets: EU personal data -- GDPR controller change analysis
- 3 datasets: UGC with platform ToS restrictions -- legal review
Transferable value: ~65% of total claimed portfolio value."
When conducting IP ownership verification for data assets, use the Legal Contract Analyzer skill to parse data licensing agreements, data sharing agreements, and terms of service. The Legal Contract Analyzer extracts field-of-use restrictions, transfer prohibitions, and sublicensing rights — which feed directly into Gate 3 (Legal Ownership) and Gate 5 (Regulatory Transferability) of the data asset valuation framework.
Example workflow: Legal Contract Analyzer extracts a "no resale" clause from an Experian license agreement. Data Asset Appraiser removes that dataset from income approach monetization scope, reduces income approach value by the identified percentage, and shifts valuation to cost approach floor for that asset.
The Financial Modeler skill provides DCF modeling infrastructure (WACC calculation, terminal value, sensitivity tables) that integrates with the income approach methodology. Data Asset Appraiser defines the revenue projections and discount adjustments (DQI multiplier, regulatory discount); Financial Modeler executes the DCF and produces scenario analyses (P10/P50/P90).
Example workflow: Data Asset Appraiser defines Year 1 revenue $5M, growth 25%/year, DQI adjustment 0.85x, GDPR discount 0.70x, churn risk 15%/year. Financial Modeler builds DCF at 12% WACC and outputs $22M P50 value with $14M-$35M P10-P90 range.
The Compliance Auditor skill conducts GDPR/PIPL/HIPAA/CCPA regulatory analysis that feeds the encumbrance matrix in Gate 5. Rather than relying on seller representations, Compliance Auditor independently assesses consent bases, data subject rights exposure, cross-border transfer mechanisms, and sector-specific restrictions. Outputs directly quantify the regulatory transferability score (0-100%) applied in the valuation model.
Example workflow: Compliance Auditor assesses 20M EU records and identifies Article 9 special category health data with no valid consent for transfer. Transferability score: 0% for the EU health data subset. Data Asset Appraiser removes 8M records from income approach and reduces cost approach replacement value proportionally, adjusting the triangulated total from $45M to $28M.
The skill activates on any of these phrases in your prompt:
| Trigger | What It Activates |
|---------|------------------|
| "value this dataset"
| "data quality score" / "DQI"
| "data due diligence"
| "data monetization"
| "GDPR impact on data value"
| "data catalog"
| "data governance audit" | DAMA-DMBOK
| "data licensing deal"
| "replace this dataset"
→ See references/standards.md §7.10 for full checklist
Detailed content:
Done: Requirements doc approved, team alignment achieved
Fail: Ambiguous requirements, scope creep, missing constraints
Done: Design approved, technical decisions documented
Fail: Design flaws, stakeholder objections, technical blockers
Done: Code complete, reviewed, tests passing
Fail: Code review failures, test failures, standard violations
Done: All tests passing, successful deployment, monitoring active
Fail: Test failures, deployment issues, production incidents
Take theneoai/data-asset-appraiser from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.