---
name: data-asset-inventory-analyzer
description: "Assesses each described data source across uniqueness, coverage, quality, timeliness, and strategic relevance, then produces a scored inventory with a prioritization matrix, tiered ratings, gap analysis, and ranked investment recommendations. Use when cataloging data assets, evaluating a data estate for strategic or monetization potential, or building a recurring input to data strategy planning."
argument-hint: "[data-sources] [strategic-context]"
allowed-tools: ["Read"]
---

# Data Asset Inventory Analyzer

## Purpose
Turn an unstructured description of a data estate into a scored, tiered inventory — rating every source on five decision dimensions and producing a prioritization matrix with ranked recommendations for where to invest, monetize, remediate, or retire. Solves the problem of a data organization that owns dozens of assets but has no defensible way to say which ones are strategically worth funding.

## When to Use
Invoke this skill when the user asks to:
- Catalog or inventory available data sources and rate their value
- Assess which data assets are worth investing in, cleaning up, or monetizing
- Prepare a data-estate assessment as input to a data strategy or roadmap
- Identify unique or defensible data the organization holds versus commodity data
- Compare data sources on quality, coverage, or strategic fit before prioritizing spend
- Build a recurring, repeatable data asset review for governance or planning cycles

## Inputs Required
Before starting, collect:
- **Data sources list**: The assets to assess — name and 1–2 sentence description of each (systems, tables, feeds, warehouses, third-party datasets)
- **Business context**: What the organization does and the strategic goals the data should serve
- **Intended use cases**: What decisions, products, or initiatives these assets are meant to feed (analytics, ML, monetization, reporting)
- **Known quality/access issues**: Any documented problems — gaps, latency, PII constraints, ownership ambiguity
- **Refresh characteristics**: How often each source updates, if known (real-time, daily, batch, static)
- **Scope of assessment**: Full estate vs. a specific domain, and whether the goal is prioritization, monetization, or remediation
- **Comparables available**: Whether similar data can be bought or built externally (for uniqueness scoring)

If refresh cadence or comparables are unknown, score conservatively on those dimensions and flag the assumption explicitly.

## Instructions

### Step 1: Normalize the Asset List
Restate each described source as a discrete inventory line with a consistent short name, a one-line description, and its apparent primary domain (customer, transactional, operational, external, derived). Merge duplicates and split any "source" that is actually several distinct assets. Confirm the count before scoring.

### Step 2: Score Each Asset on Five Dimensions (1–5)
Rate every asset on each dimension using the anchored scale below. Never assign a score without a one-clause justification.

**Uniqueness** — how defensible / hard to replicate
- 5: Proprietary, cannot be bought or rebuilt elsewhere
- 3: Partially unique; some external substitute exists
- 1: Commodity data freely available or purchasable

**Coverage** — breadth and completeness relative to the use case
- 5: Comprehensive across entities, geographies, and time
- 3: Meaningful but partial coverage with known blind spots
- 1: Sparse, fragmentary, or narrow slice only

**Quality** — accuracy, consistency, structure, trustworthiness
- 5: Clean, governed, validated, low error rate
- 3: Usable but requires meaningful cleaning/reconciliation
- 1: Unreliable, inconsistent, or poorly documented

**Timeliness** — freshness relative to what the use case needs
- 5: Real-time or fresh enough that latency is irrelevant
- 3: Periodic refresh acceptable for most uses
- 1: Stale, static, or latency undermines value

**Strategic Relevance** — fit to stated goals and use cases
- 5: Directly enables a priority strategic initiative
- 3: Supports secondary initiatives or general analytics
- 1: Tangential to current strategy

### Step 3: Compute Composite Score and Tier
For each asset, sum the five scores (max 25) and assign a tier:
- **Tier 1 — Crown Jewels** (20–25): Defensible, high-relevance assets to protect and leverage
- **Tier 2 — Strategic Assets** (15–19): Valuable, worth targeted investment to elevate
- **Tier 3 — Utility Assets** (10–14): Operationally useful but not differentiating
- **Tier 4 — Liabilities/Candidates to Retire** (below 10): Low value, high maintenance, or redundant

Weight Strategic Relevance and Uniqueness more heavily in tie-breaks — a high-quality commodity asset is worth less strategically than a moderately clean proprietary one.

### Step 4: Build the Value-Effort Prioritization Matrix
Plot each asset on two axes: **Strategic Value** (composite score) vs. **Effort to Unlock** (inferred from Quality + Timeliness gaps). Assign each asset to one quadrant:
- **Leverage Now**: High value, low effort — activate immediately
- **Invest**: High value, high effort — fund remediation to unlock
- **Maintain**: Lower value, low effort — keep running, don't over-invest
- **Reconsider**: Lower value, high effort — retire, consolidate, or defer

### Step 5: Gap and Opportunity Analysis
Identify what the estate is missing relative to the stated use cases:
- Coverage gaps where no owned asset serves a priority initiative
- Assets whose value is trapped behind a single fixable dimension (e.g., strong data with only a timeliness problem)
- Redundant or overlapping sources that should be consolidated
- Monetization candidates: high-uniqueness, high-coverage assets with external demand potential

### Step 6: Ranked Recommendations
Produce 6–10 recommendations ordered by expected strategic impact. Each recommendation states: the asset(s) involved, the action (leverage, invest, remediate, consolidate, monetize, retire), the dimension it moves, and the outcome it enables. Be decisive — name specific assets, not general principles.

## Output Format
- **Structure**: Normalized asset list → scored inventory table → tier summary → prioritization matrix → gap analysis → ranked recommendations
- **Length**: 1,000–1,400 words plus tables
- **Format**: A scored table (one row per asset, five dimension columns, composite, tier) plus structured prose for matrix, gaps, and recommendations
- **Sections**: Inventory Overview → Scored Asset Table → Tier Breakdown → Value-Effort Matrix → Gaps & Opportunities → Ranked Recommendations
- **Tone**: Data strategy advisor to an executive — analytical, decisive, investment-minded; every score defended, no hedging on tiers

## Quality Checklist
Before delivering output, verify:
- [ ] Every asset from the input appears exactly once in the scored table (count matches Step 1)
- [ ] Each asset has all five dimension scores plus a composite and a tier
- [ ] No dimension score appears without a one-clause justification
- [ ] Every asset is placed in exactly one matrix quadrant
- [ ] Gap analysis names at least one coverage gap or trapped-value asset (not "no gaps found" unless justified)
- [ ] Recommendations are ranked by impact and each names specific assets and a specific action

## Notes
- The most common finding is a high-quality asset trapped behind one weak dimension — timeliness or access — where a modest investment moves it up a full tier. Surface these first; they are the cheapest wins.
- Uniqueness is the dimension most often overrated by asset owners. Pressure-test any 5 against whether a competitor could buy or rebuild the equivalent.
- When run recurringly, keep dimension anchors constant across cycles so tier movement is comparable over time — that trend is itself a strategy signal.
- If the estate is large, offer to run a first pass at the domain level before scoring individual assets, so scoring effort concentrates on the domains that matter.

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## How to use this skill

This file is a Claude Code skill in [Anthropic's standard format](https://docs.anthropic.com/en/docs/claude-code/skills) — markdown with YAML frontmatter at the top.

**Installing in Claude Code:**
- `~/.claude/skills/<folder-name>/SKILL.md` — auto-loads in every session anywhere on your machine (recommended for skills you'll use across multiple projects)
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Restart Claude Code or open a new session to register.

**Using in Claude.ai, ChatGPT, Gemini, or other AI tools:**

1. Drag this file into a new chat, or copy and paste its contents
2. Send your task or request as your next message — the AI will apply the skill where it fits

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