Data Footprint
Which of your 267 tables is the most valuable data to harvest?
Scorecard
| Dimension | Score | Evidence |
|---|---|---|
| Pain | 5/5 | All 267 tables show N/A. Instrument built but not reading. Every commissioning decision is guesswork. |
| Demand | 4/5 | Blocks commissioning for all PRDs. BOaaS customers need data maturity scoring. 5+ internal PRDs depend. |
| Edge | 4/5 | 8-table meta schema. Walrus adapter. 23 domains. DatabaseIntrospectionService. 6+ months to replicate. |
| Trend | 5/5 | 73% AI projects fail on data (Gartner). On-chain attestation accelerating. DePIN data networks 300% YoY. |
| Conversion | 3/5 | Internal path clear. External: sellable when BOaaS customers see their own maturity dashboard. |
| Composite | 1200 | 5 x 4 x 4 x 5 x 3 |
Kill signal: If introspection populates all 267 tables but nobody checks the scores within 30 days, the instrument reads but nobody listens.
The Thesis
Data is oil. Some oil is more valuable. The refinery determines the grade.
meta_table_documentation is the meta-language for data — the same instrument the content graph is for ideas. The content graph ranks pages by PageRank. The data footprint ranks tables by maturity, coverage, and value to the business.
| Content Graph | Data Footprint |
|---|---|
| Pages (1,577 nodes) | Tables (267 rows) |
| Links (9,718 edges) | Foreign keys + relationships |
| PageRank (structural importance) | metaScore (maturity + coverage) |
| Binding dimensions (purpose, principles, platform, perspective, performance) | Scoring dimensions (schema maturity, docs, completeness) |
| Pack notation (compressed map) | Domain chips + filters (compressed view) |
| Seeds (nav, engineering) | Domains (core, venture, agent, ...) |
Four Gaps
| # | Gap | What Done Looks Like |
|---|---|---|
| 1 | meta_table_documentation has 0 rows | One row per table, auto-seeded from information_schema |
| 2 | Introspection ran but shows N/A | Record counts, column counts, FK graph populated for all 267 |
| 3 | CRUD + API detection not writing to DB | hasCrudInterface and hasAgentInterface flags accurate |
| 4 | No mapping to work charts or ventures | outcomeEnablement links tables to BOaaS operations |
On-Chain Dimension
Which tables benefit from immutable, decentralized storage (Walrus/Sui)?
| Criteria | What Qualifies | Example Tables |
|---|---|---|
| Identity | Portable, verifiable | agent_profiles, org_organisations |
| Trust | Tamper-proof reputation | meta_connections_relationships |
| Attestation | Proof of capability | meta_standards, commissioning scores |
| Lineage | Provenance trail | universal_data_batches, pipeline_executions |
Context
- ETL Data Tool — Upstream: pipelines feed data into tables
- Data Interface — Downstream: three interfaces per table
- Admin Portal — Parent: data footprint is a page within admin
- Automated Commissioning — Peer: reads footprint scores for L0-L4
- Data Footprint Docs — The commissioning instrument spec
- AI Data Industry — Market thesis: data compounds, ownership distributes
- Intelligent Hyperlinks — Three pipe generations: information, value, intent
Questions
If the data footprint is the meta-language for data, what is the equivalent of PageRank — the algorithm that ranks tables by structural importance rather than opinion?
- Should metaScore be auto-calculated from the three dimensions or remain a separate holistic judgment?
- When a table feeds 5 work charts but has zero records, is it high-priority to activate or evidence of over-engineering?
- Which tables should go on Walrus first — highest metaScore or highest compliance requirements?
- What makes a good HITL interface for this instrument — what does the operator need to see that the agent cannot assess?