2026-04-12-data-asset-decomp

Data Asset Decomp

What Problems Can a Data Asset Solve?

BLUF: There are two broad categories of supply chain problems: mandatory compliance and competitive advantage. Mandatory functions are existential—sanctions screening, export control classification (EAR/ITAR), forced labor traceability, country-of-origin verification, cybersecurity compliance, and ESG reporting. The core structural failure is limited visibility which leads to manual, audit-vulnerable processes. Failure here can mean shipment seizure, contract loss, or debarment.

Competitive advantage functions, by contrast, determine performance rather than survival. These include single-point-of-failure detection, supplier substitution simulation, geopolitical shock modeling, inventory optimization, and reshoring tradeoff analysis. Compliance keeps firms legally operational; simulation and resilience modeling determine whether they scale faster and protect margins.

Mandatory: Compliance & Regulatory Response

• Sanctions and denied-party screening across Tier 1–N

• Export control classification (EAR/ITAR dual-use ambiguity)

• Forced labor / UFLPA traceability

• Country-of-origin verification

• ESG and Scope 3 reporting requirements

• Government contract supply chain disclosure

• CFIUS (Committee on Foreign Investment in the US) exposure mapping (foreign ownership risk)

• Supplier cybersecurity compliance (CMMC in defense)

Competitive Advantage:

Resilience & Risk

• Single-point-of-failure detection

• Substitution simulation (supplier A → B)

• Geopolitical shock scenario modeling

• Capacity ramp modeling under demand spike

• Chokepoint logistics re-routing

Cost & Margin

• Lead-time optimization

• Inventory buffer optimization

• Dual sourcing ROI modeling

• Make vs. buy simulation

• Geographic reshoring cost tradeoffs

Strategic Positioning

• Supplier bargaining leverage analysis

• Industry capacity benchmarking

• Early detection of upstream distress

What Sources Does our Data Asset Integrate?

A supply chain intelligence asset must integrate both public and proprietary data, but proprietary inputs create the real competitive advantage and data moat. Public data—corporate disclosures, sanctions lists, customs filings, AIS shipping data, satellite imagery, procurement records, and macro geopolitical signals—provides structural visibility and high-level exposure mapping. Proprietary data—multi-tier supplier relationships, facility-level production allocation, throughput and inventory bands, lead times, contract risk allocation, and revenue dependency mapping—enables true dynamic vulnerability detection, modeling, and remediation.

Public data maps the skeleton; proprietary data reveals dependency and revenue-at-risk. A defensible intelligence platform emerges when proprietary data is abstracted and aggregated, then fused with macro signals to produce system-level insights no single firm can generate independently.

Public Data

Corporate & Regulatory

• 10-K supplier disclosures

• Export control lists (BIS, OFAC)

• Sanctions databases

• Government procurement awards

• ESG & sustainability filings

Trade & Logistics

• Customs/import-export filings

• AIS shipping data

• Port throughput statistics

• Trade flow databases (UN Comtrade)

• Satellite imagery (facility activity)

Macroeconomic & Political

• Tariff regimes

• Industrial policy announcements

• Labor strikes

• Conflict/geopolitical alerts

Proprietary Data

Structural Graph

• Tier 1–N supplier relationships

• Facility-level production allocation

• Approved vendor lists (AVL)

Operational

• Throughput bands

• Inventory ranges

• Lead times

• Qualification timelines

Economic

• Contract terms (LTAs, take-or-pay)

• Pricing bands

• Revenue dependency by SKU

Risk Signals

• Internal supplier audits

• Incident reports

• Insurance claims history

How Do We Earn Proprietary Data?

BLUF: We must build product features enticing firms to 1) provide their supply chain data in exchange for a valuable product workflow dependent on that data, or 2) generate data in-platform by facilitating transactions between different supply chain tiers. Compounding occurs when 1) the data acquisition cost of customer onboarding reduces through redundant suppliers, and 2) the scale of the data asset grows enough to power industry-wide risk scoring and simulation.

Single-Customer Interactions (workflow-for-data trade)

Compliance workflows are inevitable problems for customers and our foot-in-the-door; simulation workflows generate competitive advantage for customers and further expansion of our data asset. Firms will onboard limited data into the platform for defensive, mandatory workflows, then push towards the offensive through cutting costs and identifying operational efficiencies, features enabled only by the growing scale from initial customer acquisition.

Multi-Customer Interaction (transactional data creation)

• Supplier onboarding and verification in-platform. Once a firm creates a supplier profile in the portal it can be reused to model relationships between that supplier and other firms, making subsequent onboarding quicker and less costly.

• Counterparty discovery tools inside platform (suggestions of alternate suppliers for existing needs, search tool to find suppliers for new product lines)

• Broker consortium-building/data-sharing alliance agreements in-platform

How to Create a Data Asset from Scratch

The long-term vision of the data asset as a “digital twin” is clear yet attention must be paid to its initial curation. Picking one initial category of mandatory workflows (compliance) in a highly regulated and clustered critical industry (defense industrial base, semiconductors, etc.) will allow the creation of network effects at small subsets of the economy and a base to expand to other industries, workflows, and supplier tiers.

0 → 1

• Focus on one regulated vertical (e.g., defense-adjacent manufacturing) with one mandatory compliance workflow in that industry

• Capture Tier 1 + facility metadata to power the targeted workflow

• Deliver compliance workflow automation for individual firms immediately

• Build entity resolution and federated graph backbone

1 → N

• Expand depth and breadth of data Tier 2–3 via recursive supplier onboarding in-platform

• Introduce shock simulation; OSINT fusion/alerting

• Facilitated by growing scale of data, launch global and aggregated risk benchmarking

• Basis of shared suppliers fuels expansion into adjacent end industries with existing supplier base

Technical Architecture to Balance Privacy and Network Effects

The architecture of the data asset must consist of individual customer siloes to protect raw proprietary data and an anonymized, global aggregation layer to power simulation and cross-customer network effects at scale. Bridging these two layers is a shared entity identification and resolution system.

• Tenant-isolated raw data storage (strict data walls)

• Global entity resolution layer (shared supplier ID system); encrypted supplier identity registry

• Aggregation layer (capacity bands, risk scores, graph metrics) obscured by differential privacy and bucketing of data

• Risk scoring and simulation engine depends on the aggregation layer rather than individual customer tenants.

Scratch

Generalized insights for Bliss:

• Supply chain “truth” is not just static but dynamic. We must model not only structure/dependency (this company consumes these three inputs, which rely on these three upstream inputs) but also real-time data about capacity, supply, transportation times - all of which change constantly over time.

• “Defensive” vs “Offensive” vis-a-vis incentives. Shooting from the hip, but my hunch is that we can use “defensive” needs such as compliance, due diligence analysis, and mandatory audits as a means of incentivizing companies to share proprietary data, which we can then use in aggregate to provide “offensive” return in the form of financial derivatives, risk transfer via underwriting, an index etc.

• Setting up a “marketplace” of sorts where firms trade with each other creates a byproduct of backfilled supply chain data naturally through transactions, which of course creates a snowball effect/positive-reinforcement cycle over time as more data → better product → more customers → more transactions → more data.

• Regulatory impact is a mixed-bag on transparency:

  • on one hand - KYC law makes legal entities fairly easy to resolve
  • on the other hand - Trade/manifest data access is shaped by law and confidentiality requests; for example, U.S. rules allow confidential treatment of importer/consignee and shipper details on manifests.

• Should have a 3 hour strategy session just thinking about creative ways to incentivize contribution of data

• Avoids competition (a la Thiel)

• very top of the Blank preread says “question for our meeting: how do we identify a specific real problem and customer so we can start building?”

  • Current hypothesis: compliance in semi manufacturing/supply chain as a wedge to compile a data asset that can lead to creation of supply chain intelligence tool that then enables the creation of financial and insurance products
    • Sanctions and denied-party screening across Tier 1–N
    • Export control classification (EAR/ITAR dual-use ambiguity)
    • Forced labor / UFLPA traceability
    • Government contract supply chain disclosure
    • CFIUS (Committee on Foreign Investment in the US) exposure mapping (foreign ownership risk)
    • @Bliss Perry add the 3-4 specific wedges