Dataset opportunity
Gophr — Industrial Operations Dataset Opportunity
Moderate industrial operations dataset held by Gophr, usable for Industrial Monitoring and Forecasting.
Score
48
Score (0–100) blends weighted dimensions — dataset rarity, training value, buyer demand, evidence strength and right-to-license. 70+ is deal-ready. See the scored dimensions below for the breakdown.Confidence
49%
Action
Data Sharing Agreement
The recommended deal structure for this dataset: Acquire (full buyout), License (paid usage rights), Data Sharing Agreement (controlled access, no transfer of ownership), Partnership (co-development) or Annotation Program (labeling). Chosen from data ownership, licensing complexity and accessibility.Market
Global Supply Chain Analytics market = $7.16B in 2023, CAGR 17.8% (source: Grand View Research)
Recent dated external facts that triggered this opportunity — auditable provenance.
- 📰press2026-07-23
Weak housing market hurts big and bulky last-mile delivery
freightwaves.com ↗ - 📰press2026-07-22
Tractor Supply taps Instacart for same-day delivery
supplychaindive.com ↗ - 📰press2026-07-21
Monoprix installe un hub chez Segro dans le 13ème arr.parisien
supplychainmagazine.fr ↗
Lineage
How this lead was derived
The signal-first chain, end to end: recent external signals → qualified niche → resolved data-holder → site verification → scored opportunity. Every lead is explainable.
Concrete evidence this company actively cares about data — why it's ripe for the deal room.
Profile
Dataset profile
Type
Industrial Operations Dataset
Modality
Time Series
Sector
mobility
Volume
Moderate
Freshness
Real-time
Rarity
High (proprietary)
Accessibility
Restricted
Legal
Owned by the company — GDPR-sensitive (PII review)
Buyer persona
Industrial AI integrators
Gophr's Industrial Operations Dataset provides a rich collection of Time Series data, including event_streams, proprietary geo_data, and other industrial data from its extensive mobility and logistics operations. This structured dataset is uniquely suited for Industrial Monitoring applications, enabling AI models to analyze and predict fleet efficiency, delivery route performance, and operational patterns with high precision.
The business value of this data is underscored by the global Supply Chain Analytics market, which was valued at approximately $7.16 billion in 2023 and is projected to grow at a 17.8% CAGR through 2030. [4] While access requires navigating PII anonymization and proprietary data considerations, the dataset's high technical maturity and structured API access make it a valuable and rare asset for AI buyers seeking a competitive edge in the rapidly expanding logistics and industrial efficiency sector. ⚠ Diligence (valuable data, access to negotiate): Contains PII (names, addresses) requiring anonymization for AI training; Route telemetry is proprietary but delivery contents are client-owned; High technical maturity via API suggests data is well-structured but protected · corporate: independent.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
This evidence collectively proves Gophr owns a high-rarity, proprietary dataset detailing the complete lifecycle of last-mile logistics operations in dense urban environments. The granular time-series data on delivery performance, vehicle efficiency, and carbon emissions is a critical asset for Industrial AI integrators. It directly enables the development of sophisticated industrial monitoring and route optimization models, targeting a global supply chain analytics market growing at a CAGR of 17.8%.
See dimension details ↓- Dataset Specificity90
dominant 'industrial_data', sector mobility, 3 specific types
How sharply the data targets a specific, hard-to-substitute domain or task. Niche, well-defined data scores higher than generic. - Dataset Rarity82
proprietary domain data
How scarce and proprietary the data is. Unique domain data scores high; openly available data lowers it. - Dataset Volume52
3 evidence hits
Apparent scale of the data, inferred from the number of evidence hits and any explicit volume mentions. - Dataset Freshness82
real-time/streaming
How current the data stays — real-time/streaming scores highest, periodic dumps lower. - Training Value84
fit for Industrial Monitoring
How useful the data is for the target AI use-case — its fit for model training or fine-tuning. - Buyer Demand90
AI buyer demand is exceptionally high, driven by the strong growth in the Supply Chain Analytics market, which is projected to grow at a 17.8% CAGR as companies increasingly adopt data-driven strategies for efficiency. [4]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility20
restricted/unknown
How legally easy the data is to obtain and use — open/API access scores high; PII or regulated data scores low. - Acquisition Feasibility30
medium difficulty, independent
How realistic it is to actually obtain the data, given access difficulty and the holder's corporate structure. - Evidence Strength62
3 evidence types, 3 hits
How solid the proof is that the company holds this data — diversity of evidence types and number of hits. - Right to License62
ownership=owned, licensing=gdpr_sensitive
Whether the company can legally license the data out — based on ownership and licensing complexity. - Corporate Independence90
independent
Whether the holder can decide alone — an independent company scores higher than a subsidiary of a large group. - Data Orientation56
2 data-appetite signals (2 types)
How actively the company invests in data, measured by its data-appetite signals (hires, products, APIs…). - Dormant Data Surplus92
surplus=high, 3 recent external signals — proprietary data beyond what's already monetised
Volume and value of proprietary data this company holds BEYOND what it already monetises — the dormant surplus we can unlock. A company can sell some insights AND still sit on a far larger dormant asset. - ICP Audit75
⚠ review — Gophr's core business is selling logistics-as-a-service, including an open API for delivery booking and tracking, making it a technology vendor and not a holder of dormant data. Issues: Company's core product is a technology platform for logistics, not just a courier service.; They actively sell an open API for businesses to integrate delivery services, which constitutes selling intelligence/a data service. [18, 21]; The company describes itself as a 'delivery technology platform' and its product as a 'delivery command center', which is a form of selling intelligence. [4, 21
- Deep Qualification80
✓ pass — Gophr is a logistics operator, not a data seller. Its operational data, rich in time-series and geo-data, is a dormant byproduct of its core courier business. The dataset is plausible and valuable, but data ownership is mixed and involves sensitive PII under GDPR, making access complex.
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
Geospatial data
This tabular data provides historical and real-time route data for multi-modal fleets across major UK cities, enabling the modeling of urban logistics networks and traffic patterns.
Event streams
This time-series evidence captures granular delivery lifecycle events, including failed delivery patterns and unique doorstep dwell time metrics, which is essential for training models that predict and mitigate operational bottlenecks.
Industrial data
This time-series data quantifies carbon emissions per delivery and the comparative efficiency of different courier modes, directly supporting the creation of sustainable and cost-optimized logistics AI.
Marketplace
Dataset details
Detailed schema & sample available on access request.
Coverage
Scanned sources
Deliverable
Premium dataset report
Gophr Industrial Operations — a Moderate industrial operations dataset (Time Series modality) in the mobility domain. Primary AI use-case: Industrial Monitoring. Market signal: Global Supply Chain Analytics market = $7.16B in 2023, CAGR 17.8% (source: Grand View Research). Investment score 48.0/100 (confidence 0.49). Recommended action: Data Sharing Agreement.
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