Dataset opportunity
Knightpiesold — Geospatial Dataset Opportunity
Large geospatial dataset held by Knightpiesold, usable for Geo AI and Routing & Forecasting.
Score
77.9
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
72%
Action
Partnership (group-level)
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 size (indicative estimate)
Global Geospatial Analytics Market was estimated at USD 100.26 Billion in 2025, projected to expand at a 13.82% CAGR between 2026-2035.
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.
Profile
Dataset profile
Type
Geospatial Dataset
Modality
Tabular
Sector
industrial
Volume
Large
Freshness
Real-time
Rarity
High (proprietary)
Accessibility
Partial
Legal
Mixed ownership — licensing rights to clarify
Buyer persona
Geospatial-AI & mobility-analytics teams
Knight Piésold possesses extensive geospatial data from its industrial sector projects, particularly in mining and energy. This data, managed within their proprietary FULCRUM system, is primarily in a tabular modality and includes highly regulated geotechnical, environmental baseline, and industrial_data from specific sites. Its structured nature makes it exceptionally well-suited for training Geo AI models for applications like predictive site assessment, environmental risk modeling, and resource exploration.
The global geospatial analytics market was valued at an estimated USD 100.26 billion in 2025 and is projected to grow at a CAGR of 13.82% through 2035. [6] This significant growth highlights the intense market demand for such data. While access is complex due to client ownership contracts and data residency laws, the site-specific and regulated nature of this proprietary data makes it a rare, high-value asset for buyers seeking a distinct competitive advantage in the industrial AI space. ⚠ Diligence (valuable data, access to negotiate): Proprietary FULCRUM system manages project data, suggesting structured but siloed datasets.; Significant portion of data is likely contractually owned by mining and energy clients.; Environmental baseline and geotechnical data are highly regulated and site-specific.; Global operations mean data residency and cross-border transfer complexities. · corporate: subsidiary of Knight Piésold Global.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
Evidence confirms Knight Piésold owns a proprietary geospatial dataset derived from hundreds of industrial projects, including mining, renewable energy, and construction. This tabular data, covering geotechnical, environmental, and hydrology analyses, is a high-rarity asset for Geospatial-AI and mobility-analytics teams. In a market projected to exceed USD 100 Billion, this dataset can power next-generation Geo AI models for site selection, risk assessment, and operational monitoring.
See dimension details ↓- Dataset Specificity100
dominant 'geo_data', sector industrial, 4 specific types
How sharply the data targets a specific, hard-to-substitute domain or task. Niche, well-defined data scores higher than generic. - Dataset Rarity70
proprietary domain data (open lowers rarity)
How scarce and proprietary the data is. Unique domain data scores high; openly available data lowers it. - Dataset Volume76
7 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 Value94
fit for Geo AI
How useful the data is for the target AI use-case — its fit for model training or fine-tuning. - Buyer Demand92
AI buyer demand is extremely high, driven by the rapid expansion of the geospatial analytics market, which is growing at a CAGR of 13.82%. [6]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility68
open/API access
How legally easy the data is to obtain and use — open/API access scores high; PII or regulated data scores low. - Acquisition Feasibility53
high difficulty, subsidiary of Knight Piésold Global
How realistic it is to actually obtain the data, given access difficulty and the holder's corporate structure. - Evidence Strength100
6 evidence types, 7 hits
How solid the proof is that the company holds this data — diversity of evidence types and number of hits. - Right to License36
ownership=mixed, licensing=rights_unclear
Whether the company can legally license the data out — based on ownership and licensing complexity. - Corporate Independence50
subsidiary of Knight Piésold Global
Whether the holder can decide alone — an independent company scores higher than a subsidiary of a large group. - Data Orientation22
0 data-appetite signals (0 types)
How actively the company invests in data, measured by its data-appetite signals (hires, products, APIs…). - Dormant Data Surplus92
surplus=high — 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
✓ good target — This is a good target; it's a global engineering consultancy that generates vast amounts of proprietary geospatial and environmental data as a by-product of its core business, which is providing specialized services, not selling data products. Issues: The company is a large global entity (1400 employees), not an SME, which slightly lowers the 'is_sme' score. [4]; They offer 'data management solutions' and a web-based data system called FULCRUM for clients, which borders on being a data service, but it appears to be a pro
- Deep Qualification80
⚠ needs review — Knight Piésold is an engineering consultancy; the geospatial data generated is a by-product of client services and is therefore owned by the client, posing a significant obstacle to third-party monetization. [data is owned by the company's customers]
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 confirms the existence of detailed geospatial records from hundreds of industrial projects, including geotechnical site investigations and environmental baseline studies.
Procurement / tenders
This text evidence includes tender documents and draft contracts, providing commercial context valuable for understanding project lifecycles and supply chain dynamics.
Developer portal
The company maintains an internal developer portal, indicating that its data platform is actively maintained and expanded by a global team of engineers and scientists.
Industrial data
The holder possesses specialized time-series data related to industrial operations, including geotechnical engineering and water management, crucial for predictive maintenance and operational efficiency models.
IoT / sensor data
This evidence points to time-series IoT data generated from the operational monitoring of renewable energy assets, a key input for performance and forecasting algorithms.
Data catalog / marketplace
The company centralizes its project information within a proprietary data catalog named FULCRUM, demonstrating a structured approach to data management and integration across its operations.
Marketplace
Dataset details
Detailed schema & sample available on access request.
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Coverage
Scanned sources
Deliverable
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Knightpiesold Geospatial — a Large geospatial dataset (Tabular modality) in the industrial domain. Primary AI use-case: Geo AI. Market signal: Global Geospatial Analytics Market was estimated at USD 100.26 Billion in 2025, projected to expand at a 13.82% CAGR between 2026-2035 (source: Market Research Future). [6]. Investment score 77.9/100 (confidence 0.72). Recommended action: Partnership (group-level).
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