Dataset opportunity
Haagglobal — Geospatial Dataset Opportunity
Moderate geospatial dataset held by Haagglobal, usable for Geo AI and Routing & Forecasting.
Score
69.2
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
51%
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 to grow from $117.30 billion in 2026 to $309.84 billion by 2034, at a 12.90% CAGR.
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.
- ✨Signal
Haag Education monetizes proprietary damage assessment methodologies and certification data
source ↗
Profile
Dataset profile
Type
Geospatial Dataset
Modality
Tabular
Sector
industrial
Volume
Moderate
Freshness
Periodic
Rarity
High (proprietary)
Accessibility
Restricted
Legal
Owned by the company — licensing rights to clarify
Buyer persona
Geospatial-AI & mobility-analytics teams
Haagglobal holds a proprietary Geospatial Dataset in a Tabular modality, which integrates `geo_data`, `industrial_data`, and forensic `inspection_records`. This structured collection provides a unique ground-truth resource for training Geo AI models, enabling detailed analysis of industrial assets, risk assessment, and forensic engineering investigations by correlating physical inspection outcomes with specific geographic locations.
The global Geospatial Analytics market is a significant and rapidly expanding sector, projected to grow from USD 117.30 billion in 2026 to USD 309.84 billion by 2034, demonstrating a 12.90% CAGR. [9] Despite access complexities such as shared data ownership or the need for de-identification of forensic records, the dataset's value is substantial. Its rarity and detailed nature offer a distinct competitive advantage for AI buyers developing advanced models for insurance underwriting, predictive maintenance, and disaster modeling. ⚠ Diligence (valuable data, access to negotiate): Data ownership may be shared with insurance carriers or legal clients in specific forensic cases; Subsidiary of Salas O'Brien, requiring group-level coordination for data licensing; Forensic records require significant de-identification to remove PII and specific property addresses · corporate: subsidiary of Salas O'Brien.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
This evidence proves the holder owns a proprietary, multi-modal dataset combining nearly a century of forensic engineering reports with high-resolution 3D geospatial data from disaster sites. This unique collection of ground-truth information on structural failure is a rare asset for Geospatial-AI teams building next-generation models for risk assessment, insurance underwriting, and infrastructure resilience. In a geospatial analytics market projected to reach over $300 billion by 2034, this dataset offers a distinct competitive edge by providing data on how the built environment *actually* behaves under stress.
See dimension details ↓- Dataset Specificity90
dominant 'geo_data', sector industrial, 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 Volume58
4 evidence hits
Apparent scale of the data, inferred from the number of evidence hits and any explicit volume mentions. - Dataset Freshness46
periodic
How current the data stays — real-time/streaming scores highest, periodic dumps lower. - Training Value84
fit for Geo AI
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 of the geospatial analytics market which is expanding at a 12.90% CAGR. [9]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility28
restricted/unknown
How legally easy the data is to obtain and use — open/API access scores high; PII or regulated data scores low. - Acquisition Feasibility15
medium difficulty, subsidiary of Salas O'Brien
How realistic it is to actually obtain the data, given access difficulty and the holder's corporate structure. - Evidence Strength65
3 evidence types, 4 hits
How solid the proof is that the company holds this data — diversity of evidence types and number of hits. - Right to License70
ownership=company_owned, licensing=rights_unclear
Whether the company can legally license the data out — based on ownership and licensing complexity. - Corporate Independence50
subsidiary of Salas O'Brien
Whether the holder can decide alone — an independent company scores higher than a subsidiary of a large group. - Data Orientation39
1 data-appetite signals (1 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 Audit92
✓ good target — The company is a strong fit, as it's a forensic engineering and consulting firm whose core business is expert services, generating vast amounts of proprietary geospatial and material damage data as a valuable, unmonetized by-product. Issues: The company's core business is selling 'intelligence' in the form of expert consulting reports and testimony, which could be narrowly interpreted as a conflict ; Haag Global was acquired by Salas O'Brien in June 2024 and is now 'Haag, a Salas O'Brien Company'. [2] This may change its operational independence or data stra
- Deep Qualification90
⚠ needs review — Haag is a forensic services firm whose data is a by-product of client-specific engagements. Data ownership is likely shared with clients, and the recent acquisition by Salas O'Brien introduces corporate complexity, making direct data licensing highly restricted and improbable. [licensing restricted]
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
Geospatial data
The holder possesses high-resolution tabular data from 3D laser scans, BIM models, and GIS captured during real-world forensic inspections, offering unparalleled ground-truth for training Geo AI models on structural integrity.
Inspection reports
This is a deep historical archive of forensic engineering reports dating back to 1924, detailing the root causes of structural and mechanical failures to provide invaluable context for predictive maintenance algorithms.
Industrial data
The dataset includes proprietary time-series data from accredited lab tests on building material wind resistance and durability, enabling AI models to precisely link specific materials to real-world performance and risk.
Marketplace
Dataset details
Detailed schema & sample available on access request.
Want this data?
Request access — we broker a secure deal room. Operator-reviewed, no automatic sharing.
This listing was generated automatically from public signals. It is not verified, and we are not affiliated with this company.
Coverage
Scanned sources
Deliverable
Premium dataset report
Haagglobal Geospatial — a Moderate geospatial dataset (Tabular modality) in the industrial domain. Primary AI use-case: Geo AI. Market signal: Global Geospatial Analytics market to grow from $117.30 billion in 2026 to $309.84 billion by 2034, at a 12.90% CAGR (source: Fortune Business Insights). [9]. Investment score 69.2/100 (confidence 0.51). Recommended action: Partnership (group-level).
From the marketplace
Explore live data opportunities
Humatics — Industrial Operations Dataset Opportunity
View opportunity →industrialAutrix — Industrial Operations Dataset Opportunity
View opportunity →mobilityOpti Logistics — Industrial Operations Dataset Opportunity
View opportunity →Data Academy
Learn before you deal
- How a Data Transaction Works3 min read
- What you are entitled to sell3 min read
- 5 Mistakes That Drive Buyers Away3 min read