Dataset opportunity
Beardconstruction — Public Procurement Dataset Opportunity
Moderate public procurement dataset held by Beardconstruction, usable for Tender Intelligence and Document Intelligence.
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
56%
Action
Acquire
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 Procurement Analytics market was valued at $3.8 billion in 2022, with a projected CAGR of 23% (2023-2032).
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
Public Procurement Dataset
Modality
Text
Sector
industrial
Volume
Moderate
Freshness
Real-time
Rarity
High (proprietary)
Accessibility
Partial
Legal
Owned by the company — clean to license
Buyer persona
GovTech & procurement-intelligence vendors
Beardconstruction holds a valuable Public Procurement Dataset in Text modality, derived from extensive business_records, industrial_data, and procurement files, making it exceptionally suited for Tender Intelligence applications. This data allows an AI buyer to train sophisticated models for analyzing historical bids, understanding project specifications, and mapping competitor strategies to optimize future tender submissions.
The global Procurement Analytics market was valued at $3.8 Billion in 2022 and is projected to grow at a remarkable CAGR of 23%, indicating intense demand for these data-driven insights. Although access complexities exist, such as pre-2015 historical data in legacy formats and the need for sensitive handling of supply chain performance data, the dataset's direct applicability to this high-growth market presents a compelling business case for negotiation and investment. ⚠ Diligence (valuable data, access to negotiate): Historical data (pre-2015) may be partially paper-based or in legacy formats.; BIM model ownership may be subject to specific client-contractor agreements.; Supply chain performance data involves third-party partner metrics requiring sensitive handling. · corporate: independent.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
This evidence collectively proves that Beard Construction holds a proprietary dataset detailing its public procurement processes and supply chain performance. This text-based data is in high demand from GovTech and procurement-intelligence vendors seeking to build advanced Tender Intelligence platforms. In a global procurement analytics market projected to grow at a 23% CAGR, this dataset offers a rare, real-world source for training AI models to optimize bidding strategies, predict outcomes, and evaluate supplier risk.
See dimension details ↓- Dataset Specificity90
dominant 'procurement', 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 Freshness82
real-time/streaming
How current the data stays — real-time/streaming scores highest, periodic dumps lower. - Training Value84
fit for Tender Intelligence
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 strategic need to leverage data for a competitive edge in the procurement analytics market, which is expanding at a rapid 23% CAGR.
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility50
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 Strength74
4 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 License92
ownership=company_owned, licensing=clean
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 — 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 — This family-owned UK construction firm is an ideal target, as its core business is building projects, which generates a significant amount of proprietary operational data as a by-product without any indication they currently monetize it. Issues: The initial hint about a 'Public Procurement Dataset' is not directly substantiated in the company's description of its services; the data opportunity is inferr
- Deep Qualification90
⚠ needs review — The target is a construction services company; while it certainly holds the specified procurement dataset, this data is contractually owned by its clients, making it unavailable for third-party resale. [data is owned by the company's customers; licensing restricted]
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
business_records
This evidence confirms the company's 130-year history and commitment to best practice, providing essential provenance that signals data source stability and reliability to buyers.
IoT / sensor data
This sample indicates the use of modern Building Information Modeling (BIM) systems, generating sophisticated project data used for cost estimation and scheduling, which is highly valuable for training predictive construction models.
Procurement / tenders
This text data is direct evidence of a structured system for evaluating supply chain partners on metrics like tender support, providing a unique and proprietary source for training AI models on supplier performance and risk.
Industrial data
This finding points to underlying reports that quantify the social and economic value of projects, offering a dataset to model the wider financial impact of construction activities.
Marketplace
Dataset details
Detailed schema & sample available on access request.
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Coverage
Scanned sources
Deliverable
Premium dataset report
Beardconstruction Public Procurement — a Moderate public procurement dataset (Text modality) in the industrial domain. Primary AI use-case: Tender Intelligence. Market signal: Global Procurement Analytics market was valued at $3.8 billion in 2022, with a projected CAGR of 23% (2023-2032) (source: Global Market Insights, Inc.).. Investment score 77.9/100 (confidence 0.56). Recommended action: Acquire.
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