Dataset opportunity
Sinloc — Public Procurement Dataset Opportunity
Moderate public procurement dataset held by Sinloc, usable for Tender Intelligence and Document Intelligence.
Score
69.9
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
49%
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
Global Procurement Data Intelligence Market was valued at $2.6 billion in 2022, and is projected to reach $15.80 billion by 2030, at a CAGR of 25.3% (source: VMR). [14]
Recent dated external facts that triggered this opportunity — auditable provenance.
- 📰press2026-06-09
Medtronic set for Stealth AXiS expansion in Europe
medtechdive.com ↗
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
finance
Volume
Moderate
Freshness
Real-time
Rarity
High (proprietary)
Accessibility
Restricted
Legal
Mixed ownership — licensing rights to clarify
Buyer persona
GovTech & procurement-intelligence vendors
Sinloc holds a uniquely valuable Public Procurement Dataset in text modality, enriched with proprietary geo_data from urban development projects and iot_data from energy plants. This composite dataset provides a multi-dimensional view of public tenders, far exceeding standard procurement text. It enables a sophisticated Tender Intelligence use case, allowing AI buyers to analyze not just the contractual details but also the geographic and operational context of projects, thus uncovering hidden risks and opportunities to create a significant bidding advantage.
The global Procurement Analytics Market was valued at USD 2.6 billion in 2022 and is projected to reach USD 15.80 billion by 2030, growing at a CAGR of 25.3%. [14] This high growth signals intense demand from AI buyers for precisely this type of data to optimize decision-making. [14] While access is complex due to co-ownership with partners, PPP restrictions, and siloed data structures, this complexity is the very source of the dataset's rarity and high value. Negotiating access is worthwhile for buyers seeking a distinct competitive edge in a rapidly expanding market. ⚠ Diligence (valuable data, access to negotiate): Operational data from energy plants is co-owned with industrial partners like Repower Renewable; Urban development data is often tied to Public-Private Partnerships (PPP) with municipal restrictions; Data is siloed across different Special Purpose Vehicles (SPVs) and investment funds · corporate: independent.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
This evidence confirms Sinloc's ownership of a proprietary dataset detailing large-scale public procurement and infrastructure projects, including investment values and monitoring phases. This data is a critical asset for GovTech and procurement-intelligence vendors seeking to build advanced tender intelligence models. In a market projected to grow at over 25% annually, this unique dataset provides the ground truth needed to predict project outcomes, assess risk, and gain a competitive advantage.
See dimension details ↓- Dataset Specificity90
dominant 'procurement', sector finance, 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 Tender Intelligence
How useful the data is for the target AI use-case — its fit for model training or fine-tuning. - Buyer Demand92
The demand is directly correlated with the global Procurement Data Intelligence market, which is projected to grow from $2.6 billion in 2022 to $15.80 billion by 2030, at a very high CAGR of 25.3%. [19]
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 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 License36
ownership=mixed, licensing=rights_unclear
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 Surplus70
surplus=medium, 1 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 Audit50
⚠ review — Sinloc's core business is investment and advisory services for local development and public-private partnerships, not owning operational assets that generate data as a by-product, making it a bad fit. Issues: The company's core business is selling intelligence and consultancy services, which is an explicit exclusion criterion. [1, 2, 5, 21]; The company analyzes data (often public or from clients) to provide feasibility studies, advisory, and investment strategies; it does not generat
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
IoT / sensor data
The holder possesses time-series data on the operational output of infrastructure like photovoltaic plants, offering a unique signal for assessing long-term asset performance and project viability.
Geospatial data
This tabular data links specific project types, such as urban regeneration and social housing, to precise locations, enabling powerful regional analysis of public investment trends.
Procurement / tenders
The dataset contains text-based evidence of large-scale public project investments and lifecycle oversight, including monitoring services, which is critical for training AI to predict tender success and project costs.
Coverage
Scanned sources
Deliverable
Premium dataset report
Sinloc Public Procurement — a Moderate public procurement dataset (Text modality) in the finance domain. Primary AI use-case: Tender Intelligence. Market signal: Global Procurement Data Intelligence Market was valued at $2.6 billion in 2022, and is projected to reach $15.80 billion by 2030, at a CAGR of 25.3% (source: VMR). [14]. Investment score 69.9/100 (confidence 0.49). Recommended action: Acquire.