Dataset opportunity
Magil — 工业传感器数据集机会
Magil 持有的中等规模工业传感器数据集，可用于预测性维护和异常检测。
Score
72.5
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
合作（集团层面）
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)
全球预测性维护市场在 2025 年的估值为 142 亿美元，预计从 2026 年到 2033 年的复合年增长率为 27.9%（来源：Grand View Research）。[1]
Recent dated external facts that triggered this opportunity — auditable provenance.
- 📰press2026-08-04
Little Mountain's third social housing building now built, with all of Holborn's promised units reaching 100% completion this fall
dailyhive.com ↗ - 📰press2026-08-03
Balfour Beatty tops out $385M Miami Beach hotel
constructiondive.com ↗ - 📰press2026-07-31
HS2 renegotiates contracts with construction giants
constructionenquirer.com ↗ - 📰press2026-07-29
City council approves Vancouver’s tallest tower project
constructioncanada.net ↗
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
工业传感器数据集
Modality
时间序列
Sector
工业
Volume
中等
Freshness
实时
Rarity
高（专有）
Accessibility
受限
Legal
公司所有 — 许可权待澄清
Buyer persona
工业人工智能与维护优化供应商
Magil 拥有一份宝贵的工业传感器数据集，该数据集以时间序列模式进行结构化，整合了其建筑项目中的真实工业数据和物联网数据。该集合为开发和验证高保真预测性维护模型提供了坚实的基础，因为它捕捉了重型设备在真实现场条件下的运行压力和性能。
商业机会巨大，全球预测性维护市场在 2025 年的价值为142 亿美元，预计将以27.9% 的复合年增长率扩张。[1] 虽然访问需要应对合同中数据所有权共享和数据平台孤岛等复杂性，但该数据集的稀有性和特异性为快速增长的市场中的人工智能买家提供了独特的竞争优势。⚠ 尽职调查（有价值的数据，可协商的访问权限）：数据所有权可能与项目开发商（例如 Brivia Group）合同共享；；关于项目成本和专有建筑方法的商业机密性很高；；数据孤岛分布在各种 CDE 平台（Procore、Revizto）和遗留系统中。· 公司：Fayolle Group 的子公司。
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
这些证据证明 Magil 拥有专有的结构化工业数据流，包括来自高级资产信息模型（7D BIM）的时间序列信号，这些信号构成了数字孪生的基础。该数据集非常适合训练复杂的预测性维护算法，使人工智能供应商能够对资产故障进行建模并优化运营效率。在全球市场预计每年增长近 28% 的情况下，这种稀有的真实世界数据为开发和验证下一代工业人工智能解决方案提供了显著的竞争优势。
See dimension details ↓- Dataset Specificity90
占主导地位的'物联网数据'，工业领域，3 种特定类型
How sharply the data targets a specific, hard-to-substitute domain or task. Niche, well-defined data scores higher than generic. - Dataset Rarity82
专有领域数据
How scarce and proprietary the data is. Unique domain data scores high; openly available data lowers it. - Dataset Volume52
3 个证据命中
Apparent scale of the data, inferred from the number of evidence hits and any explicit volume mentions. - Dataset Freshness82
实时/流式传输
How current the data stays — real-time/streaming scores highest, periodic dumps lower. - Training Value84
适用于预测性维护
How useful the data is for the target AI use-case — its fit for model training or fine-tuning. - Buyer Demand90
人工智能买家需求极高，这得益于预测性维护市场的快速扩张，预计该市场将以 27.9% 的复合年增长率增长。[1]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility28
受限/未知
How legally easy the data is to obtain and use — open/API access scores high; PII or regulated data scores low. - Acquisition Feasibility15
中等难度，Fayolle Group 的子公司
How realistic it is to actually obtain the data, given access difficulty and the holder's corporate structure. - Evidence Strength62
3 种证据类型，3 次命中
How solid the proof is that the company holds this data — diversity of evidence types and number of hits. - Right to License70
所有权=已拥有，许可=权利不明确
Whether the company can legally license the data out — based on ownership and licensing complexity. - Corporate Independence50
Fayolle Group 的子公司
Whether the holder can decide alone — an independent company scores higher than a subsidiary of a large group. - Data Orientation73
3 个数据需求信号（3 种类型）
How actively the company invests in data, measured by its data-appetite signals (hires, products, APIs…). - Dormant Data Surplus92
盈余=高，4 个近期外部信号 — 专有数据超出已货币化的部分
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 Audit83
✓ 良好目标 — Magil 是一家大型建筑和工程公司，其核心业务是建造，而不是销售数据；它大量使用 LiDAR、BIM 和现场管理平台等现代技术，作为副产品生成大量运营数据，使其成为一个强大的目标。问题：该公司有 580-854 名员工，比典型中小企业多；；它是 Fayonne Ltd. 的子公司，这可能会使决策复杂化；；该公司已经非常注重技术，使用“大数据”等术语，因此他们可能有内部数据策略正在开发中。
- Deep Qualification90
✓ 通过 — Magil 是一家建筑承包商，不销售数据，但拥有有价值的、休眠的数据集。它使用物联网、BIM 和 CDE 平台等现代技术生成广泛的工业数据，包括传感器和设备日志。然而，由于与客户和合作伙伴共享所有权以及数据分布在 Procore 等平台之间，数据访问非常复杂。
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
IoT / sensor data
该证据表明，来自多维资产信息模型（7D BIM）的结构化时间序列数据正在生成，这对于构建支持预测性维护平台的数字孪生非常有价值。
Image collection
该公司捕获其工业资产的高分辨率LiDAR扫描和360 度图像，为时间序列数据提供关键的视觉上下文，以实现更准确的异常检测模型。
Industrial data
这证实了收集结构化大数据的明确、系统的过程，表明了数据质量高且具有可靠的来源，这对于训练企业级人工智能模型至关重要。
Marketplace
Dataset details
Detailed schema & sample available on access request.
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Coverage
Scanned sources
Deliverable
Premium dataset report
Magil Industrial Sensor — a Moderate industrial sensor dataset (Time Series modality) in the industrial domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Predictive Maintenance market was valued at $14.2 billion in 2025, projected to grow at a CAGR of 27.9% from 2026 to 2033 (source: Grand View Research). [1]. Investment score 72.5/100 (confidence 0.49). Recommended action: Partnership (group-level).
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