Dataset opportunity
Faganwhalley — 移动事件数据集机会
Faganwhalley 持有的中等规模移动事件数据集，可用于预测和异常检测。
Score
77.4
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
58%
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)
全球供应链分析市场在 2022 年的估值为 61.2 亿美元，预计从 2023 年到 2030 年的复合年增长率为 17.8%。
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
移动事件数据集
Modality
时间序列
Sector
mobility
Volume
中等
Freshness
实时
Rarity
高（专有）
Accessibility
受限
Legal
混合所有权 — 需明确许可权
Buyer persona
Quant funds & demand-forecasting AI teams
Faganwhalley 持有一个全面的移动事件数据集，结构为时间序列数据，包含 `event_streams`、`iot_data`、`industrial_data` 和 `maintenance_logs`。这些丰富的时间数据提供了详细的运营视图，使其对人工智能驱动的预测极具价值，可用于预测维护需求、优化供应链流程和管理库存。
全球供应链分析市场在 2022 年的估值为 61.2 亿美元，预计到 2030 年将以 17.8% 的复合年增长率增长至 224.6 亿美元。[1] 这种显著的增长凸显了市场对这类数据的强烈需求。虽然访问需要应对 GDPR 等复杂问题（由于存在驾驶员远程信息处理数据）和客户保密协议，但该数据集的稀有性和集成性为寻求提高预测准确性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）：远程信息处理数据涉及驾驶员行为，可能涉及 GDPR 问题；仓库库存数据可能受客户保密协议的约束；供应链流程数据的所有权可能与最终客户共享 · 公司：独立。
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
这些证据证明 Faganwhalley 拥有一个专有的、高频的移动事件数据流，直接来源于其集成物流网络。该数据集结合了物联网传感器的输入、仓库管理系统的输出以及车辆维护日志，创造了对现实世界供应链运营的独特而全面的视图。对于量化基金和人工智能预测团队来说，该数据集为建模供应链活动和经济指标提供了稀有的真实信号，为预测年增长率超过 17% 的供应链分析市场中的中断提供了原材料。
See dimension details ↓- Dataset Specificity100
主导的 'event_streams'，行业 mobility，4 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 Rarity94
专有领域数据
How scarce and proprietary the data is. Unique domain data scores high; openly available data lowers it. - Dataset Volume64
5 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 Value94
适用于 Forecasting
How useful the data is for the target AI use-case — its fit for model training or fine-tuning. - Buyer Demand90
人工智能买家需求异常高，这得益于在复合年增长率为 17.8% 的供应链分析市场中对准确预测的迫切需求。[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 Feasibility30
中等难度，独立
How realistic it is to actually obtain the data, given access difficulty and the holder's corporate structure. - Evidence Strength77
4 种证据类型，5 个命中
How solid the proof is that the company holds this data — diversity of evidence types and number of hits. - Right to License36
所有权=混合，许可=权利不明确
Whether the company can legally license the data out — based on ownership and licensing complexity. - Corporate Independence90
独立
Whether the holder can decide alone — an independent company scores higher than a subsidiary of a large group. - Data Orientation56
2 个数据需求信号（2 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 — 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 Audit92
✓ 良好目标 — Fagan & Whalley 是一个主要目标，作为一家成熟的、家族经营的物流和仓储公司，拥有大量车队，其核心运营业务的副产品产生了专有移动数据，并且没有出售数据产品的迹象。问题：该公司是一家数百万英镑的全国性企业，近期进行了收购，因此尽管它似乎是一家中小企业，但规模较大且在增长；虽然他们通过门户网站为客户提供数据洞察和对其自身供应链的可见性，但这是其核心物流服务的一项功能，而不是
- Deep Qualification80
✓ 通过 — Fagan & Whalley 是一个强大的数据持有者候选者。作为一家物流和仓储提供商，它作为其核心服务的副产品产生了大量运营数据，包括有价值的远程信息处理和 WMS 数据。虽然数据所有权是混合的并受 GDPR 约束，但近期对新的 TMS 和 WMS 平台的大量投资证实了一个丰富、结构化的移动数据集的存在。
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
Event streams
该公司从其运营中生成连续的事件流，提供人工智能团队构建预测预测模型所需的实时数据和高级洞察。
IoT / sensor data
该数据集包含专有的物联网数据，提供模型资产移动和供应链效率所需的即时跟踪和按需状态可见性。
Industrial data
证据表明存在来自仓库管理系统和车队的丰富工业数据来源，提供了对运营能力和活动的全面视图。
Maintenance logs
该数据集通过维护日志得到丰富，提供了车辆健康和可用性的独特信号，可用于预测资产停机和潜在的供应链中断。
Marketplace
Dataset details
Detailed schema & sample available on access request.
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Coverage
Scanned sources
Deliverable
Premium dataset report
Faganwhalley Mobility Event — a Moderate mobility event dataset (Time Series modality) in the mobility domain. Primary AI use-case: Forecasting. Market signal: Global Supply Chain Analytics market was valued at USD 6.12 billion in 2022, projected to grow at a CAGR of 17.8% from 2023 to 2030 (source: Grand View Research). [1]. Investment score 77.4/100 (confidence 0.58). Recommended action: Acquire.
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