Dataset opportunity
Flex — 移动遥测数据集机会
由 Flex 持有的中等移动遥测数据集，可用于预测性维护和异常检测。
Score
45
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
数据共享协议
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 = 2025 年为 142 亿美元，复合年增长率为 27.9%（2026-2033 年）（来源：Grand View Research）
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.
- 📣Press / announcement
被 D&H 收购以增强技术驱动的物流和国际影响力
source ↗
Profile
Dataset profile
Type
移动遥测数据集
Modality
时间序列
Sector
移动
Volume
中等
Freshness
实时
Rarity
高（专有）
Accessibility
受限
Legal
混合所有权 — GDPR 敏感（PII 审查）
Buyer persona
工业人工智能与维护优化供应商
Flex 持有一个宝贵的移动遥测数据集，结构为时间序列数据，其中包含来自其物流和履行运营的 geo_data、iot_data 和 transaction_data。这种丰富、多模态的数据非常适合开发复杂的预测性维护模型，因为它能够将车辆和设备的使用模式与现实世界的运营事件和潜在故障指示器相关联，从而实现主动维护计划。
预测性维护的全球市场正在迅速扩张，2025 年的市场估值为142 亿美元，预计到 2033 年的复合年增长率将达到 27.9%。这一显著增长凸显了集成式、真实遥测数据集的强烈需求和稀缺性。尽管存在访问复杂性——例如，运输数据中的 PII 需要匿名化、SKU 数据所有权共享以及在新所有者 D&H Distributing 下可能发生的数据策略转变——但该数据集直接适用于这个高价值市场，使其成为 AI 买家的一个引人注目的资产。⚠ 尽职调查（有价值的数据，可协商的访问权限）：运输数据包含 PII（姓名、地址），需要严格匿名化；SKU 级别库存数据的所有权与电子商务客户共享；最近被 D&H Distributing 收购（2026 年 1 月）；数据策略可能会在“Scale”部门下集中管理。· 公司：D&H Distributing 的子公司。
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
这些证据共同证明 Flex 拥有一个由其软件驱动的物流和自动化系统生成的专有、高稀缺性数据集。该数据集的核心是实时时间序列数据，这是开发复杂预测性维护算法的基本燃料。对于工业 AI 供应商来说，这是一个独特的机会，可以获取运营遥测数据，以优化资产性能并减少停机时间，从而进入一个预计到 2025 年将达到 142 亿美元的全球市场。
See dimension details ↓- Dataset Specificity90
占主导地位的“iot_data”，移动出行行业，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 Demand92
AI 买家需求极高，这得益于预测性维护市场的快速增长，预计该市场将以 27.9% 的复合年增长率扩张。
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility0
PII/受监管
How legally easy the data is to obtain and use — open/API access scores high; PII or regulated data scores low. - Acquisition Feasibility0
中等难度，D&H Distributing 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 License28
所有权=混合，许可=GDPR 敏感
Whether the company can legally license the data out — based on ownership and licensing complexity. - Corporate Independence50
D&H Distributing 的子公司
Whether the holder can decide alone — an independent company scores higher than a subsidiary of a large group. - Data Orientation39
1 个数据需求信号（1 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，5 个近期外部信号 — 超出已货币化的专有数据
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
⚠ 审查 — 该公司是一家跨国制造和物流巨头，而非中小型企业，尽管拥有有价值的运营数据，但与 ICP 不符。问题：该公司是一家跨国巨头，拥有约 150,000-170,000 名员工，年收入超过 270 亿美元，这明确排除了在 ICP 之外。[1, 3, 9]；提供的 URL 指向母公司 Flex Ltd. 的一个特定服务线（欧洲 3PL 履行）。[1, 7, 14]；Flex 的核心业务是制造
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
press
- “<p>Montgomery has spurred a new policy at Highway.</p> <p>The post <a href="https://www.freightwaves.com/news/highway-post-montgomery-requiring-eld-hookups-for-all-carriers">Highway, post-Montgomery, requiring ELD hookups for all carriers</a> appeared first on <a href="https://www.freightwaves.com">FreightWaves</a>.</p>”
- “<p>Government relations firm Thorn Run Partners announced that a former FMC Chair would lead its new Latin America business unit.</p> <p>The post <a href="https://www.freightwaves.com/news/former-fmc-chief-sola-to-lead-thorn-run-latam-business-team">Former FMC chief Sola to lead Thorn Run LatAm business team</a> appeared first on <a href="https://www.freightwaves.com">FreightWaves</a>.</p>”
- “<p>Colis Privé, a subsidiary of Ceva Logistics, has reached a tentative agreement to acquire major units of Paack, allowing it to enter the delivery market in Spain and Portugal. </p> <p>The post <a href="https://www.freightwaves.com/news/ceva-logistics-poised-to-acquire-european-final-mile-courier-paack">Ceva Logistics poised to acquire European final-mile courier Paack</a> appeared first on <a href="https://www.freightwaves.com">FreightWaves</a>.</p>”
Transaction data
这些证据证实了来自专有订单管理系统的高交易量交易记录，对于模拟运营负载和订单模式很有价值。
Geospatial data
这些证据指向实时地理空间跟踪数据，这对于分析全球供应链中的承运商绩效和物流效率至关重要。
IoT / sensor data
这些证据表明存在来自自动化仓库系统的时间序列数据，提供了训练预测性维护模型所需的直接运营遥测数据。
Marketplace
Dataset details
Geographic coverage
Global
Time range
Real-time
Update frequency
Real-time
Delivery
API
Formats
Time Series, JSON
License
One-time license for predictive maintenance model development and deployment. Usage restrictions may apply.
Personal data
No PII
Indicative estimate, derived from public signals — not a quote, not contractual, and not agreed with the company. Is this your company? Correct it.
This dataset's high rarity as proprietary mobility telemetry, combined with strong demand from the rapidly growing predictive maintenance sector, drives its significant valuation. The real-time, time-series nature of the data is crucial for advanced AI model development.
Detailed schema & sample available on access request.
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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
Flex Mobility Telemetry — a Moderate mobility telemetry dataset (Time Series modality) in the mobility domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Predictive Maintenance market = $14.2 billion in 2025, CAGR 27.9% (2026-2033) (source: Grand View Research). Investment score 45.0/100 (confidence 0.49). Recommended action: Data Sharing Agreement.
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