Dataset opportunity
Suivideflotte — 移动遥测数据集机会
Suivideflotte 持有的大型移动遥测数据集，可用于预测性维护和异常检测。
Score
73.3
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
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
全球汽车预测性维护市场 = 2025年50.40亿美元，复合年增长率21%（2026-2032年）
Recent dated external facts that triggered this opportunity — auditable provenance.
- 📰press2026-06-04
Knight-Swift founder, executive chairman Kevin Knight retires
freightwaves.com ↗ - 📰press2026-06-04
Freight distress spreads as bankruptcies, layoffs top 600 jobs
freightwaves.com ↗ - 📰press2026-06-04
Le cabinet Bartle recrute Hélène Lebeau comme directrice SC
supplychainmagazine.fr ↗ - 📰press2026-06-04
S&P Global warns of looming problems at Odyssey as it cuts rating
freightwaves.com ↗ - 📰press2026-06-03
Supreme Court decision raises stakes for broker hiring practices
supplychaindive.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
移动遥测数据集
Modality
时间序列
Sector
移动出行
Volume
大
Freshness
实时
Rarity
高（专有）
Accessibility
受限
Legal
混合所有权 — GDPR敏感（个人身份信息审查）
Buyer persona
工业AI和维护优化供应商
Suivideflotte 拥有丰富的移动遥测数据集，以时间序列模式呈现，包含来自客户车辆的物联网数据、事件流和地理数据。这些实时车辆数据提供了关于车辆性能、部件磨损和驾驶模式的精细洞察，使其对预测性维护应用具有极高的价值。其全面的性质有助于识别异常并预测潜在的机械故障，从而实现主动维护并减少停机时间。
此类数据的商业价值巨大，汽车预测性维护市场预计到2032年将达到191.42亿美元，复合年增长率（CAGR）为21%（2026-2032年）。此外，更广泛的汽车数据变现市场预计到2035年将达到30.04亿美元，复合年增长率（CAGR）为12.9%（2026-2035年）。尽管获取这些数据存在复杂性，需要明确的二次使用协议和强大的GDPR敏感匿名化或同意机制，但显著的市场增长突显了其对AI买家的宝贵潜力。⚠ 尽职调查（有价值的数据，可协商的访问权限）：数据来自客户车辆，需要明确的二次使用协议；位置和驾驶员行为数据属于GDPR敏感信息，需要强大的匿名化或同意机制。· 企业：独立。
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
Suivideflotte 提供高度专有且广泛的移动遥测数据集，源自超过60,000辆配备车辆，提供了丰富的时间序列车辆运营视图。这种独特的物联网传感器数据、驾驶行为和地理位置集合正是工业AI和维护优化供应商开发先进预测性维护解决方案所需的。随着全球汽车预测性维护市场预计到2025年达到50.40亿美元，该数据集提供了一个关键机会，通过实现卓越的AI模型来抢占重要的市场份额。
See dimension details ↓- Dataset Specificity90
主导的“物联网数据”，移动出行领域，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 Volume74
4个证据命中，明确提及数据量
Apparent scale of the data, inferred from the number of evidence hits and any explicit volume mentions. - Dataset Freshness82
实时/流式
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
全球人工智能驱动的预测性维护市场预计将以39.5%的复合年增长率（CAGR）增长，到2032年达到192.7亿美元，这表明对支持这些解决方案的数据需求非常高且不断增长。
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility20
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 Strength74
4种证据类型，4个命中
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 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，5个近期外部信号 — 超出已变现范围的专有数据
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 Audit75
⚠ 审查 — SuiviDeFlotte 的核心业务是提供SaaS车队管理解决方案，该方案利用专有遥测数据和人工智能驱动的分析为其客户提供智能和洞察，使其成为竞争对手而非拥有休眠数据的持有者。问题：该公司的核心业务是作为其车队管理SaaS解决方案的一部分销售智能（人工智能软件、分析、洞察），这明确排除；收集的数据并非休眠数据；它被积极使用
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
Geospatial data
这些表格数据为大型车队提供实时车辆地理位置、移动警报和地理围栏功能，为运营效率和路线优化提供了关键背景。
IoT / sensor data
这些时间序列数据捕获了来自嵌入式设备的关键车辆遥测信息，包括发动机状态、燃油消耗和维护警报，直接支持先进的预测性维护模型。
Event streams
这些时间序列数据包含驾驶行为（如速度、制动和加速）以及驾驶员识别信息，对于风险评估、保险和理解车辆应力因素至关重要。
Data-volume signal
这些多模式证据证实了数据集的巨大规模，源自超过60,000辆受监控车辆，为AI模型训练和泛化提供了强大的统计能力。
Marketplace
Dataset details
Geographic coverage
Global
Time range
Real-time
Update frequency
Real-time
Delivery
API
Formats
Time Series, IoT data, Event streams, Geo-data
License
One-time license for predictive maintenance use cases, with potential restrictions on redistribution.
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 proprietary, real-time mobility telemetry dataset offers high-value insights for predictive maintenance in the rapidly growing automotive sector. The substantial volume and high freshness of the data, combined with its exclusive nature, justify a premium valuation.
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
Suivideflotte Mobility Telemetry — a Large mobility telemetry dataset (Time Series modality) in the mobility domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Automotive Predictive Maintenance market = US$ 50.40 Billion in 2025, CAGR 21% (2026-2032). Investment score 73.3/100 (confidence 0.56). Recommended action: Data Sharing Agreement.
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