Dataset opportunity
Thefuelstore — 移动遥测数据集机会
Thefuelstore 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
47.5
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
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)
全球汽车预测性维护市场规模在 2023 年为 220 亿美元，复合年增长率为 18.6%。
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.
- 📦Data product
Fuel Insights 工具提供数据驱动的车队优化
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（个人身份信息审查）
Buyer persona
工业人工智能与维护优化供应商
Thefuelstore 持有一个有价值的移动遥测数据集，该数据集源自其广泛的燃油卡网络，包含精细的 `transaction_data` 和 `iot_data`。这些时间序列信息详细说明了燃油消耗、车辆使用模式和操作时间戳，为开发和训练预测性维护算法以预测组件磨损和优化车队服务计划提供了必需的原始数据。
商业价值锚定在汽车预测性维护市场，该市场在 2023 年的估值为220 亿美元，并预计以惊人的 18.6% 复合年增长率增长。尽管存在 GDPR 和需要协商和匿名化的数据共享所有权等访问复杂性，但该数据的稀有性和真实准确性使其成为寻求在此快速扩张市场中获得竞争优势的 AI 买家的高价值资产。⚠ 尽职调查（有价值的数据，可协商访问）：包含特定于驾驶员的交易数据（GDPR）；交易记录的所有权可能与燃油网络合作伙伴（Shell、BP 等）共享；需要匿名化特定的车队身份 · 公司：独立。
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
此证据证实了 Thefuelstore 拥有专有的移动遥测数据集，该数据集结合了实时物联网信号和大规模车队交易记录。这种时间序列和表格数据的独特组合对于开发先进的预测性维护模型至关重要，这是 AI 供应商瞄准快速增长的 220 亿美元汽车优化市场的关键能力。该数据集的深度涵盖了驾驶员行为和排放，为训练复杂的人工智能解决方案提供了显著的竞争优势。
See dimension details ↓- Dataset Specificity78
主导的 'iot_data'，移动行业，2 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 Rarity70
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 Value74
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
AI 买家需求异常高，这得益于汽车预测性维护市场的显著增长，该市场正以 18.6% 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
个人身份信息/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，独立
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 Independence90
独立
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 — 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 Audit58
⚠ 审查 — 该公司的核心业务是销售车队管理解决方案，包括远程信息处理和数据驱动的洞察，使其成为市场上的竞争对手，而不是休眠数据的来源。问题：公司核心业务是销售情报和数据衍生产品。；像“Fuel Insights”和“FuelConnect 远程信息处理”这样的产品作为服务出售，以降低客户成本并提高效率。；该公司明确将自己定位为“远程信息处理服务”提供商。；该公司已开发出自己的专有“Fuel AI”平台来分析客户数据。
- Deep Qualification90
✓ 通过 — 该公司是一家车队服务提供商，其核心业务产生了有价值的遥测数据集；最近的 CEO 变动旨在解锁新的增长途径，这提供了一个及时的机会。
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
Transaction data
该公司拥有来自 9,000 多个商用车的交易数据，提供了丰富的燃油消耗模式表格视图，这对于对运营成本和效率进行建模至关重要。
IoT / sensor data
通过关键合作伙伴关系，该数据集包括专有的时间序列物联网数据，捕获了真实的车辆遥测和驾驶员行为信号，这对于训练高精度预测性维护算法至关重要。
business_records
持有者维护商用车的详细排放记录，提供了一个独特的特征层，用于优化可持续性目标和法规遵从性的维护计划。
Marketplace
Dataset details
Detailed schema & sample available on access request.
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Coverage
Scanned sources
Deliverable
Premium dataset report
Thefuelstore Mobility Telemetry — a Moderate mobility telemetry dataset (Time Series modality) in the mobility domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Automotive Predictive Maintenance market size was USD $22 billion in 2023, growing at a CAGR of 18.6% (source: Market.us).. Investment score 47.5/100 (confidence 0.49). Recommended action: Data Sharing Agreement.
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