Dataset opportunity
Olympic Location — 维护日志数据集机会
由 Olympic Location 持有的中等规模维护日志数据集，可用于预测性维护和异常检测。
Score
73.5
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
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
全球预测性车队维护市场：2024 年为 52 亿美元，复合年增长率 (CAGR) 18.1%，到 2033 年将达到 251 亿美元。
Recent dated external facts that triggered this opportunity — auditable provenance.
- 📰press2026-06-04
3 logistics upgrades benefiting Wayfair
supplychaindive.com ↗ - 📰press2026-06-04
Amazon wants sellers to be more precise with handling times
supplychaindive.com ↗ - 📰press2026-06-04
Motul regroupe sa logistique avec FM Logistic à Nangis (77)
supplychainmagazine.fr ↗ - 📰press2026-06-04
Argan a livré 18.000 m² pour Nortene Home Depot à Louailles
supplychainmagazine.fr ↗ - 📰press2026-06-04
Pilgrim’s palettise en froid avec Promalyon à Hénin-Beaumont
supplychainmagazine.fr ↗
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.
Profile
Dataset profile
Type
维护日志数据集
Modality
时间序列
Sector
移动出行
Volume
中等
Freshness
实时
Rarity
高（专有）
Accessibility
受限
Legal
公司所有 — GDPR敏感（PII审查）
Buyer persona
工业人工智能和维护优化供应商
Olympic Location 拥有丰富的维护日志数据集，采用时间序列模式，涵盖其在出行领域的运营中产生的工业数据、物联网数据、维护日志和交易数据。这些细粒度数据对于开发和部署先进的预测性维护解决方案极具价值，能够预测设备故障并优化车辆维护计划。
车队管理中的预测性维护市场正在经历显著增长，全球预测性车队维护市场规模在2024年达到52亿美元，预计将以18.1%的复合年增长率增长，到2033年达到251亿美元。仅AI驱动的车队维护市场在2024年就价值42亿美元，以19.3%的强劲复合年增长率增长到2033年的117亿美元，这凸显了买家对AI解决方案的强烈需求。尽管面临个人数据GDPR合规性和与现有车队系统集成复杂性等挑战，但停机时间减少和运营优化带来的巨大成本节约使这些数据极具价值。⚠ 尽职调查（有价值的数据，可协商访问）：个人数据（客户详细信息、租赁历史、潜在位置数据）需要符合GDPR；与现有车队管理和预订系统集成可能很复杂。· 企业：独立。
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
Olympic Location 拥有一个庞大的专有数据集，来源于管理1200辆大型车队，包含详细的维护日志、远程信息处理数据和交易使用数据。这些丰富、时间序列的信息对于寻求开发先进预测性维护模型的工业AI和维护优化供应商来说是无价的。随着全球预测性车队维护市场预计到2033年达到251亿美元，该数据集提供了一个难得且及时的机会，可在快速扩张的领域中获得显著的竞争优势。
See dimension details ↓- Dataset Specificity100
主导的“维护日志”，出行领域，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 Volume58
4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
How current the data stays — real-time/streaming scores highest, periodic dumps lower. - Training Value94
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
全球汽车预测性维护市场，严重依赖AI和数据分析（包括维护日志），预计从2023年到2032年将以18.6%的复合年增长率（CAGR）增长，达到1000亿美元
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，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 License62
所有权=自有，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 Orientation22
0个数据需求信号（0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 Audit100
✓ 目标良好 — Olympic Location 是一家拥有真实运营业务的汽车租赁公司，该业务会产生有价值的专有数据（例如维护日志），而其核心业务并非出售数据或情报，因此是 d-nvest 的一个良好目标。
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
IoT / sensor data
此证据证实存在来自卫星定位系统的远程信息处理数据，为车队优化提供关于车辆移动和操作模式的关键洞察。
Transaction data
这指的是租赁交易记录，详细说明车辆类型、使用时长和客户预订模式，这对于需求预测和资产利用至关重要。
Industrial data
这证实了持有者在多个机构运营着一支庞大的1200辆车队，表明存在大量运营数据可用于规模化分析。
Maintenance logs
这直接表明了丰富的车辆维护历史来源，包括定期保养和续订的详细信息，这是预测性维护建模的基础。
Marketplace
Dataset details
Geographic coverage
Global
Time range
Real-time (implies ongoing, with historical data likely available)
Update frequency
Real-time
Delivery
API
Formats
JSON, CSV
License
One-time license for internal use in developing and deploying predictive maintenance models. Restrictions on redistribution and resale 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 Olympic Location operational data, combined with moderate volume and strong demand from the rapidly growing predictive fleet maintenance market, justifies a premium valuation. The time-series nature and inclusion of industrial, IoT, maintenance, and transaction data are key value drivers.
Detailed schema & sample available on access request.
Want this data?
Request access — we broker a secure deal room. Operator-reviewed, no automatic sharing.
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
Olympic Location Maintenance Logs — a Moderate maintenance logs dataset (Time Series modality) in the mobility domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Predictive Fleet Maintenance market = USD 5.2 billion in 2024, CAGR 18.1% to USD 25.1 billion by 2033.. Investment score 73.5/100 (confidence 0.56). Recommended action: Data Sharing Agreement.
From the marketplace
Explore live data opportunities
Christiani — 工业运营数据集机会
View opportunity →工业Glacierenergy — 工业运营数据集机会
View opportunity →出行Barringtonfreight — 法规记录数据集机会
View opportunity →