Dataset opportunity
Okamac — 维护日志数据集机会
Okamac 持有的中等维护日志数据集，可用于预测性维护和异常检测。
Score
64.8
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在 2025 年的估值为 134 亿美元，预计在 2026 年至 2035 年期间的复合年增长率为 23.2%。
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
零售
Volume
中等
Freshness
定期
Rarity
高（专有）
Accessibility
受限
Legal
公司所有 — GDPR 敏感（PII 审查）
Buyer persona
工业人工智能与维护优化供应商
Okamac 持有的详细维护日志数据集源自其翻新电子产品业务，包含丰富的时间序列数据。这些`industrial_data`、`maintenance_logs`和`transaction_data`的集合捕获了技术事件、组件诊断和维修历史，使其成为训练预测性维护模型以预测消费电子产品硬件故障的绝佳来源。
全球预测性维护市场是一个重要且快速增长的领域，2025 年市场价值为134 亿美元，预计将以23.2% 的复合年增长率增长。[1] 尽管存在 PII 清理、集团层面治理和专有软件关联等访问复杂性，但这一有价值的数据集为获取该高增长市场的真实世界数据提供了难得的机会，值得 AI 买家进行谈判。
⚠ 尽职调查（有价值的数据，可协商访问）：数据包含前所有者的 PII，需要严格的清理验证；Recommerce Group 的子公司，需要集团层面的数据治理批准；技术日志与专有诊断软件相关 · 公司：Recommerce Group 的子公司。
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
这些证据证明 Okamac 持有一个稀有的专有数据集，详细记录了十多年来 Apple 设备的生命周期，从组件级别的健康状况到常见的故障点。这些时间序列和工业数据集合是工业人工智能和维护优化供应商构建下一代预测性维护模型的强大资产。在一个预计年增长率超过 23% 的市场中，该数据集提供了一个独特的机会，可以对真实世界的硬件退化、可修复性和经济价值进行算法训练，从而实现高度准确的故障预测和优化。
See dimension details ↓- Dataset Specificity90
主导的“维护日志”，零售行业，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 Freshness46
定期
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
AI 买家需求极高，这得益于预测性维护市场的快速扩张，该市场正以 23.2% 的复合年增长率增长。[1]
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，Recommerce Group 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 License62
所有权=公司所有，许可=GDPR 敏感
Whether the company can legally license the data out — based on ownership and licensing complexity. - Corporate Independence50
Recommerce Group 的子公司
Whether the holder can decide alone — an independent company scores higher than a subsidiary of a large group. - Data Orientation22
0 数据胃口信号（0 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 Audit100
✓ 良好目标 — Okamac 是欧洲领先的 Mac 翻新商，一家运营型企业，作为副产品生成有价值的维护、维修和组件故障数据，使其成为理想目标。
- Deep Qualification90
✓ 通过 — Okamac 是一个强大的数据持有者。其翻新 Mac 的核心业务产生了有价值的、连贯的维护和故障日志。数据所有权清晰，但前所有者的 PII 和其子公司身份的存在需要谨慎处理数据获取。
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
Maintenance logs
该数据集包含车间检查的详细组件级别健康报告，提供了训练高保真预测性维护算法所需的精细时间序列数据。
Transaction data
这些证据证实自 2009 年以来存在历史定价和需求数据，使 AI 模型能够对维护与更换的决策进行复杂的成本效益分析。
Industrial data
持有者拥有 15 年关于常见故障点和可修复性评分的汇总数据，为验证模型和评估系统性硬件风险提供了战略性的长期视角。
Marketplace
Dataset details
Detailed schema & sample available on access request.
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Coverage
Scanned sources
Deliverable
Premium dataset report
Okamac Maintenance Logs — a Moderate maintenance logs dataset (Time Series modality) in the retail domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Predictive Maintenance Market was valued at USD 13.4 billion in 2025 and is projected to grow at a CAGR of 23.2% between 2026 and 2035 (source: Market.us). [1]. Investment score 64.8/100 (confidence 0.49). Recommended action: Data Sharing Agreement.
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