Dataset opportunity
Everactive — 工业传感器数据集机会
由 Everactive 持有的中等规模工业传感器数据集，可用于预测性维护和异常检测。
Score
48
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
58%
Action
收购
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规模在 2024 年价值约 109.3 亿美元，预计复合年增长率为 25.10%（来源：MarkNtel Advisors）。 [11]
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
工业传感器数据集
Modality
时间序列
Sector
工业
Volume
中等
Freshness
实时
Rarity
高（专有）
Accessibility
受限
Legal
混合所有权 — 清晰可许可 · PII/受监管
Buyer persona
工业人工智能与维护优化供应商
Everactive 拥有大量工业传感器数据集，主要由其无电池物联网设备组成的舰队产生的高频时间序列数据组成。其开发者门户、原始物联网数据流和维护日志的证据证实了设备性能方面丰富、细粒度信息的可用性，这些信息直接适用于训练复杂的预测性维护模型以预测设备故障。
商业价值巨大，触及了预测性维护的全球市场，该市场在 2024 年的估值为约 109.3 亿美元，预计将以 25.10% 的复合年增长率增长。[11] 虽然数据所有权可能与工业客户共享，但核心专有价值在于聚合的、匿名的传感器流。这使得该数据集成为 AI 买家稀有且有价值的资产，证明了利用它所需的协商访问是合理的。⚠ 尽职调查（有价值的数据，可协商访问）：数据所有权可能与物联网即服务合同下的工业客户共享；专有价值在于其舰队中聚合的、匿名的、高频传感器流 · 公司：独立。
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
这些证据共同证明 Everactive 拥有连续、大容量的专有工业传感器数据流，包括关键的已标记故障事件。该数据集直接服务于快速增长的预测性维护市场，该市场价值近 110 亿美元，年增长率超过 25%。对于工业 AI 供应商来说，这是一个难得的机会，可以获取训练和验证下一代预测性维护模型所需的地面实况数据，通过在设备故障发生前进行预测，提供显著的竞争优势。
See dimension details ↓- Dataset Specificity78
主导的“物联网数据”，工业领域，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 Volume64
5 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 Demand95
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 25.10% 的复合年增长率增长。[11]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility28
开放/API 访问
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 Strength77
4 种证据类型，5 次命中
How solid the proof is that the company holds this data — diversity of evidence types and number of hits. - Right to License58
所有权=混合，许可=清晰
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 — 超出已货币化数据的专有数据
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 — Everactive 的核心业务是销售端到端解决方案，包括硬件、网络和基于云的分析服务，使其成为智能的销售者，而不是休眠数据的持有者。问题：该公司的商业模式明确为“即服务”，为客户提供实时洞察、分析和警报的仪表板。[8, 9, 17]；核心产品是其无电池传感器产生的连续、基于云的分析，这是客户付费的内容。[7, 9, 15]；这是一家销售智能/AI 软件的公司，这是“良好目标”的明确排除标准。[16, 17]；该公司从销售芯片转向提供全栈解决方案，因为市场尚未准备好在硬件上构建自己的分析。[9]
- Deep Qualification90
✓ 通过 — Everactive 销售端到端的预测性维护解决方案，而非休眠数据。其无电池传感器产生的数据是其服务产品不可或缺的一部分，包括分析和警报。数据所有权很可能是混合的，客户拥有其原始数据，Everactive 保留聚合数据的权利，但具体条款不公开。
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
press
- “<p>Container import bookings are riding a prolonged wave as new tariffs are reviewed. How much of this is tariff induced and what does it mean for domestic transportation markets?</p> <p>The post <a href="https://www.freightwaves.com/news/how-much-is-trade-policy-influencing-imports">How much is trade policy influencing imports?</a> appeared first on <a href="https://www.freightwaves.com">FreightWaves</a>.</p>”
- “<figure><div><img src="https://imgproxy.divecdn.com/1VVqthKCGnSSoPYOWwrPHivNSEyrkLGpXlF5AKFYUs0/g:ce/rs:fill:1600:900:1/Z3M6Ly9kaXZlc2l0ZS1zdG9yYWdlL2RpdmVpbWFnZS9NSUNST04tTlktaW1hZ2UtMThfSXpFa1N1bC5qcGc=.webp" /></div></figure><p>The DRAM maker is also spending up to $3 billion toward the semiconductor supply chain, including $500 million to boost GlobalWafer’s manufacturing and R&D capabilities at its fab in Texas.</p>”
- “<figure><div><img src="https://imgproxy.divecdn.com/plspUUFdednGmYVMCxmcnAQM4_7HKpxWZRFbQJNr2ok/g:ce/rs:fill:1600:900:1/Z3M6Ly9kaXZlc2l0ZS1zdG9yYWdlL2RpdmVpbWFnZS9HZXR0eUltYWdlcy0yMTUzNjk1OTAyLmpwZw==.webp" /></div></figure><p>Companies are urging the Trump administration to protect additional imports from proposed Section 301 levies due to a lack of domestic availability.</p>”
IoT / sensor data
技术产品描述证实了来自连续、无电池传感器的数据生成，其规模为每天 2000 万条记录，为强大的模型训练提供了不间断的流，这是高频时间序列数据。
Developer portal
来自公司开发者门户的文档明确指出，他们旨在生成超大规模物联网数据，专门用于机器学习模型，证实了数据成熟度和 AI 就绪度很高。
User-generated content
面向公众的内容证实了公司的战略愿景是捕获和分析来自物理世界资产的数据，验证了他们对预测性维护供应商所针对的工业物联网领域的关注。
Maintenance logs
运营服务日志表明，该数据集包含已标记数据，将传感器读数直接与设备故障和低效率联系起来，这是监督学习必不可少的地面实况。
Marketplace
Dataset details
Detailed schema & sample available on access request.
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Coverage
Scanned sources
Deliverable
Premium dataset report
Everactive Industrial Sensor — a Moderate industrial sensor dataset (Time Series modality) in the industrial domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Predictive Maintenance Market size was valued at around $10.93 billion in 2024, with a projected CAGR of 25.10% (source: MarkNtel Advisors). [11]. Investment score 48.0/100 (confidence 0.58). Recommended action: Acquire.
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