Dataset opportunity
Fasterholt — 工业传感器数据集机会
Fasterholt 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
49%
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 = 2025 年为 134 亿美元，复合年增长率为 23.2%。
Recent dated external facts that triggered this opportunity — auditable provenance.
- 📰press2026-08-28
Precision Irrigation: Applying Water More Precisely Under Tightening Limits in Germany
globalagtechinitiative.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.
- 🤝Data partnership
与农业技术提供商合作进行机器优化
source ↗
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
公司所有 — 许可权待澄清
Buyer persona
工业人工智能与维护优化供应商
Fasterholt 拥有一个宝贵的工业传感器数据集，该数据集由其物理灌溉机车队生成的时间序列数据组成。这些数据，包括 `industrial_data`、`iot_data` 和 `geo_data`，为构建和训练强大的预测性维护模型提供了全面的基础，从而能够在设备发生故障之前进行预测。
全球预测性维护市场在 2025 年的估值为134 亿美元，预计将以 23.2% 的复合年增长率增长，显示出巨大的商业价值。[1] 尽管技术访问需要与 Nortoft 控制系统对接，并应对与农民之间潜在的数据所有权复杂性，但这些真实运营数据的稀缺性和丰富性使其成为这个快速扩张市场中 AI 开发人员高度追捧的资产。[1] ⚠ 尽职调查（有价值的数据，可协商的访问权限）：数据主要由田间的物理灌溉机生成；所有权可能与农民共享，但遥测数据很可能由 Fasterholt 捕获；技术访问需要与 Nortoft 控制系统对接 · 公司：独立。
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
公开证据证实 Fasterholt 拥有一项专有数据集，该数据集结合了其工业设备的历史机器性能与实时传感器和 GPS 数据。这种时间序列和地理空间数据的独特融合是训练复杂的预测性维护算法的关键资产。对于 AI 供应商而言，此数据集为构建和验证可提高运营效率并防止代价高昂的设备故障的模型提供了直接途径，目标是到 2025 年达到 134 亿美元的全球市场。
See dimension details ↓- Dataset Specificity90
主导的 'iot_data'，工业领域，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 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 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 买家需求异常高，这得益于对专业工业时间序列数据的迫切需求，以利用复合年增长率为 23.2% 的预测性维护市场。[1]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility28
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 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 License70
所有权=公司所有，许可=权利不明确
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 个数据胃口信号（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，1 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 Audit75
⚠ 审查 — Fasterholt 是一家制造灌溉机械并提供相关车队管理软件的中小企业，因此不适合，因为它已经销售其产品产生的智能。问题：公司核心业务是制造硬件（灌溉机）。[2, 11]；公司已销售用于车队管理、监控和远程控制其机器的智能/软件产品（'FasterRain' 应用程序和云解决方案）；'FasterRain' 软件专为'更智能的灌溉管理'设计，包括区域灌溉和优化，这符合销售智能的定义；公司已走上将其机器的数据/智能货币化的道路，这与 ICP 对'休眠数据'的要求相冲突。[7, 16]
- Deep Qualification70
✓ 通过 — Fasterholt 制造灌溉机械并提供用于车队管理的应用程序/云解决方案，生成有价值的工业传感器数据集。但是，数据所有权可能与农民客户共享，并且没有法律文件可以澄清转售权。
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
IoT / sensor data
该数据集包括来自机器传感器（如压力和速度）的实时时间序列数据，这是构建异常检测模型的基础。
Geospatial data
持有者收集地理空间数据，跟踪机器位置和田间定位，提供关键的环境背景，以提高维护预测的准确性。
Industrial data
该数据集包含长期的历史性能记录，提供了关于机器耐用性和运营效率的宝贵地面真实数据，这些数据对于训练和验证预测模型至关重要。
Marketplace
Dataset details
Detailed schema & sample available on access request.
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Coverage
Scanned sources
Deliverable
Premium dataset report
Fasterholt 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 = $13.4 billion in 2025, CAGR 23.2% (source: Market.us). [1]. Investment score 48.0/100 (confidence 0.49). Recommended action: Acquire.
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