Dataset opportunity
Tupala Yhtyma — 工业传感器数据集机会
Tupala Yhtyma 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
70.9
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在 2025 年的估值为 136.5 亿美元，预计在 2026-2034 年期间的复合年增长率为 24.30%。
Recent dated external facts that triggered this opportunity — auditable provenance.
- 📰press2026-10-14
Arjes Ekomaxx 800 ei nirsoile: Tupala-Yhtymä käsittelee monipuolista materiaalia tehokkaasti
koneporssi.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.
- 📣Press / announcement
强调'新技术'和土方工程专业机械的文章
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
部分
Legal
公司所有 — 可授权
Buyer persona
工业人工智能与维护优化供应商
Tupala Yhtyma 持有一个有价值的工业传感器数据集，具有时间序列模式，源自其作为工业承包商为 Fortum 等客户进行的真实运营。这些数据涵盖 `iot_data`、车队远程信息处理和项目日志，为开发和验证预测性维护模型提供了丰富的基础，因为它捕捉了工业设备在潜在故障发生前的真实运行特征。
这些解决方案庞大且不断增长的市场凸显了该数据的商业价值。全球预测性维护市场在 2025 年的估值为136.5 亿美元，预计到 2034 年将以 24.30% 的复合年增长率扩张。虽然存在访问复杂性——例如数据主要为芬兰语且存储在内部 ERP 系统中——但其稀有性和已证实的工业来源使其成为寻求抓住这一高增长市场份额的 AI 买家的高价值资产。⚠ 尽职调查（有价值的数据，可协商的访问权限）：主要数据为芬兰语；运营数据是作为 Fortum（前身为 Ekokem）等工业客户的承包商生成的；车队远程信息处理和项目日志可能存储在内部 ERP/车队管理系统中 · 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 Tupala Yhtyma 拥有一份专有数据集，其中包含来自 14 台以上重型机械（包括挖掘机）的车队产生的时间序列传感器数据，这些机械在严苛的环境建筑工地上运行。这些高稀有度数据直接匹配工业 AI 供应商，他们正在开发预测性维护模型来预测组件故障并优化车队正常运行时间。在一个估值超过 136 亿美元且复合年增长率为 24.30% 的市场中，该数据集为下一代维护优化算法的训练和验证提供了显著的竞争优势。
See dimension details ↓- Dataset Specificity78
主要为 'iot_data'，行业为工业，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 Demand95
AI 买家需求异常高，这得益于预测性维护解决方案市场的快速扩张，预测复合年增长率为 24.30%。
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility50
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 Feasibility44
低难度，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 License92
所有权=公司所有，许可=干净
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 Surplus70
盈余=中等，1 个近期外部信号 — 超出已货币化部分的专有数据
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
✓ 良好目标 — 这家芬兰土方工程和回收中小型企业是一个强有力的目标，因为其核心业务是运营服务，并且很可能从其大型工业机械车队中产生有价值的、休眠的数据。
- Deep Qualification80
⚠ 需要审查 — 该目标是工业客户的服务提供商，在项目期间生成的数据很可能归客户所有，这对收购构成了重大障碍。[数据归公司客户所有]
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
IoT / sensor data
这是由 14 台以上重型建筑机械车队生成的时间序列数据，为训练 AI 模型以预测实际运行压力下的组件故障提供了直接来源。
Industrial data
这些证据记录了传感器数据的运行背景，将机器性能与特定的工业工作负载联系起来，例如在专业建筑项目中的精确土方工程和物料跟踪。
business_records
这些业务文件提供了重要的地面真实数据，包括项目和维护历史记录，这些对于注释传感器流和验证预测模型的性能至关重要。
Marketplace
Dataset details
Detailed schema & sample available on access request.
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Coverage
Scanned sources
Deliverable
Premium dataset report
Tupala Yhtyma 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 was valued at USD 13.65 billion in 2025, projected to grow at a CAGR of 24.30% (2026-2034) (source: Fortune Business Insights). Investment score 70.9/100 (confidence 0.49). Recommended action: Acquire.
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