Dataset opportunity
Steelmar — 工业运营数据集机会
Steelmar 持有的中等工业运营数据集，可用于工业监控和预测。
Score
67.8
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
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
全球车队管理市场 = 2025 年为 377.1 亿美元，复合年增长率为 13.3%（来源：车队管理市场报告 2025-2030）
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.
- ✨Signal
专注于“综合物流”和“区域配送”意味着需要路线优化
source ↗
Profile
Dataset profile
Type
工业运营数据集
Modality
时间序列
Sector
移动
Volume
中等
Freshness
定期
Rarity
高（专有）
Accessibility
受限
Legal
公司所有 — 可授权 · PII/受监管
Buyer persona
工业人工智能集成商
Steelmar 持有一个有价值的工业运营数据集，该数据集由其移动和物流活动的时间序列数据组成。这包括来自车队移动的详细 `geo_data`、关于运营事件的 `industrial_data` 以及与交付相关的 `transaction_data`，其主要价值在于详细的路线效率指标和精确的交付时间戳，使其非常适合训练工业监控人工智能模型。
全球车队管理市场（本数据直接服务于该市场）在 2025 年的估值为377.1 亿美元，预计将以13.3% 的复合年增长率增长。[1] 虽然运营数据可能存储在旧的 TMS 或 WMS 中，遥测数据由第三方提供商管理，但这种复杂性凸显了数据的稀有性和高价值性质，为人工智能驱动的物流优化提供了显著的竞争优势。⚠ 尽职调查（有价值的数据，可协商访问）：运营数据可能存储在旧的 TMS 或 WMS 中；主要数据价值在于路线效率和交付时间戳；车队遥测可能通过第三方硬件提供商管理 · 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 Steelmar 持有一个专有的时间序列数据集，详细说明了其在西班牙的工业物流和仓储运营。该数据是训练工业监控人工智能的直接来源，提供了对供应链效率和资产利用率的详细见解。对于人工智能集成商而言，该数据集是构建和验证模型以占据不断增长的车队管理市场份额的稀有资产，该市场每年增长超过 13%，预计到 2025 年将接近 380 亿美元。
See dimension details ↓- Dataset Specificity90
主导的“industrial_data”，行业移动，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
适用于工业监控
How useful the data is for the target AI use-case — its fit for model training or fine-tuning. - Buyer Demand90
人工智能买家需求旺盛，这得益于车队管理市场的显著增长，该市场正以 13.3% 的复合年增长率扩张。[1]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility16
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
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 个命中
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=已拥有，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 — 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 Audit100
✓ 良好目标 — Steelmar 是一家西班牙物流和运输运营商，专门从事钢铁业务，作为其核心业务的副产品，生成有价值的可追溯性和运营数据，使其成为一个强大的潜在数据合作伙伴。[1, 2, 3] 问题：存在其他名称相同的非关联公司，特别是奥地利的工程/机器人公司（steelmar.at）和一家投资公司，其
- Deep Qualification70
⚠ 需要审查 — Steelmar 是一家物流服务提供商，而非数据销售商，其持有的运营数据与“工业运营”标签一致，但与“钢铁行业需求与关税”市场情报细分不符。[实体不持有细分市场的特征数据：目标的数据是运营数据（出货量、路线、时间戳），而不是定义细分市场的市场情报（定价、需求预测、关税影响分析）。[3, 12, 17]]
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
Geospatial data
这些表格证据确立了 Steelmar 作为西班牙物流和运输提供商的运营足迹，为建模区域分销网络提供了有价值的地理数据。
Transaction data
这些证据证实了数据集包含详细说明物流绩效的交易记录，包括交付窗口和记录的供应链瓶颈。
Industrial data
这些核心时间序列证据证明了数据集跟踪关键仓库绩效指标，如库存周转率和空间利用率，直接支持工业监控用例。
Marketplace
Dataset details
Detailed schema & sample available on access request.
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Coverage
Scanned sources
Deliverable
Premium dataset report
Steelmar Industrial Operations — a Moderate industrial operations dataset (Time Series modality) in the mobility domain. Primary AI use-case: Industrial Monitoring. Market signal: Global Fleet Management market = $37.71 billion in 2025, CAGR 13.3% (source: Fleet Management Market Report 2025-2030). Investment score 67.8/100 (confidence 0.49). Recommended action: Acquire.
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