Dataset opportunity
Onomondo — 移动遥测数据集机会
Onomondo 持有的海量移动遥测数据集，可用于预测性维护和异常检测。
Score
45
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
77%
Action
许可
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 142 亿美元，复合年增长率为 27.9%。
Recent dated external facts that triggered this opportunity — auditable provenance.
- 📰press2026-09-16
Onomondo launches its own SGP.32 eSIM IoT Remote Manager to prevent vendor lock-in undermining cellular IoT
iotbusinessnews.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.
- 🔌Public API
Onomondo 开发者 API，用于网络管理和实时洞察
source ↗
Profile
Dataset profile
Type
移动遥测数据集
Modality
时间序列
Sector
mobility
Volume
大型
Freshness
实时
Rarity
中等
Accessibility
开放/API
Legal
公司所有 — 许可清晰
Buyer persona
工业 AI 和维护优化供应商
Onomondo 提供了一个结构化为时间序列数据的移动遥测数据集，其中包含来自庞大连接资产网络的 `iot_data` 和 `geo_data`。这种技术网络遥测数据可通过 API 和事件流访问，直接适用于预测性维护用例，使 AI 买家能够通过分析全球车队的实时运行和连接模式来构建预测设备故障的模型。
预测性维护的全球市场在 2025 年的价值为142 亿美元，预计将以27.9% 的复合年增长率扩张。[1] 虽然访问需要从 Onomondo 的核心基础设施中提取数据，但该数据集的价值在于其专有的全球网络性能元数据，覆盖680 多个运营商，为构建高性能 AI 模型提供了稀有且宝贵的资源。该数据非 PII 的性质也便于更直接的许可流程。⚠ 尽职调查（有价值的数据，可协商的访问权限）：数据由技术网络遥测数据组成，而非 PII，便于许可；专有层体现在覆盖 680 多个运营商的全球网络性能元数据中；访问需要从其核心连接管理基础设施中提取。· 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 Onomondo 拥有来自移动资产的持续不断的全球物联网遥测流，以结构化的时间序列数据形式提供。对于工业 AI 供应商而言，该数据集是构建和验证预测性维护模型的关键燃料，这些模型可以预测设备故障。在一个预计到 2025 年将达到 142 亿美元的市场中，这些数据为创建高价值解决方案提供了直接途径，这些解决方案可以在车辆车队和工业机械发生故障之前精确定位运营风险。
See dimension details ↓- Dataset Specificity90
占主导地位的 'iot_data'，行业 mobility，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 Rarity58
专有领域数据（开放降低了稀有度）
How scarce and proprietary the data is. Unique domain data scores high; openly available data lowers it. - Dataset Volume100
12 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 买家需求旺盛，这得益于全球预测性维护市场的快速扩张，预计复合年增长率为 27.9%。[1]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility90
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 Feasibility84
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 Strength100
5 种证据类型，12 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=公司所有，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 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 Surplus92
盈余=高，1 个近期外部信号 — 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 Audit50
⚠ 审查 — Onomondo 的核心业务是销售物联网连接及其管理平台，而不是数据本身，这使其成为技术供应商且不适合。问题：核心业务是销售连接平台/服务，这是一种工具/SaaS 的形式，而不是销售休眠数据。[2, 3, 14]；该公司的价值主张是提供物联网数据传输的基础设施，而不是持有或拥有其网络中传输的数据。[11, ；Onomondo 明确向客户提供‘自由离开’，强调客户拥有 SIM 卡和相关密钥，这与 ICP 的目标背道而驰；该公司提供工具供客户监控和管理自己的数据流量，将其定位为平台/情报提供商，这是一个排除项
- Deep Qualification70
✓ 通过 — Onomondo 运营着一个全球物联网连接平台，使其成为有价值的非 PII 网络遥测数据的持有者。虽然这些数据是潜在的副产品且与预测性维护高度相关，但此遥测数据的所有权是混合的，并且在没有主服务协议的情况下许可权不明确，需要直接协商。
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
IoT / sensor data
这是核心的时间序列数据集，捕获来自全球物联网车队和移动资产的实时遥测数据，这是训练和验证预测性维护算法的基本依据。
API access
存在面向开发者的 API 和云连接器，证实了数据是为程序化访问而设计的，能够无缝集成到买家现有的 AI 工作流程中。
Downloads / exports
可下载的技术报告显示了分析设备性能以降低成本的历史记录，表明底层数据富含与资产寿命和维护优化相关的特征。
Geospatial data
该数据集通过 680 多个网络提供的全球位置数据得到丰富，提供了跨越从高速公路到偏远地区等多样化操作环境对资产性能进行建模所需的关键地理背景。
Event streams
数据包括专门用于识别网络问题的离散事件流，提供了直接标记的异常来源，非常适合训练故障检测模型。
Marketplace
Dataset details
Detailed schema & sample available on access request.
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Coverage
Scanned sources
Deliverable
Premium dataset report
Onomondo Mobility Telemetry — a Large mobility telemetry dataset (Time Series modality) in the mobility domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Predictive Maintenance market = $14.2B in 2025, CAGR 27.9% (source: Grand View Research). [1]. Investment score 45.0/100 (confidence 0.77). Recommended action: License.
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