Dataset opportunity
Getbyrd — 移动遥测数据集机会
Getbyrd 持有的中等规模移动遥测数据集，可用于预测性维护和异常检测。
Score
68.7
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
数据共享协议
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 2024 年的估值为 123 亿美元，预计复合年增长率为 29.7%（来源：Custom Market Insights）。[6]
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
移动遥测数据集
Modality
时间序列
Sector
移动
Volume
中等
Freshness
实时
Rarity
高（专有）
Accessibility
受限
Legal
混合所有权 — GDPR 敏感（PII 审查）
Buyer persona
工业人工智能与维护优化供应商
Getbyrd 持有一个宝贵的时间序列数据集，该数据集由其广泛的电子商务物流和履行运营中的 `event_streams`（事件流）、`iot_data`（物联网数据）和 `transaction_data`（交易数据）组成。这些丰富的遥测数据提供了仓库自动化和承运商活动的详细运营指标，使其直接适用于开发和训练高保真预测性维护模型，以预测设备故障并优化维护计划，从而最大限度地减少昂贵的运营停机时间。
在全球预测性维护市场背景下，该数据具有极高的相关性，该市场在 2024 年的估值为123 亿美元，预计将以 29.7% 的复合年增长率扩张。[6] 虽然由于 PII（个人身份信息）和与客户共享数据所有权，访问需要谨慎处理，但该数据集的独特价值在于其专有的跨境物流基准。这种稀缺性为人工智能买家在一个快速增长、高需求市场中提供了显著的竞争优势。⚠ 尽职调查（有价值的数据，可协商的访问权限）：包含 PII（客户送货地址和姓名），需要进行大量匿名化处理；数据所有权与电子商务客户共享库存细节；专有价值在于聚合的承运商绩效和跨境物流基准 · 公司：独立。
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
这些证据证明 Getbyrd 拥有专有的、大容量的数据集，可捕获其复杂的欧洲物流和移动网络的实时遥测数据。该数据记录了处理超过700 万年货运量的系统的性能，提供了训练复杂预测性维护模型所需的地面实况。对于快速扩张的123 亿美元工业人工智能市场的供应商而言，此时间序列数据提供了一个难得的机会，可以对真实世界的、多方参与的物流环境中的资产退化进行建模并预测故障。
See dimension details ↓- Dataset Specificity90
主导的 'iot_data'，行业为移动，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 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 Demand94
人工智能买家需求异常高，这得益于预测性维护市场以 29.7% 的复合年增长率（来源）快速增长，这使得这种专业的物流遥测数据成为优化运营的抢手资产。[6]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility0
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
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 License28
所有权=混合，许可=GDPR 敏感
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 Audit100
✓ 良好目标 — 该公司经营泛欧电子商务履行业务，产生有价值的物流和库存数据作为副产品，并且似乎不将其作为核心产品出售。问题：初始来源描述“移动遥测数据集”不准确；该公司的实际业务是电子商务履行；他们向客户提供物流分析仪表板，这需要与销售聚合数据作为产品区分开来。
- Deep Qualification90
✓ 通过 — 目标是一家技术驱动的第三方物流服务提供商，而不是数据销售商；它持有有价值的运营数据作为副产品，但所有权是混合的且受 GDPR 约束，这使得直接数据销售变得复杂。
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
press
- “<p>Les entreprises vont devoir changer profondément leur manière de planifier leurs approvisionnements par voie maritime, jusque-là structurés autour de cycles saisonniers rigides, pour adopter une approche plus dynamique capable de s’adapter en continu aux signaux du marché. C’est le discours que tient le logisticien Rhenus via sa vice-présidente en charge du fret maritime mondial, Renee […]</p> <p>L'article <a href="https://supplychainmagazine.fr/les-pics-seffacent-derriere-les-vagues-selon-rhenus/">Les pics s’effacent derrière les vagues, selon Rhenus</a> est apparu en premier sur <a ”
- “<figure><div><img src="https://imgproxy.divecdn.com/-th2eiBxA7ztQyfVhlmT1rXLm5azaqa0fpBHU8368jU/g:ce/rs:fill:1600:900:1/Z3M6Ly9kaXZlc2l0ZS1zdG9yYWdlL2RpdmVpbWFnZS9HZXR0eUltYWdlcy0yMjY1OTYzMjAzLmpwZw==.webp" /></div></figure><p>Spot rates are expected to climb for another four weeks and many vessels are full until at least July, per Xeneta.</p>”
- “<figure><div><img src="https://imgproxy.divecdn.com/2my75Ddo8ew-TYL_qC8s6V0VElTPwyePcKzMUp0T7D0/g:ce/rs:fill:1600:900:1/Z3M6Ly9kaXZlc2l0ZS1zdG9yYWdlL2RpdmVpbWFnZS9Lcm9nZXJfTWFya2V0cGxhY2VfZXh0ZXJpb3IuanBn.webp" /></div></figure><p>The grocery retailer is pushing negotiations and intentionally leveraging direct sourcing to optimize the cost of goods.</p>”
Transaction data
这些表格数据量化了运营规模，记录了超过7,000,000次年度产品运输，并提供了人工智能模型旨在优化的业务成果。
IoT / sensor data
这是来自 12 多个履行中心的传感器的时间序列数据，提供了仓库运营的实时视图，对于对资产利用率进行建模和识别性能瓶颈至关重要。
Event streams
这些事件驱动的时间序列数据跟踪 20 多个运输合作伙伴网络中的资产移动和服务水平，对于训练预测交付失败和性能下降的模型至关重要。
Marketplace
Dataset details
Geographic coverage
Europe
Time range
Real-time (rolling)
Update frequency
Real-time
Delivery
API
Formats
JSON, Time Series
License
One-time license for internal use in developing and training predictive maintenance models. Usage restrictions may apply to redistribution or resale.
Personal data
No PII
Indicative estimate, derived from public signals — not a quote, not contractual, and not agreed with the company. Is this your company? Correct it.
This proprietary, high-volume mobility telemetry dataset is highly valuable for predictive maintenance due to its real-time nature and direct application to logistics and fulfillment operations. The strong growth in the global predictive maintenance market, valued at $12.3 billion with a 29.7% CAGR, indicates significant demand for such granular operational data.
Detailed schema & sample available on access request.
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Coverage
Scanned sources
Deliverable
Premium dataset report
Getbyrd Mobility Telemetry — a Moderate mobility telemetry dataset (Time Series modality) in the mobility domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Predictive Maintenance Market was valued at $12.3 Billion in 2024, with a projected CAGR of 29.7% (source: Custom Market Insights). [6]. Investment score 68.7/100 (confidence 0.49). Recommended action: Data Sharing Agreement.
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