Dataset opportunity
Getbyrd — Mobility Telemetry Dataset Opportunity
Moderate mobility telemetry dataset held by Getbyrd, usable for Predictive Maintenance and Anomaly Detection.
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
Data Sharing Agreement
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)
Global Predictive Maintenance Market was valued at $12.3 Billion in 2024, with a projected CAGR of 29.7%.
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
Mobility Telemetry Dataset
Modality
Time Series
Sector
mobility
Volume
Moderate
Freshness
Real-time
Rarity
High (proprietary)
Accessibility
Restricted
Legal
Mixed ownership — GDPR-sensitive (PII review)
Buyer persona
Industrial AI & maintenance-optimization vendors
Getbyrd holds a valuable Time Series dataset composed of `event_streams`, `iot_data`, and `transaction_data` from its extensive e-commerce logistics and fulfillment operations. This rich telemetry data provides detailed operational metrics on warehouse automation and carrier activities, making it directly suited for developing and training high-fidelity Predictive Maintenance models to forecast equipment failure and optimize maintenance schedules, thereby minimizing costly operational downtime.
This data is exceptionally relevant in the context of the global Predictive Maintenance market, which was valued at $12.3 Billion in 2024 and is projected to expand at a 29.7% CAGR. [6] While access requires careful handling due to PII and shared data ownership with clients, the dataset's unique value lies in its proprietary cross-border logistics benchmarks. This rarity offers a significant competitive advantage to AI buyers in a rapidly growing, high-demand market. ⚠ Diligence (valuable data, access to negotiate): Contains PII (customer shipping addresses and names) requiring heavy anonymization; Data ownership is shared with e-commerce clients for inventory specifics; Proprietary value lies in the aggregated carrier performance and cross-border logistics benchmarks · corporate: independent.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
This evidence proves Getbyrd owns a proprietary, high-volume dataset capturing real-time telemetry from its complex European logistics and mobility network. The data documents the performance of a system handling over 7 million annual shipments, providing the ground truth needed to train sophisticated predictive maintenance models. For vendors in the rapidly expanding $12.3 billion industrial AI market, this time-series data offers a rare opportunity to model asset degradation and predict failures across a real-world, multi-partner logistics environment.
See dimension details ↓- Dataset Specificity90
dominant 'iot_data', sector mobility, 3 specific types
How sharply the data targets a specific, hard-to-substitute domain or task. Niche, well-defined data scores higher than generic. - Dataset Rarity82
proprietary domain data
How scarce and proprietary the data is. Unique domain data scores high; openly available data lowers it. - Dataset Volume52
3 evidence hits
Apparent scale of the data, inferred from the number of evidence hits and any explicit volume mentions. - Dataset Freshness82
real-time/streaming
How current the data stays — real-time/streaming scores highest, periodic dumps lower. - Training Value84
fit for Predictive Maintenance
How useful the data is for the target AI use-case — its fit for model training or fine-tuning. - Buyer Demand94
AI buyer demand is exceptionally high, driven by the Predictive Maintenance market's rapid growth at a sourced 29.7% CAGR, making this specialized logistics telemetry data a highly sought-after asset for optimizing operations. [6]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility0
PII/regulated
How legally easy the data is to obtain and use — open/API access scores high; PII or regulated data scores low. - Acquisition Feasibility0
medium difficulty, independent
How realistic it is to actually obtain the data, given access difficulty and the holder's corporate structure. - Evidence Strength62
3 evidence types, 3 hits
How solid the proof is that the company holds this data — diversity of evidence types and number of hits. - Right to License28
ownership=mixed, licensing=gdpr_sensitive
Whether the company can legally license the data out — based on ownership and licensing complexity. - Corporate Independence90
independent
Whether the holder can decide alone — an independent company scores higher than a subsidiary of a large group. - Data Orientation56
2 data-appetite signals (2 types)
How actively the company invests in data, measured by its data-appetite signals (hires, products, APIs…). - Dormant Data Surplus92
surplus=high, 5 recent external signals — proprietary data beyond what's already monetised
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
✓ good target — The company operates a pan-European e-commerce fulfillment business, generating valuable logistics and inventory data as a by-product, and does not appear to sell this data as its core product. Issues: The initial sourcing description 'Mobility Telemetry Dataset' is inaccurate; the company's actual business is e-commerce fulfillment.; They offer a logistics analytics dashboard to their clients, which needs to be distinguished from selling aggregated data as a product.
- Deep Qualification90
✓ pass — The target is a tech-enabled 3PL services provider, not a data seller; it holds valuable operational data as a byproduct, but ownership is mixed and subject to GDPR, making direct data sales complex.
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
This tabular data quantifies the operational scale, documenting over 7,000,000 annual product shipments and providing the high-level business outcomes that AI models aim to optimize.
IoT / sensor data
This is time-series data from sensors across 12+ fulfillment centers, offering a real-time view of warehouse operations essential for modeling asset utilization and identifying performance bottlenecks.
Event streams
This event-driven time-series data tracks asset movement and service levels across a network of 20+ shipping partners, crucial for training models that predict delivery failures and performance degradation.
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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