Dataset opportunity
Gobolt — Mobility Telemetry Dataset Opportunity
Moderate mobility telemetry dataset held by Gobolt, usable for Predictive Maintenance and Anomaly Detection.
Score
71.8
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
56%
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
Global Commercial Vehicle Telematics market = $24.3B in 2024, CAGR 12.9% (source: Precedence Research)
Recent dated external facts that triggered this opportunity — auditable provenance.
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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.
- 🧑💻Hiring a data role
Actively recruiting for technology and engineering roles to build supply chain networks
source ↗
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
Gobolt holds a comprehensive Mobility Telemetry Dataset structured as Time Series data, which integrates geo_data, industrial fleet operational data, IoT sensor data, and transaction records. This granular, multi-modal data is specifically engineered for developing advanced Predictive Maintenance models, enabling the anticipation of vehicle component failures and the optimization of complex maintenance schedules.
The global commercial vehicle telematics market represents a substantial and rapidly expanding opportunity, valued at USD 24.3 billion in 2024 with a projected CAGR of 12.9%. [1] While access to this dataset requires navigating certain complexities, such as the strict anonymization of PII in last-mile delivery logs and potential co-ownership of shipment data with merchants, its value is immense. The inclusion of sustainability and carbon emission data as a key secondary asset further enhances its strategic worth for AI buyers focused on efficiency and ESG objectives. ⚠ Diligence (valuable data, access to negotiate): Last-mile delivery data contains PII (names, addresses) requiring strict anonymization.; Fleet telemetry is proprietary, but shipment-specific data may involve merchant co-ownership.; Sustainability and carbon emission data is a key secondary asset. · corporate: independent.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
This evidence collectively proves Gobolt owns a proprietary, time-series dataset generated by its fleet of electric vehicles during last-mile delivery operations. This rare telemetry data is a direct input for sophisticated predictive maintenance models, a key demand for industrial AI vendors seeking to optimize fleet uptime and reduce operational costs. In a commercial vehicle telematics market projected to exceed $24 billion in 2024, this dataset offers a unique asset for building next-generation AI solutions for the rapidly growing EV logistics sector.
See dimension details ↓- Dataset Specificity100
dominant 'iot_data', sector mobility, 4 specific types
How sharply the data targets a specific, hard-to-substitute domain or task. Niche, well-defined data scores higher than generic. - Dataset Rarity94
proprietary domain data
How scarce and proprietary the data is. Unique domain data scores high; openly available data lowers it. - Dataset Volume58
4 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 Value94
fit for Predictive Maintenance
How useful the data is for the target AI use-case — its fit for model training or fine-tuning. - Buyer Demand85
AI buyer demand is high, driven by the significant growth in the commercial vehicle telematics market (CAGR of 12.9%), as this data is crucial for developing cost-saving predictive maintenance applications. [1]
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 Strength74
4 evidence types, 4 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 Orientation39
1 data-appetite signals (1 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 Audit92
✓ good target — Gobolt is a strong target as its core business is third-party logistics, generating a valuable byproduct of mobility and fulfillment data that is used for operational visibility but not sold as a standalone product. [1, 3] Issues: The company is tech-forward and already provides data visibility and APIs to its clients as part of its service; the data is not entirely 'dormant' but is not b; Initial search results can be confused with an Indian company of a similar name (gobolt.in)
- Deep Qualification90
⚠ needs review — GoBolt is a 3PL provider that holds valuable, proprietary mobility and logistics data as a byproduct of its core business; it does not sell this data. The dataset is plausible and coherent with its activities, but access is complex due to mixed data ownership with merchants and the presence of sensi [licensing restricted]
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
IoT / sensor data
This confirms the collection of proprietary IoT data from a fleet of electric delivery vehicles, a critical asset for training advanced predictive maintenance algorithms.
Geospatial data
This indicates the presence of tabular geospatial data used for route optimization, which provides crucial context for analyzing vehicle performance and wear patterns across different territories.
Transaction data
This points to tabular transaction data detailing fulfillment and last-mile delivery operations, which helps link vehicle telemetry to specific commercial activities and payloads.
Industrial data
This confirms the collection of time-series industrial data focused on the operational and environmental performance of their sustainable logistics fleet, particularly valuable for modeling EV-specific component failure.
Coverage
Scanned sources
Deliverable
Premium dataset report
Gobolt Mobility Telemetry — a Moderate mobility telemetry dataset (Time Series modality) in the mobility domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Commercial Vehicle Telematics market = $24.3B in 2024, CAGR 12.9% (source: Precedence Research). Investment score 71.8/100 (confidence 0.56). Recommended action: Data Sharing Agreement.