Dataset opportunity
Voltalis — Sensor Telemetry Dataset Opportunity
Moderate sensor telemetry dataset held by Voltalis, usable for Predictive Maintenance and Anomaly Detection.
Score
72.4
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 Predictive Maintenance market = $14.29 billion in 2025, CAGR 27.9% (2026-2033)
Recent dated external facts that triggered this opportunity — auditable provenance.
- 📰press2026-06-04
Colorado co-op delivers 100% renewables in March, a first
utilitydive.com ↗ - 📰press2026-06-04
Les petites toitures solaires deviennent un produit comme les autres
greenunivers.com ↗ - 📰press2026-06-04
MISO’s resource outlook improves as forecast generation additions outpace demand growth
utilitydive.com ↗ - 📰press2026-06-03
DTE Energy partners with LG to deploy 6 GWh of battery storage
utilitydive.com ↗ - 📰press2026-06-03
Google to fund 100-MW virtual power plant in PJM in ‘first-of-its-kind’ deal
utilitydive.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.
Profile
Dataset profile
Type
Sensor Telemetry Dataset
Modality
Time Series
Sector
other
Volume
Moderate
Freshness
Real-time
Rarity
High (proprietary)
Accessibility
Restricted
Legal
Mixed ownership — GDPR-sensitive (PII review)
Buyer persona
Industrial AI & maintenance-optimization vendors
Voltalis holds a rich Sensor Telemetry Dataset of Time Series data, encompassing event streams, geo-data, industrial data, and IoT data. This high-frequency information, collected from devices installed on customer premises, is uniquely suited for Predictive Maintenance applications by enabling the identification of subtle patterns and anomalies critical for forecasting potential equipment failures and optimizing maintenance schedules.
The global predictive maintenance market is substantial, estimated at $14.29 billion in 2025 and projected to reach $98.16 billion by 2033, growing at a 27.9% CAGR. This significant market demand is driven by the potential to drastically reduce costly unplanned downtime, with median costs reaching approximately $125,000 per hour in some industries. Despite the complexity of access due to personal information requiring GDPR compliance and the data being a byproduct of Voltalis's services rather than a direct sale, the rarity and operational relevance of this real-world, granular data make it exceptionally valuable for advanced AI buyer use-cases. ⚠ Diligence (valuable data, access to negotiate): Data contains personal information, requiring strict GDPR compliance.; Data is collected from customer premises via installed devices.; Voltalis is compensated by electricity networks for its services, not directly by selling raw data. · corporate: independent.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
Voltalis offers an exceptionally large-scale, proprietary dataset, comprising over 200 billion time-series data points from more than 1.5 million connected devices and 10 billion real-time power reduction orders. This unique sensor telemetry and industrial consumption data is highly valuable for Industrial AI and maintenance-optimization vendors, directly addressing the rapidly expanding $14.29 billion Global Predictive Maintenance market. Its depth and real-time operational insights into diverse equipment make it a critical asset for developing advanced AI models and optimizing asset performance.
See dimension details ↓- Dataset Specificity86
dominant 'iot_data', sector other, 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 Demand90
The AI-driven predictive maintenance market, which relies heavily on sensor telemetry data, is projected to grow at a CAGR of 39.5% from USD 1.77 billion in 2025 to USD 19.27 billion by 2032, indicating very high and rapidly increasing buye
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility20
restricted/unknown
How legally easy the data is to obtain and use — open/API access scores high; PII or regulated data scores low. - Acquisition Feasibility30
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 Orientation22
0 data-appetite signals (0 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 — Voltalis is a strong target as they operate a real business (energy demand response) that generates a vast amount of proprietary sensor telemetry data as a by-product, which they do not currently commercialize.
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
IoT / sensor data
This represents a massive collection of IoT sensor telemetry from over 1.5 million connected devices, accumulating more than 200 billion data points and 10 billion power reduction orders, providing unparalleled scale for training predictive maintenance models on diverse equipment performance and operational responses.
Industrial data
This evidence details granular energy consumption data, broken down by specific uses like heating and hot water, available in both monetary and energy units, alongside historical and forecasted consumption, offering critical insights into appliance-level usage patterns essential for identifying efficiency anomalies and predicting equipment failures.
Geospatial data
This tabular data reveals significant geographical variations in energy costs and consumption patterns across different regions, providing valuable contextual information for understanding regional factors influencing equipment performance and maintenance needs.
Event streams
This real-time event stream captures the active coordination and aggregation of electricity consumption reductions across millions of flexible equipment types, including heaters, AC units, and EV chargers, offering unique insights into how diverse industrial assets respond to dynamic control signals, crucial for developing proactive maintenance strategies.
Coverage
Scanned sources
Deliverable
Premium dataset report
Voltalis Sensor Telemetry — a Moderate sensor telemetry dataset (Time Series modality) in the other domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Predictive Maintenance market = $14.29 billion in 2025, CAGR 27.9% (2026-2033). Investment score 72.4/100 (confidence 0.56). Recommended action: Data Sharing Agreement.