Dataset opportunity
Withthegrid — Industrial Sensor Dataset Opportunity
Large industrial sensor dataset held by Withthegrid, usable for Predictive Maintenance and Anomaly Detection.
Score
72.2
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
92%
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 size was valued at USD 12.94 Billion in 2024 and is poised to grow at a CAGR of 26.9% from 2026–2033. (source: Polaris Market Research)
Recent dated external facts that triggered this opportunity — auditable provenance.
- 📰press2026-06-15
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utilitydive.com ↗ - 📰press2026-06-15
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utilitydive.com ↗ - 📰press2026-06-12
Meta expands US solar portfolio, inks PPA with Zelestra
utilitydive.com ↗ - 📰press2026-06-12
Judge overturns DOE’s cancellation of $82.1M in clean energy grants
utilitydive.com ↗ - 📰press2026-06-12
Au Royaume-Uni, le dirigeant d’EDF doute du besoin de nouvelles éoliennes
greenunivers.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.
Profile
Dataset profile
Type
Industrial Sensor Dataset
Modality
Time Series
Sector
industrial
Volume
Large
Freshness
Real-time
Rarity
Medium
Accessibility
Partial
Legal
Largely customer-owned — licensing rights to clarify
Buyer persona
Industrial AI & maintenance-optimization vendors
Withthegrid holds a valuable Industrial Sensor Dataset consisting of Time Series data collected from the assets of utility customers and Independent Power Producers (IPPs). This real-world operational data, including metrics like vibration, temperature, and pressure from IoT devices, is directly applicable for training and validating Predictive Maintenance models, enabling the anticipation of equipment failures before they occur.
The global Predictive Maintenance market was valued at approximately $12.94 billion in 2024 and is projected to grow at a remarkable CAGR of 26.9% through 2033. Despite access complexities—data is owned by clients and long-term history requires specific agreements—the rarity and granularity of this industrial_data make it a high-value asset. For AI developers, acquiring such authentic operational data is a primary challenge, justifying negotiated access to gain a competitive edge in a rapidly expanding market. ⚠ Diligence (valuable data, access to negotiate): Data is primarily owned by utility customers and IPPs.; Company policy states cloud services 'forget' raw messages after 2 weeks for Teleport gateway users.; Access to long-term historical datasets likely requires specific agreements with their Asset Monitoring Platform (AMP) clients. · corporate: independent.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
This evidence confirms Withthegrid possesses a significant collection of real-world, time-series sensor data from diverse industrial assets including grids, pipelines, and renewable energy sources. This is a critical resource for AI vendors building predictive maintenance solutions to capitalize on a market growing at over 26% annually. The data directly supports the development of sophisticated models for anomaly detection and asset optimization, offering a competitive edge in monitoring high-value equipment like wind turbines, transformers, and batteries.
See dimension details ↓- Dataset Specificity78
dominant 'iot_data', sector industrial, 2 specific types
How sharply the data targets a specific, hard-to-substitute domain or task. Niche, well-defined data scores higher than generic. - Dataset Rarity46
proprietary domain data (open lowers rarity)
How scarce and proprietary the data is. Unique domain data scores high; openly available data lowers it. - Dataset Volume100
22 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 Value74
fit for Predictive Maintenance
How useful the data is for the target AI use-case — its fit for model training or fine-tuning. - Buyer Demand95
The global predictive maintenance market, which fundamentally relies on industrial sensor data, is projected to grow from USD 14.2 billion in 2025 to USD 98.1 billion by 2033, at an extremely high CAGR of 27.9%, indicating massive and growi
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility48
open/API access
How legally easy the data is to obtain and use — open/API access scores high; PII or regulated data scores low. - Acquisition Feasibility84
medium difficulty, independent
How realistic it is to actually obtain the data, given access difficulty and the holder's corporate structure. - Evidence Strength100
5 evidence types, 22 hits
How solid the proof is that the company holds this data — diversity of evidence types and number of hits. - Right to License8
ownership=customer_owned, licensing=rights_unclear
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 Surplus70
surplus=medium, 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 Audit50
⚠ review — Withthegrid sells a hardware and software platform to asset owners and grid operators to monitor and control their energy assets; it does not own the assets or the resulting data itself. Issues: Core business is selling a technology product (SaaS/PaaS/Hardware), not operating a business that generates data as a byproduct. [2, 3, 6]; The company provides tools for others to manage their assets; the proprietary data belongs to their customers (asset owners, grid operators), not to Witht
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
API access
A mature, standardized API provides real-time data access from over 450 industrial asset types, enabling buyers to power continuous, live predictive models rather than relying on static files.
Knowledge base / docs
Extensive technical documentation and release notes accompany the data, providing the essential context and metadata that significantly accelerate data science workflows.
IoT / sensor data
The holder actively collects time-series data from a wide range of connected IoT devices, including PV inverters, batteries, and wind turbines, providing a rich source for energy-sector AI applications.
Public datasets
The company's platform is engineered to export large-scale datasets without row limits, signaling the capacity to deliver the substantial data volumes required for robust AI model training.
Industrial data
The dataset explicitly contains sensor readings from core industrial assets like grids and pipelines, and is already used for anomaly detection, directly validating its high value for predictive maintenance use cases.
Coverage
Scanned sources
Deliverable
Premium dataset report
Withthegrid Industrial Sensor — a Large industrial sensor dataset (Time Series modality) in the industrial domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Predictive Maintenance market size was valued at USD 12.94 Billion in 2024 and is poised to grow at a CAGR of 26.9% from 2026–2033. (source: Polaris Market Research). Investment score 72.2/100 (confidence 0.92). Recommended action: Data Sharing Agreement.