Dataset opportunity
Return — Industrial Sensor Dataset Opportunity
Moderate industrial sensor dataset held by Return, usable for Predictive Maintenance and Anomaly Detection.
Score
45
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
Acquire
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 USD 14.2 billion in 2025, projected to grow at a CAGR of 27.9%.
Recent dated external facts that triggered this opportunity — auditable provenance.
- 📰press2026-09-01
Gigabatterij van 800 megawattuur voor Groningen: Vattenfall en Return tekenen historische deal
solarmagazine.nl ↗
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
Software that responds in real time / turn data into decisions
source ↗
Profile
Dataset profile
Type
Industrial Sensor Dataset
Modality
Time Series
Sector
industrial
Volume
Moderate
Freshness
Real-time
Rarity
High (proprietary)
Accessibility
Partial
Legal
Owned by the company — clean to license
Buyer persona
Industrial AI & maintenance-optimization vendors
Return holds a proprietary Industrial Sensor Dataset composed of high-frequency Time Series telemetry from its owned and operated large-scale battery energy storage systems (BESS). This operational `iot_data`, including `event_streams` and `industrial_data`, provides granular, real-world insights into asset performance and degradation, making it exceptionally well-suited for developing Predictive Maintenance AI models.
The global Predictive Maintenance market was valued at USD 14.2 billion in 2025 and is projected to grow at a remarkable CAGR of 27.9%. [2] While access to this data is negotiated due to its origin from valuable physical assets, its rarity and direct applicability for grid modeling and battery degradation AI offer significant external value, justifying the investment for buyers seeking a competitive edge in this rapidly expanding market. ⚠ Diligence (valuable data, access to negotiate): Data is generated by physical energy storage assets owned and operated by the company.; Includes high-frequency telemetry from large-scale battery systems (BESS).; Operational data is used for internal optimization but holds significant external value for grid modeling and battery degradation AI. · corporate: independent.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
This evidence confirms Return generates proprietary, first-party time-series data from its own large-scale energy storage solutions across Europe. For industrial AI vendors, this dataset is a rare opportunity to train and validate predictive maintenance models on real-world operational data from grid-balancing systems. In a market projected to grow at nearly 28% annually, this unique data provides a significant competitive edge by enabling more accurate failure prediction and asset optimization for the rapidly expanding clean energy sector.
See dimension details ↓- Dataset Specificity90
dominant 'iot_data', sector industrial, 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 Demand95
AI buyer demand is extremely high, driven by the rapid expansion of the Predictive Maintenance market, which is projected to grow at a 27.9% CAGR. [2]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility50
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 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 License92
ownership=company_owned, licensing=clean
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, 1 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 — The company's core business is providing 'storage-as-a-service' by building and operating large-scale battery systems, not generating data as a by-product of another operational business. Issues: Core business is providing an energy infrastructure service, not a non-data operational business.; The company's product is access to their platform and network of energy storage solutions. [3]; This is a service/infrastructure provider, which is explicitly excluded by the ICP.; The company is a platform that invests in and supports clean energy projects, which is a financial/investment model, not an operational one in the sense of the
- Deep Qualification90
✓ pass — Return is a data_holder. It builds, owns, and operates large-scale battery storage systems, selling storage-as-a-service. The operational sensor data is a by-product of its core business and is owned by the company, making it a strong target.
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
IoT / sensor data
This is proprietary IoT data generated from the intelligent management of energy flows within the company's owned and operated energy storage infrastructure, crucial for building asset performance optimization models.
Industrial data
This evidence points to high-value industrial sensor data from battery storage systems actively used for grid balancing, a key input for AI vendors improving grid stability and operational efficiency.
Event streams
This indicates the presence of real-time event streams generated by the software systems that connect and manage their distributed energy assets, essential for developing responsive control and anomaly detection algorithms.
Marketplace
Dataset details
Detailed schema & sample available on access request.
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Coverage
Scanned sources
Deliverable
Premium dataset report
Return Industrial Sensor — a Moderate industrial sensor dataset (Time Series modality) in the industrial domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Predictive Maintenance market was valued at USD 14.2 billion in 2025, projected to grow at a CAGR of 27.9% (source: Grand View Research). [2]. Investment score 45.0/100 (confidence 0.49). Recommended action: Acquire.
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