Dataset opportunity
Alcemi — Industrial Sensor Dataset Opportunity
Moderate industrial sensor dataset held by Alcemi, usable for Predictive Maintenance and Anomaly Detection.
Score
73.2
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
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 = $13.65 billion in 2025, CAGR 24.30%.
Recent dated external facts that triggered this opportunity — auditable provenance.
- 📰press2026-08-12
CIP déploie la plus puissante batterie d’Europe, au Royaume-Uni
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
Moderate
Freshness
Real-time
Rarity
High (proprietary)
Accessibility
Restricted
Legal
Owned by the company — licensing rights to clarify
Buyer persona
Industrial AI & maintenance-optimization vendors
Alcemi possesses a valuable Industrial Sensor Dataset composed of Time Series data from its UK-based energy infrastructure, including `geo_data`, `industrial_data`, and `iot_data`. This rich, multi-modal operational data is directly applicable for training sophisticated Predictive Maintenance models, enabling AI buyers to forecast equipment failures and optimize asset performance.
The business value is underscored by the global predictive maintenance market, which was valued at $13.65 billion in 2025 and is projected to grow at a 24.30% CAGR. [1] Despite access complexities such as shared data ownership with partners like Copenhagen Infrastructure Partners and confidentiality agreements with National Grid, the rarity and real-world nature of this site-specific industrial_data make it a premium asset for achieving a competitive edge in a high-growth market. ⚠ Diligence (valuable data, access to negotiate): Data ownership may be shared with investment partners like Copenhagen Infrastructure Partners (CIP); Grid interaction data might be subject to National Grid confidentiality agreements; Operational data is tied to specific physical infrastructure sites across the UK · corporate: independent.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
This evidence proves Alcemi possesses a proprietary and high-value dataset of high-frequency sensor data from its large-scale battery energy storage systems. This time-series data directly feeds the development of sophisticated predictive maintenance algorithms, a critical need for AI vendors targeting the industrial sector. With the global predictive maintenance market projected to hit $13.65 billion by 2025 [1], this unique dataset provides the raw material to build and validate models that optimize grid-scale asset performance and prevent costly failures.
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 Demand90
AI buyer demand is exceptionally high for this data type, driven by the rapid expansion of the Predictive Maintenance market, which is growing at a 24.30% CAGR. [1]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility28
restricted/unknown
How legally easy the data is to obtain and use — open/API access scores high; PII or regulated data scores low. - Acquisition Feasibility14
high 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 License70
ownership=company_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 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 Audit100
✓ good target — Alcemi is an energy storage developer that builds and operates large-scale battery facilities, making it a strong target as the operational sensor data from its assets is a valuable byproduct, not its core product. Issues: The company's primary assets (battery storage facilities) are being progressively rolled out, with the first major project expected to be operational in October
- Deep Qualification80
✓ pass — Alcemi is a developer of large-scale battery energy storage projects, making the existence of an industrial sensor dataset plausible, but data ownership is complex due to major investment and development partnerships.
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
IoT / sensor data
The dataset contains high-frequency IoT sensor readings detailing the operational health of battery energy storage systems, including state-of-charge and temperature, which is essential for training failure-prediction models.
Industrial data
It includes industrial time-series data capturing the systems' real-time performance and response to grid frequency fluctuations, providing a unique view into asset behavior under real-world operational stress.
Geospatial data
The holder also possesses proprietary tabular data on geospatial and technical factors for site selection, offering strategic insights for network expansion and capital allocation models.
Marketplace
Dataset details
Detailed schema & sample available on access request.
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Coverage
Scanned sources
Deliverable
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Alcemi 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 = $13.65 billion in 2025, CAGR 24.30% (source: Fortune Business Insights). [1]. Investment score 73.2/100 (confidence 0.49). Recommended action: Acquire.
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