Dataset opportunity
Tericpower — Industrial Sensor Dataset Opportunity
Moderate industrial sensor dataset held by Tericpower, usable for Predictive Maintenance and Anomaly Detection.
Score
77.8
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
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 = $14.09 billion in 2025, CAGR 34.14%.
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
Partial
Legal
Owned by the company — clean to license
Buyer persona
Industrial AI & maintenance-optimization vendors
Tericpower holds a valuable Industrial Sensor Dataset comprised of Time Series data from its Battery Energy Storage System (BESS) sites. This rich stream of `iot_data` and `industrial_data`, sourced from `event_streams` and SCADA systems, provides the granular operational metrics essential for developing and training Predictive Maintenance models.
The global market for Predictive Maintenance is substantial, estimated at $14.09 billion in 2025 with a projected CAGR of 34.14%. [4] This high-growth demonstrates the immense demand for such datasets, particularly within the energy and utilities sector which is forecast to grow at a similar rate. [4] While access requires navigating data ownership tied to project SPVs and the technical expertise to interpret battery metrics, the rarity and strategic value of this data for optimizing asset performance make it a compelling acquisition for sophisticated AI buyers. ⚠ Diligence (valuable data, access to negotiate): Data ownership may be tied to specific project SPVs (Special Purpose Vehicles).; Operational data is likely siloed within SCADA systems of individual BESS sites.; Technical expertise required to interpret battery chemistry and degradation metrics. · corporate: independent.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
This evidence collectively proves Tericpower owns a rare and proprietary time-series dataset from its fleet of seven operational Battery Energy Storage Systems (BESS). This data directly feeds the development of predictive maintenance models, a critical need for AI vendors targeting the industrial energy sector. In a market projected to exceed $14 billion by 2025, this unique operational sensor data offers a powerful competitive edge for optimizing asset performance and preventing 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 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 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 growth of the Predictive Maintenance market, which is expanding at a 34.14% CAGR. [4]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility62
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 Feasibility4
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 License92
ownership=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 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, 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 Audit100
✓ good target — Tericpower is an ideal target as it's an SME that develops and operates battery energy storage systems, generating proprietary sensor and operational data as a by-product which it does not currently sell.
- Deep Qualification70
✓ pass — Tericpower develops, owns, and operates BESS projects, making the existence of an industrial sensor dataset plausible, but its business model is providing energy services and project development, not selling data. Data ownership is likely complex and unclear due to project-specific financing structu
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
press
- “<p>Les producteurs de biogaz et les vendeurs de gaz fossile réclamaient cette règle du jeu depuis des mois. L’évolution de l’économie du biométhane à partir de 2028 et notamment</p> <p>L’article <a href="https://www.greenunivers.com/2026/06/biomethane-des-cpb-jusquen-2041-un-guichet-ouvert-bientot-ferme-et-beaucoup-de-questions-427854/">Biométhane : des CPB jusqu’en 2041, un guichet ouvert bientôt fermé et beaucoup de questions</a> est apparu en premier sur <a href="https://www.greenunivers.com">GreenUnivers</a>.</p>”
- “<p>Jeune agrégateur d’actifs de production d’électricité renouvelable, Bohr Energie boucle une levée de fonds de série A de 9,5 M€ en fonds propres, après un premier tour de 4 M€ en seed en 2024. Les actionnaires historiques  </p> <p>L’article <a href="https://www.greenunivers.com/2026/06/bohr-energie-collecte-95-me-va-franchir-1-gw-agrege-427457/">Bohr Energie collecte 9,5 M€, va franchir 1 GW agrégé</a> est apparu en premier sur <a href="https://www.greenunivers.com">GreenUnivers</a>.</p>”
- “<p>Electricity demand is climbing at a pace the power industry hasn’t seen in a generation, and the companies trying to feed it with geothermal energy are mostly racing in one direction: drill faster. Ormat Technologies is making a different bet. The company that has built and run geothermal plants for six decades thinks the constraint […]</p> <p>The post <a href="https://www.powermag.com/ormat-bets-on-standardization-to-win-the-geothermal-race/">Ormat Bets on Standardization to Win the Geothermal Race</a> appeared first on <a href="https://www.powermag.com">POWER Magazine</a>.</p> <p><i”
Developer portal
Public-facing documentation confirms Tericpower's market position as a specialized partner in energy storage development, assuring buyers that the underlying data comes from a source with deep domain expertise.
IoT / sensor data
The company confirms it operates seven distinct Battery Energy Storage Systems (BESS), providing a multi-site source of continuous IoT data crucial for training and validating robust predictive models.
Industrial data
Tericpower's stated expertise in BESS optimization indicates the dataset captures a wide range of operational scenarios, enabling the development of sophisticated models that go beyond simple failure prediction to improve asset performance.
Event streams
The company's market dominance in a key region confirms the dataset's proprietary nature, capturing unique event streams from a majority of utility-scale battery projects that competitors cannot replicate.
Marketplace
Dataset details
Geographic coverage
Global
Time range
Real-time
Update frequency
Real-time
Delivery
API
Formats
Time Series, JSON
License
One-time license for use in developing and training predictive maintenance models. Specific usage restrictions to be detailed in the full license agreement.
Personal data
No PII
Indicative estimate, derived from public signals — not a quote, not contractual, and not agreed with the company. Is this your company? Correct it.
This proprietary, high-rarity industrial sensor time-series data from operational BESS sites is highly valuable for predictive maintenance, a rapidly growing market segment. The real-time freshness and moderate volume from seven sites support its premium valuation.
Detailed schema & sample available on access request.
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This listing was generated automatically from public signals. It is not verified, and we are not affiliated with this company.
Coverage
Scanned sources
Deliverable
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Tericpower 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 = $14.09 billion in 2025, CAGR 34.14% (source: Mordor Intelligence). Investment score 77.8/100 (confidence 0.56). Recommended action: Acquire.
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