Dataset opportunity
Ingridcapacity — Industrial Sensor Dataset Opportunity
Moderate industrial sensor dataset held by Ingridcapacity, usable for Predictive Maintenance and Anomaly Detection.
Score
47.5
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 = $12.3 Billion in 2024, CAGR 29.7%.
Recent dated external facts that triggered this opportunity — auditable provenance.
- 📰press2026-09-30
Ingrid Capacity och Mirova investerar i batterilager i Sverige och Finland
energinyheter.se ↗
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
Recruiting Data Engineers and Algorithm Developers for grid optimization
source ↗
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
Ingridcapacity holds a substantial Industrial Sensor Dataset composed of high-frequency Time Series data, including `event_streams` and `iot_data` from its 450MW portfolio of energy assets. This granular operational data is ideally suited for training sophisticated Predictive Maintenance models to anticipate equipment failure and optimize asset performance.
The global market for Predictive Maintenance was valued at $12.3 Billion in 2024 and is projected to grow at a 29.7% CAGR, demonstrating immense demand for relevant data. [7] While access requires navigating complexities such as national security regulations and TSO/DSO confidentiality, the rarity and direct applicability of this industrial_data to a high-value market make it a compelling asset for AI developers seeking a competitive edge. ⚠ Diligence (valuable data, access to negotiate): Operates critical energy infrastructure which may involve national security regulations regarding data sharing.; Data ownership for managed assets (3rd party batteries) must be distinguished from their own 450MW portfolio.; High-frequency grid data is often subject to TSO/DSO confidentiality agreements. · corporate: independent.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
This evidence collectively demonstrates that Ingridcapacity holds a large-scale, proprietary time-series dataset from industrial energy assets under active management. The data captures real-time operations, including participation in high-stakes frequency and balancing markets, and is already proven valuable for machine-learning forecasts. For vendors in the rapidly growing predictive maintenance market ($12.3B in 2024), this dataset offers a rare opportunity to train and validate models on diverse, real-world operational conditions, directly addressing the need for asset optimisation and reliability.
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
Demand for this data is driven by the high-growth Predictive Maintenance market, which is expanding at a 29.7% CAGR. [7]
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 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 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 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 Audit58
⚠ review — The company's core business is selling energy grid optimization and stability as a service, using its own software and physical battery assets, which makes it a seller of intelligence, not a holder of dormant data. Issues: Company's core product is intelligence/software: Ingrid Capacity's business model is to build, own, and operate battery storage facilities and then use its prop; Already monetizing data/insights: The company's primary revenue comes from selling its energy storage and optimization solutions, not from a separate operationa; Describes itself as a software provider: The company explicitly states it builds 'the infrastructure and software' and operates a 'proprietary trading and optim
- Deep Qualification80
✓ pass — Ingrid Capacity is a strong data holder candidate. It develops and operates a large portfolio of battery energy storage systems, using a proprietary data-driven platform for real-time optimization, which generates a valuable industrial sensor dataset. Data ownership is mixed, and licensing rights are unclear due to likely TSO/DSO confidentiality and national security regulations.
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
IoT / sensor data
This evidence indicates a substantial volume of IoT data from a diverse portfolio of managed assets (450 MW), providing the scale and variety needed to train robust predictive maintenance models.
Event streams
The dataset includes high-value event streams from real-time energy balancing markets, which is critical for AI vendors developing models that optimize asset performance against grid reliability and market demands.
Industrial data
This confirms the presence of curated industrial data already structured for and validated by the holder's own machine-learning and optimization engine, significantly reducing data preparation efforts for a buyer.
Marketplace
Dataset details
Detailed schema & sample available on access request.
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Coverage
Scanned sources
Deliverable
Premium dataset report
Ingridcapacity 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 = $12.3 Billion in 2024, CAGR 29.7% (source: Custom Market Insights). [7]. Investment score 47.5/100 (confidence 0.49). Recommended action: Acquire.
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