Dataset opportunity
Powin — Industrial Operations Dataset Opportunity
Large industrial operations dataset held by Powin, usable for Industrial Monitoring and Forecasting.
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
92%
Action
License
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 Battery Energy Storage System (BESS) Market is projected to reach USD 105.96 billion by 2030 from USD 50.81 billion in 2025, at a CAGR of 15.8%.
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.
Profile
Dataset profile
Type
Industrial Operations Dataset
Modality
Time Series
Sector
industrial
Volume
Large
Freshness
Real-time
Rarity
High (proprietary)
Accessibility
Open / API
Legal
Mixed ownership — clean to license
Buyer persona
Industrial AI integrators
Powin holds a valuable Industrial Operations Dataset composed of Time Series data from its global fleet of Battery Energy Storage Systems (BESS). This data, including `iot_data`, `maintenance_logs`, `geo_data`, and `event_streams`, provides a comprehensive view of BESS performance, degradation, and operational behavior, making it ideal for developing advanced Industrial Monitoring and predictive maintenance AI models. Access is managed through integration with Powin's proprietary HybridOS and Remote Operations Center (ROC).
This data is situated within the rapidly growing BESS software market, which is directly tied to the operational value of such systems. The market is projected to reach $105.96 billion by 2030, growing at a CAGR of 15.8%. [12] Despite access complexities, such as integration requirements and the potential need for data anonymization, the rarity and depth of this industrial_data offer a significant competitive advantage for AI buyers aiming to capture value in the high-growth energy sector. [12] ⚠ Diligence (valuable data, access to negotiate): Data is generated from physical assets (BESS) often owned by third-party developers or utilities.; Access requires integration with their proprietary HybridOS and Remote Operations Center (ROC).; Anonymization of specific grid locations or client IDs may be required for secondary licensing. · corporate: independent.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
This evidence collectively demonstrates ownership of high-fidelity time-series data from industrial Battery Energy Storage Systems (BESS). The dataset includes detailed SCADA feeds, real-time event streams, and asset health metrics, which are critical for developing advanced industrial monitoring and predictive maintenance models. For AI integrators, this proprietary data is a direct pathway to capturing value in the BESS market—projected to double by 2030—where optimizing site performance and asset lifespan is a key competitive advantage.
See dimension details ↓- Dataset Specificity100
dominant 'industrial_data', sector industrial, 5 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 (open lowers rarity)
How scarce and proprietary the data is. Unique domain data scores high; openly available data lowers it. - Dataset Volume100
15 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 Value100
fit for Industrial Monitoring
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 very high, driven by the significant growth in the BESS market (CAGR of 15.8%) and the critical need for data-driven industrial monitoring solutions to optimize energy assets. [12]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility90
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
7 evidence types, 15 hits
How solid the proof is that the company holds this data — diversity of evidence types and number of hits. - Right to License58
ownership=mixed, 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 Orientation22
0 data-appetite signals (0 types)
How actively the company invests in data, measured by its data-appetite signals (hires, products, APIs…). - Dormant Data Surplus92
surplus=high, 2 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 — Powin's core business is selling fully integrated battery energy storage systems (BESS), which include advanced software (StackOS) for energy management, monitoring, and optimization as a key product feature, making it a seller of intelligence rather than a holder of dormant data. Issues: The company's core product is a vertically integrated hardware and software platform. [5, 13]; The software, StackOS, is marketed as an advanced battery management and energy management system that p
- Deep Qualification90
✓ pass — The target, Powin, no longer exists as an independent entity; it filed for bankruptcy in June 2025 and its assets, including all its intellectual property and software (StackOS), were acquired by FlexGen in August 2025. Therefore, any data opportunity now resides with FlexGen, the new owner.
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
Developer portal
This evidence points to technical documentation and developer resources, proving the underlying data is structured for programmatic access and integration, which accelerates time-to-value for AI teams.
Industrial data
This is granular time-series data from industrial control systems, including SCADA and power plant controller logs, essential for modeling and optimizing complex site-level operations like load balancing.
Event streams
These are continuous streams of real-time operational data from battery plants, providing the foundational data for training models that monitor asset health and enable immediate decision-making.
press
- “<p>A major renewable energy developer and a leading independent asset manager have joined to support a portfolio of battery energy storage systems in Poland, part of the continuing buildout of new power infrastructure across Europe.</p> <p>The post <a href="https://www.powermag.com/battery-energy-storage-grid-investments-surge-across-europe/">Battery Energy Storage, Grid Investments Surge Across Europe</a> appeared first on <a href="https://www.powermag.com">POWER Magazine</a>.</p> <p><img alt="Fig1-ABB-Dalmine_c" class="attachment-post-thumbnail size-post-thumbnail wp-post-image" height="269"”
- “<p>Support from the U.S. Department of Energy will be critical to the industry’s near-term success as customers look to lithium alternatives, International Zinc Association officials told Utility Dive.</p>”
Downloads / exports
This refers to technical marketing collateral like white papers, which provide crucial context on system design and intended operational parameters surrounding the raw data.
Geospatial data
This indicates structured logs of complex operational sequences, such as black start procedures, which are invaluable for training AI to manage critical, high-stakes grid events.
IoT / sensor data
This is sensor-level data from connected assets, providing the ground-truth from the energy management system needed to train highly accurate predictive models and identify performance limitations.
Maintenance logs
These are system logs detailing asset uptime and the application of predictive maintenance software, which provide direct evidence for validating and improving AI-driven maintenance strategies.
Marketplace
Dataset details
Geographic coverage
Global
Time range
Real-time
Update frequency
Real-time
Delivery
API integration with HybridOS and ROC
Formats
Time Series, Logs, Event Streams
License
One-time license for AI model development and industrial monitoring use cases.
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 dataset offers high-fidelity, real-time time-series data from a global fleet of BESS, crucial for advanced industrial monitoring and predictive maintenance in a rapidly growing market. Its proprietary nature and large volume of operational insights drive significant value.
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
Premium dataset report
Powin Industrial Operations — a Large industrial operations dataset (Time Series modality) in the industrial domain. Primary AI use-case: Industrial Monitoring. Market signal: Global Battery Energy Storage System (BESS) Market is projected to reach USD 105.96 billion by 2030 from USD 50.81 billion in 2025, at a CAGR of 15.8% (source: MarketsandMarkets). [12]. Investment score 47.5/100 (confidence 0.92). Recommended action: License.
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