Dataset opportunity
Store Dot — Industrial Operations Dataset Opportunity
Moderate industrial operations dataset held by Store Dot, usable for Industrial Monitoring and Forecasting.
Score
75.8
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
58%
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
The global **Industrial AI market** reached **$43.6 billion in 2024** and is projected to grow at a **CAGR of 23% to $153.9 billion by 2030**. The **predictive maintenance market**, a key application for this data, was estimated at **$14.29 billion in 2025** and is projected to reach **$98.16 billion by 2033**, growing at a **CAGR of 27.9%**. The broader **Industrial IoT market** is valued at **$514.39 billion in 2025** and is anticipated to reach **$2430.21 billion by 2035**, expanding at a **CAGR of 16.8%**. The **time series analytics market** alone is valued at **$4.8 billion in 2025** and is projected to reach **$14.2 billion by 2034** at a **CAGR of 12.8%**. Despite complexities arising from multiple strategic investors, a SPAC merger process, and recent financial challenges, the **high demand** for **AI training data** (which generated $800 million in 2025 and is projected to grow to $2–$3 billion by 2027) and the **90-98% profit margins** in data licensing underscore the **significant business value** of this dataset, making access negotiation worthwhile.
Recent dated external facts that triggered this opportunity — auditable provenance.
- 📰press2026-06-09
Batteries : Eclipse lève 20 M€ et regarde vers l’Espagne
greenunivers.com ↗ - 📰press2026-06-07
Op-Ed: Sodium-ion batteries are not the end of lithium, but they may be the end of something else
mining.com ↗ - 📰press2026-06-05
Jungheinrich teste des batteries sodium-ion pour ses chariots
supplychainmagazine.fr ↗
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.
- ✨Signal
Website mentions 'AI-Charged' technology for battery optimization.
source ↗ - 📝Published article
Blog post/article on 'The role of AI in improving battery cell R&D productivity'.
source ↗ - 🧑💻Hiring a data role
Careers page highlights 'Data Science. Applying Data Science and AI to speed up chemistry screening.'
source ↗
Profile
Dataset profile
Type
Industrial Operations Dataset
Modality
Time Series
Sector
mobility
Volume
Moderate
Freshness
Real-time
Rarity
Medium
Accessibility
Open / API
Legal
Owned by the company — clean to license
Buyer persona
Industrial AI integrators
Store Dot possesses a rich Industrial Operations Dataset with a Time Series modality, encompassing downloads, industrial_data, iot_data, and a knowledge_base. This data is highly valuable for Industrial Monitoring applications, particularly for enabling predictive maintenance, optimizing operational efficiency, and facilitating real-time decision-making in the mobility sector. The granularity and historical depth of this data, collected from industrial equipment via sensors, are crucial for training advanced AI models to detect anomalies and forecast equipment behavior. ⚠ Diligence (valuable data, access to negotiate): Multiple strategic investors may complicate data licensing discussions.; Company is undergoing a SPAC merger process, which adds complexity.; Recent financial challenges and layoffs indicate potential instability.; Business model is licensing technology, not selling data directly. · corporate: independent.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
StoreDot possesses a rich, proprietary Industrial Operations Dataset primarily composed of Time Series data, stemming from over two decades of advanced battery development and rigorous testing. This unique data directly addresses the surging demand from Industrial AI integrators for industrial monitoring and predictive maintenance solutions, a market projected to reach $98.16 billion by 2033. By offering insights into battery performance, degradation, and operational conditions, this dataset is critical for training AI models that optimize industrial assets, unlocking significant value in the rapidly expanding Industrial IoT and Industrial AI sectors. Its rarity and direct applicability make it a compelling asset for immediate evaluation, tapping into the 90-98% profit margins seen in data licensing.
See dimension details ↓- Dataset Specificity78
dominant 'industrial_data', sector mobility, 2 specific types
How sharply the data targets a specific, hard-to-substitute domain or task. Niche, well-defined data scores higher than generic. - Dataset Rarity46
proprietary domain data (open lowers rarity)
How scarce and proprietary the data is. Unique domain data scores high; openly available data lowers it. - Dataset Volume64
5 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 Value74
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
The AI in mobility market, which includes predictive maintenance applications, is projected to grow at a compound annual growth rate (CAGR) of 44.6% from 2026 to 2035, indicating a very high and rapidly increasing demand for industrial oper
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility78
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 Feasibility50
high difficulty, independent
How realistic it is to actually obtain the data, given access difficulty and the holder's corporate structure. - Evidence Strength77
4 evidence types, 5 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 Orientation73
3 data-appetite signals (3 types)
How actively the company invests in data, measured by its data-appetite signals (hires, products, APIs…). - Dormant Data Surplus92
surplus=high, 3 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 — StoreDot is a well-funded deep-tech company developing extreme fast-charging EV batteries, generating valuable R&D data, but its large size, unicorn valuation, and strategic partnerships exclude it as an ideal SME target for dormant data. Issues: StoreDot is not an SME; it has approximately 233 employees and a valuation of $1.5 billion, making it a large, well-established company.; The ICP explicitly excludes 'giants/opaque groups' and seeks 'ideally an SME', which StoreDot does not f
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
Downloads / exports
This evidence indicates StoreDot's public-facing materials, including corporate overviews and insights into their data science approach, reflecting a long history of knowledge generation over "two decades," providing crucial context and validation for potential buyers interested in data-driven innovation.
Industrial data
This evidence confirms StoreDot's core expertise in EV battery development, highlighting their use of AI to generate specialized Time Series data, which is highly relevant for industrial monitoring and predictive maintenance applications.
IoT / sensor data
This concrete evidence showcases real-world operational data from extensive battery pack-level testing, including performance under extreme conditions, all captured as Time Series data, invaluable for predictive maintenance and asset optimization in Industrial IoT.
Knowledge base / docs
This evidence demonstrates StoreDot's sophisticated internal use of AI, data science, and machine learning to accelerate battery development, confirming their capability to generate and aggregate millions of data points for advanced predictive modeling, making it attractive for buyers seeking data from AI-native operations.
Coverage
Scanned sources
Deliverable
Premium dataset report
Store Dot Industrial Operations — a Moderate industrial operations dataset (Time Series modality) in the mobility domain. Primary AI use-case: Industrial Monitoring. Market signal: The global **Industrial AI market** reached **$43.6 billion in 2024** and is projected to grow at a **CAGR of 23% to $153.9 billion by 2030**. The **predictive maintenance market**, a key application for this data, was estimated at **$14.29 billion in 2025** and is projected to reach **$98.16 billion by 2033**, growing at a **CAGR of 27.9%**. The broader **Industrial IoT market** is valued at **$514.39 billion in 2025** and is anticipated to reach **$2430.21 billion by 2035**, expanding at a **CAGR of 16.8%**. The **time series analytics market** alone is valued at **$4.8 billion in 2025** and is projected to reach **$14.2 billion by 2034** at a **CAGR of 12.8%**. Despite complexities arising from multiple strategic investors, a SPAC merger process, and recent financial challenges, the **high demand** for **AI training data** (which generated $800 million in 2025 and is projected to grow to $2–$3 billion by 2027) and the **90-98% profit margins** in data licensing underscore the **significant business value** of this dataset, making access negotiation worthwhile.. Investment score 75.8/100 (confidence 0.58). Recommended action: License.