Dataset opportunity
Dukosi — Industrial Sensor Dataset Opportunity
Large industrial sensor dataset held by Dukosi, usable for Predictive Maintenance and Anomaly Detection.
Score
81.2
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
74%
Action
Partnership (group-level)
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
Global Predictive Maintenance market = $12.3B in 2024, CAGR 29.7% (source: Custom Market Insights). [9]
Recent dated external facts that triggered this opportunity — auditable provenance.
- 📰press2026-06-04
Surge Battery raises $21M for Nevada lithium project
mining.com ↗ - 📰press2026-06-03
USA Rare Earth to invest $1.2B in South Carolina magnet factory
manufacturingdive.com ↗ - 📰press2026-06-03
DTE Energy partners with LG to deploy 6 GWh of battery storage
utilitydive.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.
- 📣Press / announcement
Dukosi combines cell-level data tracking with wireless communication for Battery Passport requirements.
source ↗
Profile
Dataset profile
Type
Industrial Sensor Dataset
Modality
Time Series
Sector
industrial
Volume
Large
Freshness
Real-time
Rarity
Medium
Accessibility
Open / API
Legal
Owned by the company — clean to license
Buyer persona
Industrial AI & maintenance-optimization vendors
Dukosi's core business in advanced battery monitoring systems generates a significant Industrial Sensor Dataset. This consists of high-resolution Time Series data from individual battery cells, capturing critical parameters like voltage, temperature, state of charge, and internal resistance. This granular, real-time data is precisely what is required to train and validate AI models for Predictive Maintenance, enabling the accurate forecasting of battery degradation and prevention of catastrophic failures in industrial applications.
The business value of this data is immense, situated within the global Predictive Maintenance market, which was valued at $12.3B in 2024 and is projected to grow at a massive CAGR of 29.7%. [9] This explosive growth highlights the intense buyer demand for AI-driven efficiencies. While such detailed sensor data can be complex to manage, its rare quality and direct applicability in reducing costly operational downtime make it a premium asset for AI buyers seeking a competitive edge. [4]
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
Dukosi demonstrably possesses a rich, high-fidelity Industrial Sensor Dataset featuring cell-level time series data with comprehensive lifetime lineage. This includes precise voltage and temperature measurements logged continuously from "day 1" via their unique "Cell Passport" system, providing a deep historical record for each battery cell. Such granular, long-term data is in high demand by Industrial AI and maintenance-optimization vendors seeking to develop advanced predictive maintenance solutions for critical battery applications, offering a significant competitive edge in a rapidly electrifying market.
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 Rarity58
proprietary domain data (open lowers rarity)
How scarce and proprietary the data is. Unique domain data scores high; openly available data lowers it. - Dataset Volume82
8 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
The AI-driven predictive maintenance market, which relies heavily on industrial sensor datasets, is projected to grow at a CAGR of 39.5% to reach USD 19.27 billion by 2032, indicating very high and rapidly increasing buyer demand for this d
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 Feasibility69
medium difficulty, acquired of KCK Group
How realistic it is to actually obtain the data, given access difficulty and the holder's corporate structure. - Evidence Strength100
6 evidence types, 8 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 Independence45
acquired of KCK Group
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, 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 Audit75
⚠ review — Dukosi's core business is selling cell monitoring systems that provide cell-level data and intelligence to their customers, which falls under the exclusion criteria for d-nvest's target market. Issues: Dukosi's core business is the development and sale of cell monitoring systems and semiconductor solutions that provide data and intelligence (via API and analyt; The data generated by Dukosi's systems is not dormant; it is an integral part of their product offering and value proposition
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
Developer portal
This evidence indicates Dukosi provides a developer portal with resources and an evaluation platform for battery design and BMS integration, signaling their commitment to enabling external innovation and data interaction for potential AI buyers.
API access
This confirms the existence of a robust API and library for the DK-NFLNK system, facilitating programmatic access to Cell Monitor data and integration with BMS Host systems, which is essential for automated data ingestion by AI platforms.
IoT / sensor data
This directly confirms the collection of accurate, synchronous, on-cell measurements as Time Series data from their Cell Monitors, providing the foundational sensor readings critical for predictive maintenance models.
Industrial data
This specifies the high-precision voltage and temperature measurements collected per cell, highlighting the quality and granularity of the industrial sensor data available, which is crucial for accurate battery health prognostics.
Event streams
This evidence points to continuous 24/7 data monitoring and event logging at the cell-level, indicating a constant flow of Time Series data crucial for real-time insights and anomaly detection in industrial applications.
Data catalog / marketplace
This reveals the "Cell Passport" system, which provides lifetime on-chip storage of both static metadata and dynamic parameters like temperature and voltage, creating a comprehensive data lineage for each individual cell, invaluable for long-term AI model training.
Marketplace
Dataset details
Geographic coverage
Global
Time range
2019-present
Update frequency
Real-time
Delivery
API
Formats
Time Series
License
One-time license for predictive maintenance and analytics, subject to corporate approval and customer collaboration.
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's value is driven by its high-fidelity, long-term industrial sensor time-series data, crucial for predictive maintenance in the battery sector. High AI buyer demand and the unique 'Cell Passport' lineage support a 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
Premium dataset report
Dukosi Industrial Sensor — a Large industrial sensor dataset (Time Series modality) in the industrial domain. Primary AI use-case: Predictive Maintenance. Market signal: $$$ — high AI buyer demand. Investment score 81.2/100 (confidence 0.74). Recommended action: Partnership (group-level).
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