Dataset opportunity
Okamac — Maintenance Logs Dataset Opportunity
Moderate maintenance logs dataset held by Okamac, usable for Predictive Maintenance and Anomaly Detection.
Score
64.8
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
49%
Action
Data Sharing Agreement
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 was valued at USD 13.4 billion in 2025 and is projected to grow at a CAGR of 23.2% between 2026 and 2035.
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
Maintenance Logs Dataset
Modality
Time Series
Sector
retail
Volume
Moderate
Freshness
Periodic
Rarity
High (proprietary)
Accessibility
Restricted
Legal
Owned by the company — GDPR-sensitive (PII review)
Buyer persona
Industrial AI & maintenance-optimization vendors
Okamac holds a detailed Maintenance Logs Dataset derived from its refurbished electronics operations, containing rich Time Series data. This collection of `industrial_data`, `maintenance_logs`, and `transaction_data` captures technical events, component diagnostics, and repair histories, making it a prime source for training Predictive Maintenance models to anticipate hardware failures in consumer electronics.
The global predictive maintenance market is a significant and rapidly growing sector, valued at USD 13.4 billion in 2025 and projected to grow at a CAGR of 23.2%. [1] This valuable dataset, despite access complexities like PII sanitization, group-level governance, and proprietary software ties, offers a rare opportunity to acquire real-world data for this high-growth market, justifying the negotiation effort for AI buyers. ⚠ Diligence (valuable data, access to negotiate): Data includes PII from previous owners requiring strict sanitization verification; Subsidiary of Recommerce Group, requiring group-level data governance approval; Technical logs are tied to proprietary diagnostic software · corporate: subsidiary of Recommerce Group.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
This evidence proves Okamac holds a rare, proprietary dataset detailing over a decade of Apple device lifecycles, from component-level health to common failure points. This collection of time-series and industrial data is a powerful asset for Industrial AI and maintenance-optimization vendors seeking to build next-generation predictive maintenance models. In a market projected to grow at over 23% annually, this dataset offers a unique opportunity to train algorithms on real-world hardware degradation, repairability, and economic value, enabling highly accurate failure prediction and optimization.
See dimension details ↓- Dataset Specificity90
dominant 'maintenance_logs', sector retail, 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 Freshness46
periodic
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 Demand90
AI buyer demand is extremely high, driven by the rapid expansion of the Predictive Maintenance market, which is growing at a 23.2% CAGR. [1]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility0
PII/regulated
How legally easy the data is to obtain and use — open/API access scores high; PII or regulated data scores low. - Acquisition Feasibility0
medium difficulty, subsidiary of Recommerce Group
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 License62
ownership=company_owned, licensing=gdpr_sensitive
Whether the company can legally license the data out — based on ownership and licensing complexity. - Corporate Independence50
subsidiary of Recommerce Group
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 — 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 — Okamac is a European leader in Mac refurbishment, an operational business that generates valuable maintenance, repair, and component failure data as a by-product, making it an ideal target.
- Deep Qualification90
✓ pass — Okamac is a strong data holder. Its core business of refurbishing Macs generates valuable, coherent maintenance and failure logs. Data ownership is clear, but the presence of PII from previous owners and its subsidiary status require careful navigation for data acquisition.
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
Maintenance logs
The dataset contains detailed, component-level health reports from workshop inspections, providing the granular time-series data essential for training high-fidelity predictive maintenance algorithms.
Transaction data
This evidence confirms the existence of historical pricing and demand data since 2009, allowing AI models to perform sophisticated cost-benefit analysis on maintenance versus replacement decisions.
Industrial data
The holder possesses 15 years of aggregated data on common failure points and repairability scores, offering a strategic, long-term view for validating models and assessing systemic hardware risks.
Marketplace
Dataset details
Detailed schema & sample available on access request.
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Coverage
Scanned sources
Deliverable
Premium dataset report
Okamac Maintenance Logs — a Moderate maintenance logs dataset (Time Series modality) in the retail domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Predictive Maintenance Market was valued at USD 13.4 billion in 2025 and is projected to grow at a CAGR of 23.2% between 2026 and 2035 (source: Market.us). [1]. Investment score 64.8/100 (confidence 0.49). Recommended action: Data Sharing Agreement.
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Learn before you deal
- How a Data Transaction Works3 min read
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- 5 Mistakes That Drive Buyers Away3 min read