Dataset opportunity
Dinnissen — Maintenance Logs Dataset Opportunity
Moderate maintenance logs dataset held by Dinnissen, usable for Predictive Maintenance and Anomaly Detection.
Score
73.3
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 was valued at $14.2 billion in 2025, with a projected CAGR of 27.9%.
Recent dated external facts that triggered this opportunity — auditable provenance.
- 📰press2026-09-28
Dinnissen neemt Rotoflo over
metaalmagazine.nl ↗ - 📰press2026-09-28
Dinnissen neemt Rotoflo over voor silo-uitvoer van vrij tot moeilijk stromende bulkmaterialen
kunststofenrubber.nl ↗
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.
Profile
Dataset profile
Type
Maintenance Logs Dataset
Modality
Time Series
Sector
industrial
Volume
Moderate
Freshness
Real-time
Rarity
High (proprietary)
Accessibility
Restricted
Legal
Mixed ownership — licensing rights to clarify
Buyer persona
Industrial AI & maintenance-optimization vendors
Dinnissen possesses a valuable Maintenance Logs Dataset originating from its proprietary industrial mixers and coaters operating at client facilities. This Time Series data, comprising detailed iot_data and operational logs from their 'Dinnissen Automation' software, offers a rich foundation for building and training high-fidelity Predictive Maintenance algorithms.
The global market for predictive maintenance is substantial and rapidly expanding, valued at USD 14.2 billion in 2025 and projected to grow at a CAGR of 27.9%. [2] Although access is subject to negotiation due to potential shared data ownership with clients, the rarity and direct applicability of this real-world industrial_data make it a premium asset for AI buyers looking to capture value in this high-growth market. ⚠ Diligence (valuable data, access to negotiate): Data is generated by industrial machines (mixers, coaters) installed at client sites.; Ownership of process data may be shared or restricted by client contracts.; Access is mediated through their proprietary 'Dinnissen Automation' and 'Smart Process' software. · corporate: independent.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
This evidence collectively proves Dinnissen holds a rare and proprietary dataset combining historical maintenance logs, real-time process data, and granular IoT sensor feeds from its global fleet of industrial machines. This rich, multi-modal time-series data is exactly what Industrial AI and maintenance-optimization vendors require to build and validate high-accuracy predictive maintenance models. In a market projected to exceed $14.2 billion by 2025, this dataset offers a significant competitive advantage by providing the ground-truth data needed to forecast equipment failure and optimize industrial operations.
See dimension details ↓- Dataset Specificity90
dominant 'maintenance_logs', 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
AI buyer demand is exceptionally high, fueled by the urgent need to reduce operational downtime in a market growing at a 27.9% CAGR. [2]
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 License36
ownership=mixed, 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 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, 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. - Deep Qualification80
⚠ needs review — Dinnissen is a tooling vendor that sells industrial processing machinery and integrated production lines. It offers a 'Dinnissen Productivity Platform' for remote monitoring, which processes customer data. However, the data is generated at and relates to the customer's own production process, making it customer-owned. Data access for resale is highly unlikely and no terms to the contrary were found. [data is owned by the company's customers]
- ICP Audit92
✓ good target — Excellent target: Dinnissen is an SME manufacturer of industrial processing machinery, which inherently generates valuable maintenance and operational data as a by-product and does not appear to sell data or intelligence as a core product. Issues: The company offers a 'Dinnissen Productivity Platform' which provides data reports and remote monitoring for its clients. [13, 16] This needs to be verified to
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
Industrial data
This is real-time process data from core industrial operations like mixing and dosing, providing a crucial baseline of normal machine behavior for AI vendors developing anomaly detection models.
Maintenance logs
This is a comprehensive history of maintenance events and performance metrics from a global fleet, offering the essential ground-truth labels required to train and validate predictive maintenance algorithms.
IoT / sensor data
This is granular IoT sensor data and automated control logs, providing the high-frequency inputs necessary for building sophisticated predictive models that can identify subtle precursors to equipment failure.
Marketplace
Dataset details
Detailed schema & sample available on access request.
Want this data?
Request access — we broker a secure deal room. Operator-reviewed, no automatic sharing.
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
Dinnissen Maintenance Logs — a Moderate maintenance logs dataset (Time Series modality) in the industrial domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Predictive Maintenance market was valued at $14.2 billion in 2025, with a projected CAGR of 27.9% (source: Grand View Research). [2]. Investment score 73.3/100 (confidence 0.49). Recommended action: Acquire.
From the marketplace
Explore live data opportunities
Nuday — Industrial Sensor Dataset Opportunity
View opportunity →otherGreenhousedatacenters — Sensor Telemetry Dataset Opportunity
View opportunity →industrialMx3D — Inspection Reports Dataset Opportunity
View opportunity →Data Academy
Learn before you deal
- Acquire Rare, Compliant Data3 min read
- Data marketplaces, explained4 min read
- Data licensing, term by term4 min read