Dataset opportunity
Tupala Yhtyma — Industrial Sensor Dataset Opportunity
Moderate industrial sensor dataset held by Tupala Yhtyma, usable for Predictive Maintenance and Anomaly Detection.
Score
70.9
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 USD 13.65 billion in 2025, projected to grow at a CAGR of 24.30% (2026-2034).
Recent dated external facts that triggered this opportunity — auditable provenance.
- 📰press2026-10-14
Arjes Ekomaxx 800 ei nirsoile: Tupala-Yhtymä käsittelee monipuolista materiaalia tehokkaasti
koneporssi.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
Article highlighting focus on 'new techniques' and specialized machinery in earthmoving
source ↗
Profile
Dataset profile
Type
Industrial Sensor Dataset
Modality
Time Series
Sector
industrial
Volume
Moderate
Freshness
Real-time
Rarity
High (proprietary)
Accessibility
Partial
Legal
Owned by the company — clean to license
Buyer persona
Industrial AI & maintenance-optimization vendors
Tupala Yhtyma holds a valuable Industrial Sensor Dataset with a Time Series modality, derived from its real-world operations as an industrial contractor for clients like Fortum. This data, encompassing `iot_data`, fleet telematics, and project logs, provides a rich foundation for developing and validating Predictive Maintenance models, as it captures the authentic operational signatures of industrial equipment leading up to potential failures.
The business value of this data is underscored by the massive and growing market for these solutions. The global Predictive Maintenance market was valued at USD 13.65 billion in 2025 and is projected to expand at a CAGR of 24.30% through 2034. While access complexities exist—such as the data's primary Finnish language and its storage in internal ERP systems—its rarity and proven industrial provenance make it a high-value asset for AI buyers aiming to capture a share of this high-growth market. ⚠ Diligence (valuable data, access to negotiate): Primary data is in Finnish; Operational data is generated as a contractor for industrial clients like Fortum (formerly Ekokem); Fleet telematics and project logs are likely stored in internal ERP/fleet management systems · corporate: independent.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
This evidence collectively proves Tupala Yhtyma owns a proprietary dataset of time-series sensor data from a fleet of over 14 heavy machines, including excavators, operating in demanding environmental construction sites. This high-rarity data is a direct match for industrial AI vendors developing predictive maintenance models to anticipate component failure and optimize fleet uptime. In a market valued at over USD 13.6 billion and growing at a CAGR of 24.30%, this dataset offers a significant competitive advantage for training and validating next-generation maintenance-optimization algorithms.
See dimension details ↓- Dataset Specificity78
dominant 'iot_data', sector industrial, 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 Rarity70
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 Value74
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, driven by the market's rapid expansion for Predictive Maintenance solutions, with a forecasted CAGR of 24.30%.
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility50
restricted/unknown
How legally easy the data is to obtain and use — open/API access scores high; PII or regulated data scores low. - Acquisition Feasibility44
low 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 License92
ownership=company_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 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 Surplus70
surplus=medium, 1 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 Audit100
✓ good target — This Finnish earthworks and recycling SME is a strong target as its core business is operational services, and it likely generates valuable, dormant data from its large fleet of industrial machinery.
- Deep Qualification80
⚠ needs review — The target is a service provider for industrial clients, and the data generated during projects is likely owned by the customer, posing a significant obstacle to acquisition. [data is owned by the company's customers]
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
IoT / sensor data
This is time-series data generated from a fleet of over 14 heavy construction machines, providing a direct source for training AI models to predict component failure under real-world operational stress.
Industrial data
This evidence documents the operational context of the sensor data, linking machine performance to specific industrial workloads like precise earthmoving and material tracking in specialized construction projects.
business_records
These business documents provide essential ground-truth data, including project and maintenance histories, which are critical for annotating sensor streams and validating the performance of predictive models.
Marketplace
Dataset details
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
Tupala Yhtyma Industrial Sensor — a Moderate industrial sensor dataset (Time Series modality) in the industrial domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Predictive Maintenance market was valued at USD 13.65 billion in 2025, projected to grow at a CAGR of 24.30% (2026-2034) (source: Fortune Business Insights). Investment score 70.9/100 (confidence 0.49). Recommended action: Acquire.
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