Dataset opportunity
Tado — Maintenance Logs Dataset Opportunity
Moderate maintenance logs dataset held by Tado, usable for Predictive Maintenance and Anomaly Detection.
Score
47.5
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, projected to grow at a 23.2% CAGR (2026-2035).
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
other
Volume
Moderate
Freshness
Real-time
Rarity
High (proprietary)
Accessibility
Restricted
Legal
Mixed ownership — GDPR-sensitive (PII review)
Buyer persona
Industrial AI & maintenance-optimization vendors
Tado holds extensive Time Series data from its smart thermostat ecosystem, including `iot_data`, `event_streams`, and detailed `maintenance_logs`. These datasets capture real-world operational and failure patterns of HVAC systems, making them a prime resource for developing a Predictive Maintenance model.
The global Predictive Maintenance market is substantial, valued at USD 13.4 billion in 2025 and projected to grow at a 23.2% CAGR. [9] While access requires navigating high GDPR sensitivity due to home occupancy patterns and shared data ownership, the rarity and richness of this real-world HVAC performance data offer a significant competitive advantage in this rapidly growing market. ⚠ Diligence (valuable data, access to negotiate): Data involves private home climate and occupancy patterns (high GDPR sensitivity).; Ownership is shared with end-users; requires anonymization or specific consent for secondary use.; Company already has an 'Energy Cockpit' and grid-balancing products, suggesting they know the data's value. · corporate: independent.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
This evidence collectively proves Tado owns a large-scale, proprietary dataset detailing the real-world performance and failure of residential HVAC systems. This unique collection of time-series data, including specific error codes and performance logs, directly enables the development of predictive maintenance algorithms. For industrial AI and maintenance-optimization vendors, this dataset is a rare opportunity to train models that can anticipate equipment failures, a critical capability in a global market projected to grow at over 23% annually.
See dimension details ↓- Dataset Specificity74
dominant 'maintenance_logs', sector other, 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 Demand90
AI buyer demand is extremely high, driven by the Predictive Maintenance market's rapid expansion, which is projected to grow at a 23.2% CAGR. [9]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility20
restricted/unknown
How legally easy the data is to obtain and use — open/API access scores high; PII or regulated data scores low. - Acquisition Feasibility14
high 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 License28
ownership=mixed, licensing=gdpr_sensitive
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 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 Audit58
⚠ review — Tado's core business is selling hardware and a subscription-based AI software service that provides energy-saving intelligence, making it a bad target as it already sells intelligence as a product. Issues: Company's core business is selling intelligence/AI software, which is an explicit exclusion criterion.; The business model revolves around a paid subscription for AI-driven features (AI Assist) that optimize energy consumption. [18, 19]; The company has evolved from a hardware seller to a 'Home Energy Platform' that sells 'heating and cooling as a service'. [8, 11]; The data collected is not 'dormant'; it is the primary fuel for the AI/analytics services they charge for. [18, 19]
- Deep Qualification90
✓ pass — Tado is a data holder selling smart heating hardware and subscription-based software features. The underlying IoT data is a byproduct, not the core product. Ownership is mixed due to GDPR and user-generated data, and while no explicit resale restrictions were found, the data is highly sensitive. The 'Maintenance Logs' label is coherent with their predictive maintenance features.
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
IoT / sensor data
The company captures real-time time-series data on indoor environmental conditions and heating activity from millions of connected devices, providing essential context for HVAC performance models.
Maintenance logs
This dataset contains direct performance data and error codes from over 20,000 digitally connected heating systems, offering ground-truth logs essential for training predictive failure algorithms.
Event streams
Tado aggregates data streams that correlate residential energy demand with external variables like weather and dynamic pricing, a valuable input for energy optimization and grid management solutions.
Marketplace
Dataset details
Detailed schema & sample available on access request.
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Coverage
Scanned sources
Deliverable
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Tado Maintenance Logs — a Moderate maintenance logs dataset (Time Series modality) in the other domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Predictive Maintenance market was valued at USD 13.4 billion in 2025, projected to grow at a 23.2% CAGR (2026-2035). [9]. Investment score 47.5/100 (confidence 0.49). Recommended action: Data Sharing Agreement.
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