Dataset opportunity
Aktiia — Sensor Telemetry Dataset Opportunity
Large sensor telemetry dataset held by Aktiia, usable for Predictive Maintenance and Anomaly Detection.
Score
68.8
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
74%
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
Global Predictive Maintenance market = USD 14.31 billion in 2025, CAGR 30.5% (2026-2035)
Recent dated external facts that triggered this opportunity — auditable provenance.
- 📰press2026-06-05
J&J, Medtronic back CereVasc’s $85M funding round
medtechdive.com ↗ - 📰press2026-06-05
Olympus’ Keith Boettiger on robotic GI surgery push
medtechdive.com ↗ - 📰press2026-06-05
Dexcom buys Nutrisense; Insulet rolls out patch pump update
medtechdive.com ↗ - 📰press2026-06-04
Can surgical robots fly? SS Innovations discusses challenges, solutions
therobotreport.com ↗ - 📰press2026-06-04
More Americans own wearables, connected health devices: survey
medtechdive.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.
Profile
Dataset profile
Type
Sensor Telemetry Dataset
Modality
Time Series
Sector
healthcare
Volume
Large
Freshness
Real-time
Rarity
Medium
Accessibility
Restricted
Legal
Mixed ownership — GDPR-sensitive (PII review)
Buyer persona
Industrial AI & maintenance-optimization vendors
Aktiia possesses a Sensor Telemetry Dataset of Time Series data, derived from medical devices, offering real-time physiological measurements crucial for healthcare applications. This valuable data, encompassing medical records and IoT data, is ideal for Predictive Maintenance as it enables the identification of patterns and anomalies in device performance or patient health, allowing for proactive interventions. The dataset's richness supports advanced AI analytics for forecasting potential issues before they escalate.
The market for such data is experiencing significant growth, with the global healthcare data monetization market valued at USD 998.3 million in 2024 and projected to reach USD 4,077.14 million by 2034, at a CAGR of 18.2%. Furthermore, the broader predictive maintenance market was valued at USD 14.31 billion in 2025 and is expected to reach USD 205 billion by 2035, expanding at a 30.5% CAGR, with the healthcare segment specifically anticipated to grow at a 31.0% CAGR. Despite the complexities of strict anonymization, pseudonymization, robust consent management, and adherence to regulatory compliance (GDPR, FDA, CE Mark), the high demand from AI buyers for data-driven insights makes this dataset exceptionally valuable. ⚠ Diligence (valuable data, access to negotiate): Data contains highly sensitive personal health information (PHI) requiring strict anonymization, pseudonymization, and robust consent management for any licensing or sharing.; Regulatory compliance (GDPR, medical device regulations, FDA, CE Mark) is paramount for any data licensing or use, especially given its medical device status.; User content ownership remains with users; any data licensing must clearly distinguish between raw user data and derived/aggregated/anonymized insights controlled by Aktiia (Hilo). · corporate: independent.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
Aktiia holds an exceptional collection of time-series data, encompassing over 10 billion signals from more than 70,000 users, providing a robust foundation for advanced AI model development. This extensive dataset, capturing complex physiological patterns, offers significant value for Industrial AI and maintenance-optimization vendors. It enables the training and validation of predictive maintenance algorithms designed to detect subtle anomalies and forecast critical events, a capability essential in a rapidly growing market projected to reach USD 14.31 billion by 2025.
See dimension details ↓- 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. - Dataset Specificity78
dominant 'iot_data', sector healthcare, 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 Rarity46
proprietary domain data (open lowers rarity)
How scarce and proprietary the data is. Unique domain data scores high; openly available data lowers it. - Dataset Volume98
8 evidence hits, explicit data-volume mention
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. - Buyer Demand92
The global AI in healthcare market, a key buyer segment for such data, is projected to grow at a CAGR of 37.1% from 2024 to 2032, reaching USD 317.1 billion, with the healthcare segment of the predictive maintenance market also showing a st
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility14
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 Feasibility32
high difficulty, independent
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 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, 5 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 — Aktiia (rebranding to Hilo) is a well-funded SME that collects significant proprietary sensor telemetry data, but its core business is centered on providing AI-driven insights and data services derived from this data, making it an unsuitable target for a dormant data marketplace. Issues: Aktiia's core business is described as a 'blood pressure intelligence platform' and its mission is to provide 'groundbreaking insights into blood pressure manag; They explicitly offer 'AI-driven predi
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 the core asset: Aktiia possesses an immense volume of IoT time-series data, comprising over 10 billion signals from tens of thousands of users, making it exceptionally valuable for training predictive algorithms.
Public datasets
This evidence points to Aktiia's expertise in analyzing and presenting public perception data related to blood pressure, offering valuable market intelligence for strategic planning.
Downloads / exports
This indicates Aktiia's capability to generate and distribute proprietary reports based on their data, providing accessible market insights to a broader audience.
Data-volume signal
This highlights Aktiia's ability to capture and analyze multimodal data at scale, revealing granular, sub-surface patterns in blood pressure behavior that are critical for deep analytical models.
Medical records / imaging
This confirms Aktiia's direct collection of diverse health data, including blood pressure, weight, and heart rate, providing rich contextual information for advanced physiological modeling.
Data catalog / marketplace
This demonstrates Aktiia's capability to publish multimodal reports based on their proprietary data, showcasing their analytical insights and market understanding.
Coverage
Scanned sources
Deliverable
Premium dataset report
Aktiia Sensor Telemetry — a Large sensor telemetry dataset (Time Series modality) in the healthcare domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Predictive Maintenance market = USD 14.31 billion in 2025, CAGR 30.5% (2026-2035). Investment score 68.8/100 (confidence 0.74). Recommended action: Data Sharing Agreement.