IoT Analytics Platform and Solutions
Embedded Analytics Solutions for the IoT SaaS Applications
IoT Analytics Platforms Faces Unique Challenges
For IoT providers, the challenge has quickly shifted from merely connecting devices and collecting data from devices to the far greater challenge of analyzing and acting on the mountains of data that are now being created within IoT analytics solutions.
Semi-Structured Data
Qrvey can efficiently collect and transform the most complex of data types, including semi- and unstructured data.
Native Data Warehouse
Qrvey includes a built-in data warehouse specifically built for large IoT data volumes for IoT analytics use cases.
Data Performance
As a cloud-native platform, Qrvey has the scalability and performance to meet the most demanding use cases.
Scalable and Secure
Qrvey solves the scalability challenge using serverless technology to provide the most flexibility and scalability for your SaaS application.
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IoT Industry Analytics and Business Intelligence
Analytics Solutions for the Connected Enterprise
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IoT Analytics Software Needs Embedded Analytics
Qrvey is the only truly cloud-native embedded analytics solution that gives IoT providers a complete solution to reduce the need to build in-house.
The ultimate goal of IoT analytics services is to allow their customers to collect IoT data in a format that can be directly and immediately available for analysis within IoT SaaS applications. Only then can the promise of the connected enterprise be realized, where decisions can be made at the speed of the data collected.
REDUCE COMPLEXITY
IoT Analytics And Embedded Analytics
True embedded analytics for IoT SaaS applications requires a comprehensive solution to provide the ROI to SaaS companies.
Qrvey approach to embedded analytics starts with a built-in data layer to power embedded visuals that enable users to build their down dashboards within your SaaS application.
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Embedded Dashboards and Builder
Offer pre-built dashboards faster than ever. When individual users want more, empower them with custom dashboard builders available within your IoT SaaS application.
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Unified Data Pipeline
Scaling data analysis on large volumes of data is tough to build in-house. Save yourself time and money as Qrvey’s embedded analytics solution an enterprise scalable data lake with unified pipeline for any data.
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Embedded Workflow Automation
Qrvey offers no-code automation tools that puts advanced logic including alerting, data write-backs and webhooks into your IoT SaaS application.
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Key Capabilities of Qrvey for IoT Analytics
Real-Time Data Processing
Spot trends in real-time, support rapid decision-making and error reduction with real-time data analysis.
Enterprise Scale
Efficiently scale to billions of records and thousands of concurrent users while reducing infrastructure costs.
Integrated AI
Use AI to predict outliers and anomalies to keep operations running smoothly and on track.
Multi-Tenant Data Lake
Scale analytics offering by combining data sources into a scalable central analytics layer for multi-tenant apps.
Data Governance
Manage data security by ensuring data quality, regulatory compliance, and effective data stewardship.
Integration Ready
Unified data pipeline supporting integrations from enterprise systems, applications, and all data sources.
Self-Service Analytics
Empower users to build their own custom dashboards using Qrvey’s multi-tenant security layer and embedded builders.
Embedded Data Visualization
Seamlessly integrate Qrvey into your application and users will never know a third party solution is in use.
Support
Your success is our success. Our award willing support team is with your every step of the way.
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Analytics for Those Who Want More
Build Less Software. Deliver More Value.
Frequently Asked Questions
IoT analytics solutions include collecting and analyzing data from internet-connected sensors, devices and systems to generate insights about operations, efficiency and product usage.
- IoT engineers/analysts
- Operations managers
- Product managers
- Executives in manufacturing, transportation, energy and other IoT adopting industries.
- Sensor monitoring – Analyze sensor data to detect anomalies and trigger alerts.
- Predictive maintenance – Forecast equipment failures to optimize maintenance scheduling.
- Usage patterns – Analyze product usage data to redesign products or target new applications.
- Operational optimization – Analyze systemic efficiencies to reduce costs and waste.
- Supply chain tracking – Monitor location, condition and handling of supply chain assets in transit.
- Smart spaces – Provide insights about occupancy, energy use, environmental conditions in workplaces/cities.
- Network management – Monitor network performance in near real-time to improve quality of service.