IoT Industry Analytics and
Business Intelligence

Analytics Solutions for the Connected Enterprise

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IoT Analytics Faces Unique Challenges

With over 20 billion IoT-connected devices expected by 2020, the IoT industry is bringing the promise of the connected enterprise into reality for thousands of companies across dozens of diverse industries from manufacturing to retail to agriculture.

But 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.

Challenge #1

Semi-Structured Data

Most machine data is semi-structured in nature, which requires extra processing to collect, organize, transform and prepare it for analysis. Most analytics products however, were built in the structured data era and simply aren’t suited for this task.

Qrvey solves the semi-structured data challenge with its All Data Accepted philosophy. It can efficiently collect and transform the most complex of data types, including semi- and unstructured data, and make it available for a variety of analytics use cases.

Challenge #2

Increased Data Volumes

Machines generate data at a blistering pace and our human workforce isn’t equipped to analyze such large amounts of data efficiently. That’s why IoT providers are looking towards new technologies, like machine learning, to fill in the widening gap in analysis services.

Qrvey solves the data volume challenge by integrating machine learning into the platform to augment data, spot trends and gain insights faster and better than traditional human-focused solutions.

Challenge #3

Data Performance

IoT devices are generating data on a real-time basis, so logic dictates that analysis needs to happen at the speed the data is being generated. This required IoT providers to accept a completely new time scale for reporting in real-time as their new normal.

Qrvey solves the performance problem using its cloud-native architecture, leveraging the full power of the cloud and AWS to economically meet any analytics challenge. Datasets with hundreds of millions of rows can now deliver insights in just seconds.

Challenge #4

Instant Scalability

Building IoT solutions that can solve the first three challenges aren’t much good if they’re not highly flexible and scalable enough to meet changing customer demands and future growth. IoT providers are constantly struggling to deploy analytics solutions that can outpace these ever-increasing requirements.

Qrvey solves the scalability challenge using microservices. The platform utilizes dozens of AWS microservices that were selected and tuned to provide the most flexibility and scalability for your analytics applications, ensuring that your solutions will evolve as fast as the industry overall.

Customizable IoT Analytics and BI Platform

Qrvey’s All-in-One Analytics Solution

Qrvey is the only all-in-one, cloud-native analytics solution that gives IoT providers everything they need to solve their industry’s biggest challenges.

The ultimate goal of IoT providers is to allow their customers to collect data in a format that can be directly and immediately available for a variety of analytics applications, from business intelligence, to self-service analytics and advanced machine learning models. Only then can the promise of the connected enterprise be realized, where decisions can be made at the speed of the data collected.

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IoT Industry Analytics and Business Intelligence

Analytics Solutions for the Connected Enterprise

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