Internet of Things Analytics
The Internet of Things (IoT) is here. Our houses, cars, manufacturing equipment, plane engines, and pretty much everything else you can imagine - don’t forget your cell phone - are loaded with sensors that send billions and trillions of pieces of data about what we are doing, where we are, and how mechanical components are performing 24x7x365.
By 2020, Gartner predicts that more than half of major new business processes and systems will incorporate some element of the IoT. Analytics are essential to the success of IoT solutions. It’s not just collecting and storing all that data that poses a challenge. Businesses need to derive insights, automate decisions, and build new data-centric services that move your business forward.
Modern data applications in today’s connected world require real-time monitoring of tremendous amounts of fast-moving data. They also require the ability to perform historical analysis for deeper insights. Arcadia Enterprise provides data-native visual analytics to help you rapidly build analyses and applications that are secure, performant, and scalable so they can gain continuous insights from IoT data analytics.
“We ingest 10TB in 5M raw files of semi-structured and unstructured data from systems out in the field every week. Arcadia Enterprise helps us ingest, analyze, and visualize this raw information to provide easily-consumed visual data applications for our technical support, customer service, product engineering, and sales & marketing teams. This information is invaluable in understanding how customers use our systems. With Arcadia, we have instant insight into detailed granular information on product usage trends, failure reports, component failure patterns, and an understanding of potential customer opportunities."
-Enterprise Storage Manufacturer
- Collect and analyze data from vehicles, in-road sensors and other sources in a single platform.
- Visually analyze real-time streaming telematics data alongside historical data for deeper pattern analysis.
- Understand traffic patterns, offer more targeted insurance packages and provide specific service recommendations.
- Remotely diagnose fleet issues.
- Ingest real-time and batch data from all sensors and components.
- Visually analyze historical patterns within massive data sets to predict which components and pieces of equipment are likely to fail.
- Avoid costly downtime by repairing parts proactively.
System Log Analysis
- Collect, store and analyze all logs in a single platform.
- Efficiently diagnose and solve system issues remotely.
- Analyze how various features, components and configurations affect system performance.
Manufacturing Quality Assurance
- Move from batch-level visibility to micro-unit-level visibility throughout the product lifecycle.
- Analyze remote sensor data to monitor appliance components to understand and improve performance.
- Improve product design by collecting all sensor data from all equipment in the field in real-time rather than relying on batches and samples.
Data Center and System Monitoring
In this case study, we’ll show you how one data infrastructure company was able to analyze sensor data in field systems to work to gain insights for planning product roadmaps, improving customer satisfaction and achieving revenue objectives.
Connected Vehicle Demo
See how Arcadia Data can analyze IoT-scale data from connected vehicles. Integrate real-time streaming and historical analysis in one visual analytics platform. Get the most from Apache Spark, Hadoop, Kudu, Solr, and more.
The sensors in connected cars can generate up to 130 terabytes of data per year per car. This includes data from electronic control units such as RPMs, speed, fuel efficiency, and temperatures, as well as location, safety, voice and video data. Some of these sources stream in real time, while others are regularly delivered in batches. With Arcadia Enterprise, you can visualize the real-streams and perform deep discovery on historical data seamlessly on the same modern business intelligence platform.
Turn IoT data from enterprise data servers into meaningful lifecycle analytics. Understand which components are failing, compare components across vendors, learn which features are being adopted and identify servers that are near capacity.