Teradata announced Monday formation of its global IoT analytics unit within Teradata Labs, based in the United States, UK and India, while boosting get focus on developing innovations to derive the greatest value from the Analytics of Things (AoT). The special-ops team of data scientists, data engineers and software designers are tasked with building new, cloud-based analytic solutions and services to simplify advanced analytics, data movement and database management for the Internet of Things.
The IoT Analytics unit is further applying machine learning and advanced analytics techniques to system administration and DevOps tasks. They are applying machine learning to Teradata systems in order to solve complex performance and workload congestion problems in seconds.
Teradata’s AoT services deliver early warning detection that uses predictive analytics to find and correct issues with machines and devices sooner, reducing repair and warranty costs while protecting brand reputation. Its continuous monitoring of assets allow new revenue opportunities and pricing strategies based on power-by-the-hour and pay-per-use models instead of purchases. Real-time monitoring and analysis of physical assets, allowing companies to understand and act upon a variety of real-time insights including security alerts, energy and fuel usage, idle time, faulty parts and geo-positioning.
“Teradata has positioned itself well from a product and services perspective to power the Analytics of Things. More than 70 percent of IoT analytics ecosystems utilize data discovery platforms, analytic appliances, enterprise data warehouses and data marts,” said John L. Myers, Managing Research Director at Enterprise Management Associates, citing recent research on the Internet of Things the analyst firm conducted among 250 global technology and business leaders. “In comparison, today, these ecosystems are using relatively fewer Hadoop (13.2 percent of environments) or NoSQL (13.6 percent) data stores.”
“The smartest people at Teradata are laser focused on building the best technologies to power the Analytics of Things,” said Oliver Ratzesberger, president, Teradata Labs. “With this announcement, we are making it easier for our customers to move sensor data around, optimize data management systems to deal with the massive volumes of data, and run real-time, advanced analytics against streams of IoT data. We’re giving our customers powerful tools and technologies to analyze IoT data for new insights, applications and use cases.”
Teradata Aster Analytics answers the “why did this happen” question using IoT data. The pre-built analytic functions include new IoT data preparation capabilities and machine learning techniques to quickly understand and detect patterns in machine behavior. This can be used to mitigate risk, reduce maintenance cost and downtime and increase productivity.
Aster Analytics makes it easier and faster to find meaningful and relevant insights hidden in massive volumes of IoT data with millisecond performance. With Aster Analytics, Graph, R and Mapreduce are combined on one framework. With all functions executed as SQL commands and with all analytics engines doing what they do best, Aster Analytics provides high performance processing across massive volumes of data.
In addition to Aster Analytics optimized performance, the Teradata Aster Seamless Network Analytics Processing (SNAP) Framework allows Graph, R and MapReduce engines to operate seamlessly while the Massive Parallel Processing (MPP) architecture allows analytic processing to be done at the scale needed.
The Aster Analytics web-based Apps Center solution provides an easy-to-use interface for building, deploying, sharing and consuming data within the Aster Analytics tool. It enables self-service big data and advanced analytics as well as the ability for technical and non-technical users to discover business and customer insights as well as opportunities to gain an edge over competition.
Several of the machine learning models generated can be ported to run on virtually any operational environment that can run Java. The Teradata Aster Scoring SDK (software developer’s kit) allows analysts to deploy Aster IoT analytic models into virtually any IoT edge servers, public clouds, and in the data center.
Teradata is extending the IoT capabilities of Teradata Listener with connectors that make it easier to acquire and distribute streaming sensor data for analysis. Capturing and managing continuous streams of data is normally complex and labor intensive. These new connectivity options make it easy and fast for Listener to deliver new data streams of sensor data to the Teradata Unified Data Architecture, either on-premises and in the cloud.