Teradata has announced the creation of IoT Analytics unit in the US, UK and India within its labs to focus on developing innovations and take value from Analytics of Things (AoT). The data analytics and marketing applications company has instated a special operations team consisting of data scientists, data engineers, and software designers, for building new, cloud based analytic solutions that will simplify advanced analytics, data movement, and database management of IoT.
Oliver Ratzesberger, President, Teradata Labs, says, “The smartest people at Teradata are laser focused on building the best technologies to power the Analytics of Things. 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.”
Already a part of the kitty
The company has a prebuilt analytic function called the Teradata Aster Analytics, which uses IoT data to find meaningful and relevant insights from volumes of IoT data. Its IoT data preparation capabilities and machine learning techniques that understand and detect patterns in machine behavior can be used to mitigate risk, reduce maintenance cost and downtime and increase productivity. It also allows analysts to deploy generated machine models in a virtual operation environment, be it IoT edge servers, public clouds, or in the data centers.
For collecting and distributing IoT Data Streams from Teradata Listener Enhancements, the company 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 but with the new connectivity, it is easy and fast for Listener to deliver new data streams of sensor data to theTeradata Unified Data Architecture, either on-premises and in the cloud.
Taking IoT analytics global
By applying machine learning to Teradata systems in order to solve complex performance and workload congestion problems in seconds, IoT Analytics unit is applying machine learning and advanced analytics techniques to system administration and DevOps tasks.
The AoT services serve early warning detection that uses predictive analytics to find and correct issues with machines and devices sooner, thus reducing repair and warranty costs while protecting brand reputation. It continuously monitors assets to enable new revenue opportunities and pricing strategies based on power-by-the-hour and pay-per-use models instead of purchases. And with real-time monitoring and analysis of physical assets, companies can understand and act upon a variety of real-time insights including security alerts, energy and fuel usage, idle time, faulty parts, geo-positioning and more.
According to a report on Internet of Things by Enterprise Management association, more than 70 per cent of IoT analytics ecosystems utilize data discovery platforms, analytic appliances, enterprise data warehouses and data marts. In comparison, today, these ecosystems are using relatively fewer Hadoop (13.2 per cent of environments) or NoSQL (13.6 per cent) data stores.