Teradata is launching four accelerators to transform IoT applications in manufacturing, transportation, mining, energy, and utilities.
Teradata has today announced four ‘Analytics of Things Accelerators’ (AoTAs), made up of technology-agnostic intellectual property and professional services, to help companies turn the huge amount of data gathered from Internet of Things (IoT) devices into insights that can be acted upon. Teradata’s so-called AoTAs will apply proven techniques and approaches to reduce the cost and risk of putting IoT projects in place, accelerate their time to value, and ultimately boost ROI.
Supported by Teradata’s four AoTAs, organizations will be in a better position to outline which sensor data to keep and act upon, and able to select different methods and combinations of analytical techniques to solve the specific challenges they face.
Related: Excuse me, where’s the Global IoT Analytics Unit please?
Teradata’s AoTAs are “transformational in scope”
Teradata is seeking a move beyond one-size-fits-all analytics projects to offer scalable solutions that can be applied to thousands of complex devices and assets.
“Teradata AoTAs are already addressing and resolving $100 million-dollar problems for manufacturers of vehicles, equipment, oil and gas systems, and consumer goods,” said Oliver Ratzesberger, Teradata’s executive vice president and chief product officer.
“These challenges represent billion-dollar budgets for each company, to be clear on the scale of business value addressed by AoTA. For example, our AoTAs have increased overall equipment effectiveness as much as 85 percent, while also improving predictability and asset availability. We are seeing a lot of excitement around our accelerators, because the return on investment is transformational in scope and compelling in business impact.”
Speaking to Internet of Business, Niall O’Doherty, business development director with Teradata’s emerging industries team, said that the scope of potential transformation across verticals was bigger than many people anticipate – although it might be time to change the way we speak about how “things” relate to each other.
“I believe that the this is just the tip of the iceberg when it comes to improvements and savings as a result of IoT. Perhaps the “Things” in IoT is too vague and misleading. After all isn’t everything a thing? These “things” are in fact systems and the value comes from understanding and optimising these systems.”
“Transformational change will come when the IoT embraces Systems Science or Systems Theory – the interdisciplinary field that studies the nature of systems — from simple to complex — in nature, society, and science itself.”
Industry partners to benefit from Teradata
Teradata’s AoTAs are a result of the company’s years of experience at the heart of IoT applications across major industries. In transport, for example, the company’s sensor-based analytics have been central to the success of North America’s Union Pacific Railroad and ambitious rail projects with Siemens in Spain.
O’Doherty pointed both to this track record and to the importance of harnessing sensor data in the right way.
“Teradata has always been able to deliver analytics at scale and, for me, this is the key to delivering the transformational change that IoT can deliver when companies start looking at optimising systems,” he said.
“Just capturing and storing sensor data at the volumes that are needed is not enough.”
“I believe that Teradata uniquely has the technology, people and experience to deliver the scale, integration and analytics required so our partners can build new operational capabilities on our IoT platform.”
Executive vice president Oliver Ratzesberger stated that many companies are unaware of Teradata’s history in consulting services, which goes back to the 1990s.
“Teradata consulting professionals are veterans at solving the biggest data problems in virtually every major industry, and experts in the use of IoT data, regardless of the customer’s analytic ecosystem,” he said.
The new Teradata AoTAs are made up of:
1) Condition-Based Maintenance Accelerator, which continuously monitors and analyses asset data from remote devices or equipment at scale to increase availability, improve safety, and reduce costs.
2) Manufacturing Performance Optimization Accelerator, which identifies complex production problems across equipment performance and availability for quick corrective action.
3) Sensor Data Qualification Accelerator, which automates recommendations on the optimal frequency of sensor readings based on relevant anomaly patterns.
4) Visual Anomaly Prospector Accelerator, which mines large amounts of multidimensional time series (MTS) data from remotely monitored equipment and devices, and visually helps an end user discover anomaly patterns that frequently precede a key event.