"They wanted to increase the profitability of that route," Meley says. "But business travelers preferred to fly because there was a greater likelihood they would get to their meetings on time."
With Teradata's help, Siemens was able to leverage sensor data to optimize preventative maintenance for the trains. The effort was so effective at making the route reliable that Renfe was able to give travelers a guarantee that if their train was delayed by more than 15 minutes, it would pay back the whole ticket.
"They were able to capture a huge chunk of the segment that was originally flying as a result of this super-reliable route," Meley says.
Of course, the opportunity doesn't lie with just trains. Manufacturing equipment, oil and gas pipelines, truck fleets, even server farms and retail floors represent massive opportunities. PwC forecasts the digital universe will consist of 21 billion connected "things" by 2020. Data volumes generated by the IoT already dwarf the data created by social media, and that machine data will continue to grow. Teradata says companies are going to need guidance to generate business value from the enormous volumes of IoT sensor data to come.
That's the role the company sees for itself, Meley says. Its new Analytics of Things Accelerators (AoTA) are derived from its IoT field engagements in manufacturing, transportation, mining, energy and utilities. Meley says the new AoTA will help organizations determine what sensor data to trust and keep, while choosing types and combinations of analytical techniques to best address specific business questions. The idea is to move away from "brute force" one-off projects to enterprise-class solutions that can scale across thousands of complex devices and assets, resulting in continuous positive business impact.
The new Teradata AoTAs are the following:
- Condition-Based Maintenance Accelerator. This AoTA are for use cases like Union Pacific's. It's for continuously monitoring and analyzing asset data at scale to increase availability, improve safety and reduce costs.
- Manufacturing Performance Optimization Accelerator. This AoTA is intended to identify complex production problems across equipment performance and availability for quick corrective action.
- Sensor Data Qualification Accelerator. This AoTA addresses a key bugbear of IoT analytics: It automates recommendations on the optimal frequency of sensor readings based on relevant anomaly patterns.
- Visual Anomaly Prospector Accelerator. This AoTA mines large amounts of multidimensional time series (MTS) data to visually help an end user discover anomaly patterns that frequently precede a key event.
"Teradata AoTAs are already addressing and resolving $100 million problems for premier producers of vehicles, equipment, oil and gas systems and consumer goods," Oliver Ratzesberger, executive vice president and chief product officer at Teradata, said in a statement Wednesday. "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."
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