"Teradata everywhere is really about delivering agility and flexibility in our data analytics as our customers' businesses are evolving," said Ratzesberger.
"We feel that Teradata is implementing an 'everywhere' strategy, while our competitors are implementing an 'only' strategy. You can only have these features on premise, [or] you can have only these features in their rental cloud, and by the way it doesn't work very well in the public cloud. That's what our competitors are doing compared to Teradata Everywhere."
Teradata is marketing itself as the only data analytics company that offers all of its features in every deployment, from public cloud to Teradata hardware.
Customers can choose the data analysis they want and buy it in a bundle, subscription, and pay-as-you-go option. The Teradata Analytics Platform is embedded in every choice, and customers can migrate to a different deployment at any time, taking their licenses and applications with them.
AI at Teradata
To boost its ambitions to replace the multiple competing vendors, Teradata also announced a series of additional products. They include IntelliSphere, a software suite that combines ten Teradata tools under a single subscription-based licence.
"It's an easy flexible way to consume and build an analytical ecosystem in a very cost effective and scalable way," said Ratzesberger.
The final element of the package supports the growing interest in Artificial Intelligence (AI). It combines four new services developed to help large enterprises gain business value from AI and has been developed by Teradata subsidiary Think Big Analytics, a company focused on Hadoop and big data solutions.
These services include reviews and recommendations for AI uses aligned to business strategies and an AI analytics-as-a-service offering to manage AI models from development to handover to operations.
They're joined by two new accelerators designed to boost deployment: the Financial Crimes Accelerator to detect fraud in banking by using deep learning, and the AnalyticOps Accelerator to facilitate deep learning models at scale.
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