Teradata today announced the Teradata Analytics Platform, allowing users to run their analytics against larger data sets with better speed and frequency.
Oliver Ratzesberger, chief product officer at Teradata, said: “We are incorporating analytic functions and engines, as well as an individual’s preferred tools and languages across data types for superior business insight for our customers.”
Within a single workflow, users are able to switch between the most common interfaces and tools, including commercial and open source. The result is better, faster and more precise insights based on all data, rather than a subset.
Teradata Analytics Platform comes with analytic functions, leading analytic engines, analytic tools and languages.
Teradata Analytics Platform will integrate Teradata and Aster technology, allowing data scientists to execute a variety of techniques to prepare and analyze data within a single workflow, at speed and at scale.
In the future, the Teradata Analytics Platform will include leading engines such as Spark, TensorFlow, Gluon and Theano to provide quick and easy access to a full range of algorithms, including those for artificial intelligence and deep learning.
Leveraging these engines, the Teradata Analytics Platform provides scalable analytic functions, such as attribution, path analytics, time series, and a range of statistical, text and machine learning algorithms.
Meanwhile, Teradata announced Teradata IntelliSphere software portfolio that unlocks a wealth of key capabilities for enterprises to leverage all the core software required to ingest, access, deploy and manage a flexible analytical ecosystem.
Danske Bank, a financial services leader in the Nordics, has worked with Think Big Analytics, a Teradata company, to launch AI-driven fraud detection platform that is already expected to meet 100 percent ROI in its first year of production.
The engine uses machine leaning to analyze tens of thousands of latent features, scoring millions of online banking transactions in real-time to provide actionable insight regarding true, and false, fraudulent activity. By significantly reducing the cost of investigating false-positives, Danske Bank increases its overall efficiency and is now poised for substantial savings.
Teradata has introduced Agile Analytics Factory that can help clients overcome the obstacles they face on their analytics journey, including the inability to operationalize analytics and data science use cases, address data governance issues or create repeatable analytic processes.
Teradata’s Agile Analytics Factory provides data science skills and analytics expertise as a service to deliver systemic and repeatable analytic capabilities. The program also applies data management practices, employs agile delivery methods, and works across technology and business functions to reduce costs and complexity in data and analytic environments.
Bankia, a financial institution in Spain, used Teradata to execute a multichannel transformation strategy.
Teradata agile analytics services include an operational data science initiative to improve customer experience, increase multichannel sales, and create an Operations Excellence Center. Leveraging AI, the Center facilitates industrialization and automation of analytic processes. Bankia is working to operationalize 200 models, with 3000 variables daily, through improved resource utilization and productivity.
Teradata is helping clients capitalize on the power of AI to deliver high value business outcomes in the areas of fraud detection, manufacturing performance optimization, risk modelling, and precision recommendation engines.