Teradata (NYSE: TDC), the leading data and analytics company, today announced the Teradata Analytics Platform. This modern platform delivers access to the best functions and leading engines to enable users throughout the organization to leverage their preferred tools and languages, at scale, across multiple data types. With the Teradata Analytics Platform, this is accomplished by embedding analytics close to the data, eliminating the need to move data and allowing users to run their analytics against larger data sets with greater speed and frequency.
“In today’s environment many different users have many different analytic needs,” said Oliver Ratzesberger, Executive Vice President and Chief Product Officer at Teradata. “This dynamic causes a proliferation of tools and approaches that are both costly and siloed. We solve this dilemma with the unmatched versatility of the Teradata Analytics Platform, where we are incorporating a choice of analytic functions and engines, as well as an individual’s preferred tools and languages across data types. Combined with the industry’s best scalability, elasticity and performance, the Teradata Analytics Platform drives superior business insight for our customers.”
There are more analytic tools and techniques available than ever before, and business analysts want to use the best methods that are most appropriate for each of their projects. For example, predicting a parts failure that impacts consistent, reliable delivery of energy to businesses and residences requires analysis and monitoring of data across different sources, including weather data, utility control data, outage data, transformer and smart meter data, usage data, and asset information including location. Analysis of such disparate data can require a wide variety of tools and techniques, demanding that the analyst bounce between languages, data formats, user interfaces and completely different systems. Relevant data must also be integrated and moved across systems.
With the Teradata Analytics Platform, many of these steps disappear or can be combined. Much of the data in this example would be already integrated within a single ecosystem, which can also easily support raw data from other sources. Now 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.