Enterprise applications power the heart of business productivity, but they are traditionally difficult to implement, upgrade, and innovate. We look at how the next generation of enterprise apps could change the game. Pronouncements of moving to the cloud would have been unthinkable just a few years ago, but Teradata, like many vendors, is adapting to a changed technology landscape. After a series of disappointing quarters, Teradata's board ousted CEO Michael Koehler in March and tapped board member Victor Lund to replace him. Introduced at the Summit, Lund was frank about Teradata's past and its future.
Earlier this month, Teradata unveiled a new marketing campaign to reposition the company to go beyond IT in addressing the business. The company, which formerly positioned itself with variants of the theme of the world's biggest, fastest data warehouse is now positioning itself with a stop the insanity-style tagline, "Stop buying analytics. It's time to invest in answers." Have we got your attention yet? It's not surprising that rebranding and changing the message was the highlight of the company's annual customer event last week, Teradata Analytics Universe, which itself is a rebranding from the event formerly known as Partners.
Teradata, which practically invented the enterprise data warehouse, has always been a company where engineering came up front. Company veterans and longtime customers knew Teradata products by their model numbers; a 6800 told you that you were using a giant enterprise data warehouse, as opposed to a 2800 that was sized for departments. We delve into where IoT will have the biggest impact and what it means for the future of big data analytics. As longtime data warehouse provider, Teradata was the go-to box for complex SQL analytic queries across large bodies of data. So it's not surprising that in recent years, emergence of commodity hardware and open source infrastructure posed an existential threat.
At last week's Teradata Partners Conference in Atlanta the company hit several important cloud milestones with its "Teradata Everywhere" and "Borderless Analytics" announcements. And in another sign that it's evolving, Teradata also announced a range of analytic solutions supported by consulting services. Teradata Everywhere is the ability to run the same database and workloads without alteration in multiple deployment environments. The choices include on-premises systems, VMWare-based private-cloud instances, Teradata's Managed Cloud services and Teradata Database on public clouds including Amazon Web Services and, by year end, Microsoft Azure. The newest options here are Teradata on VMware and parallel processing support on Amazon Web Services.
Teradata, which told us last week to "stop buying analytics," used its annual user conference this week to elaborate on that curious statement and explain its radical plan to dramatically simplify its customers' analytics investments through massive consolidation of its competitive offerings under its new Vantage data platform. To hear Teradata COO Oliver Ratzesberger explain it, top executives at Fortune 500 firms -- and the boards that hold their purse strings -- are simply fed up with big analytic investments that haven't panned out, and they're turning to Teradata for answers. "The last five to 10 years have been a curse and a blessing," Ratzesberger tells Datanami in an interview here at Teradata Analytics Universe in Las Vegas, Nevada, where approximately 3,000 Teradata customers, partners, and employees gathered for four-and-a-half days of training, education, and commiserating about failed analytic projects. "There are few executives left who don't say'I've spent billions of dollars. I have Vertica there, Hana there, Greenplum there. We bought a couple instances of Netezza. But IBM just de-released Netezza, Vertica just got sold a second time, Greenplum is now this open source thing. And Hadoop – well, that is going away.' "They're literally coming to us and saying'Give us a proposal to clean up the dozens of instances and consolidate them into one,'" Ratzesberger continues. "And what they quickly figure out is, if you can run it with a handful of systems, TCO [total cost of ownership] is orders of magnitude different, because the TCO in most organizations include 2,000 headcount to run all of these technologies, and 2,000 headcount is a lot of money." Ratzesberger says he recently spoke with the head of risk at one of the largest banks of the world who runs 800 separate systems designed to measure risk. All told, the various Python, R, Spark, and Hadoop systems cost the company $2 billion per year. "None of these other solutions are scalable to that regard.