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IBM Exec Offers Inside Look at Vivisimo Big Data Acquisition
IBM Exec Offers Inside Look at Vivisimo Big Data Acquisition
By Jennifer LeClaire / CRM Daily Like this on Facebook Tweet this Link thison Linkedin Link this on Google Plus
PUBLISHED:
MAY
03
2012

IBM is acquiring Vivisimo in its latest Big Data move. What does that mean for your enterprise? Perhaps plenty.

Vivisimo is a federated discovery and navigation software that helps organizations access and analyze Big Data across the enterprise. Vivisimo software automates the discovery of data and helps employees navigate it with a single view across the enterprise, no matter what format it is or where it resides.

The goal is to provide valuable insights that drive better decision-making for solving operational challenges. We caught up with David Corrigan, director of strategy for IBM's Infosphere portfolio, to discuss the promise of Vivisimo, how it fits into IBM's portfolio and how it can help enterprise customers.

Q. How does the Vivisimo acquisition fit into IBM's portfolio?

A. It fits really well into our Big Data platform. Vivisimo is going to be incorporated into the Information Management brand and will be part of the Big Data portfolio. We see it as a critical capability because it enables federated discovery and navigation of Big Data sources -- unstructured and structured content.

Vivisimo adds a unique capability to the market. We can say: "We can understand your Big Data sources. We'll leave them exactly where they are and we'll figure out the value. You decide what you need to bring into the Big Data platform itself for deeper analysis. In other words, you can get an idea of the value and you can navigate through it before you bring it all into the Big Data platform. Of course, there's a separate set of use cases around navigation and search and visualization that enable end-user applications.

Q. So what does that mean practically speaking for clients? Can you offer us a couple of scenarios?

A. Big Data is emerging in a lot of organizations and they have to figure it out as they go. They might be doing groundbreaking new things. The ability to leave the data in place but to quickly discover and profile and get a better understanding of it is going to help a lot of organizations start to sell the concept of Big Data.

There's a whole set of cases where an end-user application requires navigating and searching through this Big Data content. It could be a customer service application where you have to search through different content around customers -- PDF docs, call log details, etc. -- and you're able to do that in a federated manner now with Vivisimo. As you search for some topics, it will dynamically suggest others. That's a huge boost from the point of view of developing these Big Data applications.

Q. How is this different from what your competition offers?

A. It's different in a number of ways. There are definitely different takes on Big Data and how it should be used. A lot of companies that have adopted Hadoop and believe in that open-source technology. Their message is essentially, "Listen, Hadoop is the cheapest and most cost-effective way of analyzing big volumes or variety of data, so dump everything in there and analyze it."

We're different because we're saying, "Hang on a second. It is a very cost-effective way to analyze a huge volume of data. But what if I could tell you by looking at your existing sources whether or not it was valuable and do discovery and what if I could analyze data in motion with streaming analytics and determine what needs to be persisted or just simply find insights themselves?" That makes IBM extremely unique.

We are very dedicated to adding analytics to Big Data technology. We don't see it as a simple pre-processing tool in order to get everything into a relational data warehouse and analyze it. Relational warehouses absolutely are part of our Big Data platform and they are very good at management and planning type scenarios. So deep analytics, forecasting, historical analysis -- you need a structured warehouse to do those things and it has to be able to handle Big Data volumes.

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