How does IBM monetize big data and how do you see that evolving?
Today we are primarily a supplier of big data insight and analytic capabilities to clients we serve, to allow them to build their own solutions around what we’ve categorized as six entry points of big data solution areas. Those entry points are: Creating new business models, customer insight, improving IT economics, optimizing operations and reducing fraud, managing risk, and transforming financial processes. That’s where clients are using the majority of these big data insight information capabilities today.
The most interesting one is helping them create new business models where they’re monetizing information they may have had on hand for a long time, but they didn’t know to correlate that with other things that may be happening in a social domain or that could be combined or correlated with other information that they possess.
That’s a today statement in terms of how we help our clients utilize those capabilities. But when we talk about capabilities like Watson we really are talking about delivering those insights as a service to our clients. In that type of a model, being able to curate the information necessary to help our clients get the highest quality insight is growing in terms of its relevance. Whether it’s engagement advice or whether it’s helping clinicians identify probable causes or possible solution paths, those are things we are selling back as a cloud-based service over time.
So I think we’re in one of those periods where we’re seeing some of the approaches transition from traditional Software as a Service and traditional on-prem models to really pioneering some information-as-a-Service models.
So you are early in the game for this Information-as-a-Service play?
In the latter phase, we are. And it comes through a couple of different, interesting paths. Certainly through insights like our Kenexa solution. Kenexa has a really relevant corpus of information that it has gathered over many years of behavioral science and survey science. And now, just at the beginning of this year, Craig Hayman [IBM general manager of Industry Cloud Solutions] announced that we are applying big data techniques to what Kenexa delivers. It always delivered Software as a Service, but now we can go much deeper in the insights that we can unleash to the chief human resources officers of enterprises we want to serve.
The same is true around our capabilities in security with what we do to deliver our QRadar solutions in our security portfolio. There are also examples of this in Coremetrics in Smarter Commerce. So Watson isn’t the only one, but I think over the course of the last two years we’ve applied many more big data techniques to these other more vertical solutions to give a big data advantage to the data sets that they’ve always been on top of.
Where would you say IBM stands competitively in the world of big data and analytics?
I think we’re No. 1. I think if you look at it by a revenue metric, if you look at it by who is recognized as the leaders in this space measured by people like Wikibon or if you look at how many favorable mentions are delivered into the marketplace, we would be No. 1 by any of those measures.
Our big data capabilities have been growing at very high rates. These are smaller businesses compared to some of our well-established data management portfolios, but these are new and growing extremely fast. I think 2013 was an important year of establishing real relevance and a strong set of customer outcomes around our big data portfolio. I would say it’s in part because of our ability to embrace Hadoop as we have and created a great offering around Big Insights, which is our enterprise Hadoop distribution.
We’ve also unleashed ways for our clients to engage with IBM to just learn about this space–through things like the Big Data University, or our Big Data Stampede engagement offering that allows clients to experiment with the technology or techniques and learn about how it can be applied to any of these entry point areas to deliver an enterprise benefit for their organizations.
I would also say that capabilities like our Data Explorer have also ensured that our clients can drive really quick wins. Not everything is established with a Hadoop distribution that takes a long time to build. Things can be done very quickly to allow us to help our clients navigate to where that relevant information is and how to apply it in context to other information for problem solving. These are really important in customer service examples and areas where data is scattered around the enterprise.