Table of contents for IBM Insight 2015
I am attending IBM Insight 2015 and will blog about a few of the sessions. First up, the opening keynote on the insight economy. IBM is focusing on the disruption caused by analytics and insight across all industries. Internal and external data and increasingly sophisticated analytics are changing how companies interact with customers, manage risk and more.
Bob Picciano, who heads up IBM Analytics, kicked things off. IT value, he says, is increasingly focused on how it can generate value from the data it is managing – unlocking the insight in the data to drive what IBM calls the Insight Economy. Of course the data itself is also changing, with more unstructured data and more device data, while cloud is changing how this data can be managed. You want, he says, to inject this insight into every decision in every business process.
IBM’s latest positioning of course is Cognitive – encouraging its customers to become Cognitive businesses that learn and adapt. There’s a little bit of a distinction between Cognitive Computing (Watson) and a Cognitive Business (which might also use other things). This focus on analytics and Cognitive is driving a huge business and rapid growth for IBM.
The Internet of Things (IoT) he says is generating a huge amount of data that is mostly just dropped, not stored or analyzed at any point. New technologies however mean that this data can be put to work more effectively. This focus on IoT brought the first customer on stage, Whirlpool. 3 out of 4 homes in the US have at least one of their products and they have a huge range of product lines. Whirlpool gave some simple examples like machines that detect a buildup of lint that is making a dryer less efficient, suggest maintenance that a machine needs etc.
Next up was a discussion of the role of twitter data. Coke discussed how they use twitter data to listen to the sentiment around their brand and how they hope to use twitter data in a more analytic way to personalize interactions. Some good examples of overall market sentiment and brand discussion get reflected in twitter conversations though still just plans to apply twitter in personalized marketing.
Mike Rodin was up next to start the discussion of Watson and Cognitive computing. He began by making the point that Cognitive systems are particularly good at unlocking data that is “dark” to traditional systems like unstructured documents, sound, images etc. Watson, he says, has been evolving rapidly over the last few years. There are now over 30 Watson APIs including NLP, Neural Networks and many other machine learning techniques. Cognitive systems he says have some differences: They understand natural language or natural image inputs, they reason against this understanding and they learn continuously. It’s not clear how these Watson components interact with their other reasoning and analytic technologies but there’s clearly a lot of investment in the Watson platform and ecosystem.
GoMoment came on stage to talk about their use of Watson. The key role it seemed to play was to be able to read a text and apply context – in this case being in a hotel and a specific location and time – to give an immediate response. The ability to interpret the texts allows for a fundamentally different interaction than a more structured app. This increases engagement and allows for more rapid response to problems. It’s a pity he did not talk more about how the application was developed – the balance between coding and learning for instance. Another Watson app, VineSleuth, was up next. This is designed to analyze wine and then personalize recommendations wine4.me. As you try wine and review it the recommendations change. Recently Watson was added to provide natural language and speech to text so that a kiosk would be developed that would take a verbal request and turn it into a recommendation. This time it’s clearer that the analytics and expert rules underpin the recommendations while Watson makes it easier to interact with these analytics in a natural way.
More of a focus next on social data with StatSocial talking about using Watson with social profile data to create a rich profile for consumers – personality traits derived from your social interactions. This allows retailers to customize their direct mail and other interactions.
New research is focused on helping Watson interpret images – giving it the ability spot abnormalities and variations in MRIs and other images. This allows it to identify critical images out of a set, compare those with historical images to find similar ones and then present summaries of the diagnoses resulting from those other images. Watson, as always, presents its reasoning and the strength of its suggestion. The ability to include image data in this is potentially huge, of course.
Then of course we had to have a robot, Pepper from Softbank. It’s not clear how much of this is programmed or scripted. Very cute though.
Enough Watson apparently, back to Mike and the Weather Company. The Weather Company collects a ton of data about weather and works with IBM to provide this data and analysis/decisions based on it to companies. The focus on apps and localized weather prediction has driven a massive increase in the number of forecasts and the number of locations for which predictions are required. In parallel the company is focusing on decisions that companies make where weather should be part of that decision-making whether that’s shopping or routing for instance. Helping organizations like the Red Cross deploy assets in a more focused, more accurate way. Precision and timing are critical plus the ability to find people who are out and about and target them with specific instructions. Mike wrapped up by announcing IBM’s new Insight Cloud Services for delivering various IBM analytics and insight (including the work with The Weather Company and Twitter) through the cloud for embedding into mobile apps.
Box came on stage next to talk about integrating unstructured content stored in box into the IBM analytic and content management ecosystem. All seems pretty straightforward in terms of value proposition – makes perfect sense but not much to say about it yet.
Finally a recap of Watson Analytics and its ability to empower “citizen analysts”. A demo of the Watson Analytics user interface followed with its nice visual interactive style. Still no way to deploy the predictive models you can build though. And of course there’s still the question of when it make sense to empower citizen analysts – check out our Analytic landscape research bit.ly/1NwdJGU for a point of view.
Lots of interesting stuff here, though the cloud services availability was a critical announcement that got buried in the Watson hype. Still not clear how Watson and the rest of the IBM analytic stack should interact but hopefully this will become clearer.