May 30, 2012
We all want to implement smarter analytics - to turn information into insights that we can use to improve both our personal and professional lives. Yet we are often faced with a fundamental, often nagging, problem of getting the right data for whatever analytics we are trying to implement.
May 29, 2012
Optimality is the new nirvana. The promise of "next best action" is that, somehow, we can program the optimal automated response into every business scenario. Of course, this dream presupposes that someone in your organization can specify the optimal response for any scenario that your personnel are likely to confront.
May 29, 2012
Here are the quick-hit ponderings that I posted on the IBM Netezza Facebook page this past week. I started the week in a sentimental mood, then developed my 2020 vision, then tried my best to cram it all in memory, then into the palm of my hand, and then finally crammed far more recommendation engine down my mental maw than a mortal human should be expected to chew on:
May 23, 2012
Here are the quick-hit ponderings that I posted on the IBM Netezza Facebook page this past week. Clearly, I was focused on the "big" side of big data, and on the "statistics" DNA of the analytics that power big data, and on the limits of what you can in fact "optimize" with big data and analytics:
May 21, 2012
Game-changing analytics applications don't spring spontaneously from bare earth. You must plant the seeds through continuing investments in applied data science and, of course, in the big data analytics platforms and tools that bring it all to fruition.
May 15, 2012
If there’s more and more data arriving and time isn’t expandingi, then data must be arriving at greater and greater velocity. In my last post I talked about Variety in the Volume, Variety, Velocity triumvirate. There’s more to be said about that, but first I’d like to take a run at Velocity. We’ve got used to the idea that you load stuff into a database (or other data store) then you take a look at it. That’s just too slow for lots of operational decision making processes. And if you think about it, as the volume of data available increases the bar is constantly rising on real-time analysis. But for many kinds of decisions, you just need the data that comes with the event you want to decide about: is this a fraudulent transaction? Was this call dropped?
May 14, 2012
Multiple sclerosis (MS) is a chronic neurologic disorder that afflicts many in the primes of their lives. The biomedical research community has ramped up its use of big data analytics to illuminate the myriad factors that contribute to the onset and progression of MS. On April 26, IBM announced that the State University of New York (SUNY) Buffalo is using tools from our Netezza portfolio and from our big data analytics business partner, Revolution Analytics, for their ongoing MS research initiative. We have recently published blogs on the effort, by Steve Hamm, Mike Kearney, and yours truly.
May 14, 2012
The human condition is an unfathomable mystery, a complex stew of biological, genetic, behavioral, cultural, environmental, psychological, and spiritual factors. But fathom it we must. When our personal condition stumbles from wellness to illness, we will use any resources at our disposal, especially the full repertoire of modern healthcare, to restore it. When our health issue is congenital, we'll use explore all options that might help us live as close to a normal life as we can. At the very least, we want all the facts and diagnostic tools that might help us understand what's ailing us and what, if anything, we can do about it.
May 9, 2012
Further to news of SUNY’s exploration of big data to understand possible causes of multiple sclerosis, I spoke with David Smith, VP of Marketing at Revolution Analytics, for a briefing on some advantages of R for analysis of large data sets.
May 8, 2012
If this was a start-up, that would be good for at least $100M... Analytics. Big Data. At a recent conference I attended, one of the keynote speakers stated that start-ups with “Analytics” in their business description are getting about two times the average valuation by the venture capital community, but those that combine “Analytics” and “Big Data” are getting about ten times the valuation. Netezza is no longer a start-up - we at Netezza have been helping customers with analytics and big data since our beginnings over ten years ago. And then there was that little matter of our acquisition by IBM, itself at a pretty healthy valuation. There isn’t really anything new about big data but the name. Companies have had to deal with larger amounts of data, more types of data, and faster generated or changing data since data has existed. Now because the term has gone viral, all the data management vendors are trying to wedge it into every press release and all their social media posts to catch the search engines. (Vendors in other segments seem to be looking for ways to get in on that game. Maybe we’ll see Kellogg’s “Big Data Crunch” on our supermarket shelves soon.)