Success of Data Insight–Driven Enterprises in Digital Economy

This article originally appeared on IBM Data Magazine.

Connecting everything to the Internet—the Internet of Everything—brings an interesting problem to the forefront: data onslaught. Examples of data onslaught in the new digital economy includes the 2.5 quintillion bytes of new data collected every single day (and it is expected to increase three times by 2017), or the 2.5 PB of data collected by a major retailer every hour or the fact that by 2015, 1 trillion devices are expected to be connected to the Internet and generate data for consumption.

A key point that almost every organization seems to miss in the data economy is that just because they are collecting so much data doesn’t mean they are collecting the right data, or even enough data. They may be either collecting very little of something very important or not collecting the right data at all. Even more appalling are situations in which organizations collect huge amounts of data and do absolutely nothing with it. People often make the mistake of connecting value with voluminous data.

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Did Germany Cheat in World Cup 2014?

– By Andy Thurai (@AndyThurai)

This blog originally appeared on BigML blog site.

Now that I got your attention about Germany’s unfair advantage in the World Cup, I want to talk about how they used analytics to their advantage to win the World Cup—in a legal way.

player-performance

I know the first thing that comes to everyone’s mind talking about unfair advantage is either performance-enhancing drugs (baseball & cycling) or SpyCam (football, NFL kind). Being a Patriots fan, it hurts to even write about SpyCam, but there are ways a similar edge can be gained without recording the opposing coaches’ signals or play calling.

It looks like Germany did a similar thing, legally, and had a virtual 12th man on the field all the time. For those who don’t follow football (the soccer kind) closely, it is played with 11 players on the field.

So much has been spoken about Big Data, Analytics and Machine Learning from the technology standpoint. But the World Cup provided us all with an outstanding use case on the application of those technologies.

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I am an IBMer…

As I indicated earlier, I left Intel to pursue an outstanding opportunity in the same space. I know I kept this as a surprise while I went on vacation and didn’t write much, which led to some speculation on where I was going…so here it is. I am going back to IBM after being away for four years – a (sweet) homecoming of sorts :).

When I left Intel, I was seriously considering another opportunity, equally good. But I got to talk to some of my old pals at IBM and learned that they were looking for someone with my skills. Couldn’t hurt, I thought, talking to them. Well, now I am an IBMer!

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Not with Intel Any More…

You might have read my recent blog about Kin Lane. I didn’t realize that I would have to make a decision of my own when I wrote that blog. Though our situations were entirely different, it is always tough to call it right when you are faced with multiple choices, especially when all of them seem like the right answer. In any case, I have decided to move on from my position at Intel in pursuit of other opportunities.

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Prescriptive Analytics: Predict and Shape the Future

This article originally appeared on Gigaom

–  By Andy Thurai (@AndyThurai) and Atanu Basu (@atanubasu). Andy Thurai is the Chief Architect and CTO for Intel App Security unit. Atanu Basu is the CEO of Ayata.

Knowledge is power, according to Francis Bacon, but knowing how to use knowledge to create an improved future is even more powerful. The birth of a sophisticated Internet of Things has catapulted hybrid data collection, which mixes structured and unstructured data, to new heights.

Broken Analytics

According Gartner, 80% of data available has been collected within the past year. In addition, 80% of the world’s data today is unstructured. Using older analysis, security, and storage tools on this rich data set is not only painful, but will only produce laughable results.

Even now, most corporations use descriptive/diagnostic analytics. They use existing structured data and correlated events, but usually leave the newer, richer, bigger unstructured data untouched. The analyses are built on partial data and usually produce incomplete takeaways.

Smarter Analytics to the rescue

Gaining momentum is a newer type of analytics technology, called prescriptive analytics, which is about figuring out the future and shaping it using this hybrid data set. Prescriptive analytics is evolving to a stage where business managers – without the need for data scientists – can predict the future and make prescriptions to improve this predicted future.

Prescriptive analytics is working towards that “nirvana” of event prediction and a proposed set of desired actions that can help mitigate an unwanted situation before it happens. If a machine prescribes a solution anticipating a future issue and you ignore it, the machine can think forward and adapt automatically. It can realize there was no action taken and predict a different course of events based on the missed action and generate a different prescription that takes into account the new future.

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Which kind of Cyborg are you?

By Andy Thurai (@AndyThurai)

[This article is a result of my conversations with Chris Dancy (www.Chrisdancy.com) on this topic. The original version of this was published on Wired magazine @ http://www.wired.com/insights/2014/01/kind-cyborg/].

Machines are replacing humans in the thinking process. The field of Cognitive Thinking is a mixture of combining rich data collection (with wide array of sensors), machine learning, predictive analysis, and cognitive anticipation in a right mix. Machines can do “just-in-time-machine-learning” rather than using predictive models and are virtually model free.

The Cognitive Computing concept revolves around few combined concepts:

  1. Machines learn and interact naturally with people to extend what either humans or machines could do on their own.
  2. They help human experts make better decisions.
  3. These machines collect richer data sets and use them in their decision making process, which creates the need for intelligent interconnected devices. This creates a network of intelligent sensors feeding the super brain.
  4. Machine learning algorithms sense, predict, infer, think, analyze, and reason before they make decisions.

Which kind of cyborg are you?

The field of cybernetics has been around for a long time. Essentially, it is the science (or art) of the evolution of cyborgs.  The cyborgs have evolved from assistive cyborgs to creative cyborgs. Not only can they adapt to human situations, but they are also able to learn from human experiences (machine learning), think (cognitive thinking), and figure out (situation analysis) how to help us rather than being told.

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