Weather predictions, APIs, IoT, and a powerful digital platform for your uninterrupted Business

This article was originally published on IBM Big Data Hub.

Many of us seem to watch weather forecasts to figure out what to wear the next day but forget about it right after that, unless of course there is snow in the forecast. Especially here in the northeast; we dread watching the weather report for about six months of the year.

For this reason, IBM’s acquisition of The Weather Company was a head-scratching moment for many because we are used to only the consumer aspect of weather, not the business side—especially given the high speculation by The Wall Street Journal.

Why did IBM, an IT software company, go after The Weather Company then? IBM started this fundamental shift a few years ago, transforming itself from a big IT and mainframe provider to a digital, data and insight company. Recent speeches by the CEO of IBM clearly articulate its main focus has shifted toward cognitive computing, analytics, IoT, APIs, hybrid cloud and digital platforms that support big corporations to reinvent themselves and engage in the digital economy.

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Want to practice capitalism?

Capital 2015-09-24_13-50-47

As with most modernized economies, the United States economy utilizes capitalist principles. It is only fitting that we invented a technological solution that will help companies engage in c-api-talism using APIs in a more efficient manner.

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Digitizing Healthcare, Because Our Lives Matter

This article originally appeared on IBM Big Data & Analytics Hub.

The United States spends around 17-18% of its GDP on healthcare every year. When you put this into dollar numbers, it is a mind-boggling $2.9 trillion.

Unfortunately, that spending will grow at a faster rate now due to baby boomers becoming an aging population, and they are the largest demographic in the U.S. (Baby boomers are about 76 million, which accounts for 25% of the population of the U.S.). The healthcare related spending is expected to grow at a faster pace than the under 5% annual rate it grew over the last decade.

Unless the U.S. gets this spiraling healthcare spending under control, in a few short years we will be spending almost 25% of our entire GDP in healthcare trying to fix people’s failing health, instead of spending it somewhere else where it is desperately needed. Obviously, we can’t stop the aging population, but we can make the healthcare system more efficient. Overall, chronic diseases account for about 86% of the health care spending in USA. Severe chronic conditions such as heart disease, arthritis, asthma and diabetes alone cost 33% of the total spending.

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The “Ugly Duckling” API syndrome

Ugly Duckling lookingAs an advisor and a digital strategist, part of my role is to talk to customers about their APIs, IoTs, and their overall digital strategy. After conversations with a lot of them, one thing stands out: Almost everyone thinks their API is awesome. While a good portion of them are very impressive, and some are moderate to good, I do see a bunch of ugly ones too. However, if you are a producer — whether it is a movie, a baby, or an API – it will be hard for you to realize, admit, and come to terms with your “Ugly Ducklings.” Obviously, the amount of sweat and tears that went into your initiative causes you to look at your APIs through beer goggles, which makes them look good in your eyes, even if the APIs are mediocre.

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Bringing your ideas to life in digital economy

Bringing your life-changing ideas to fruition needs a different mindset (and toolset) in the digital economy. The need for speed with digital innovation is more important than ever, with every start-up trying to push the envelope with their new ideas.

Remember the good old days, when you had an idea and tried to prove that concept by doing a POC (Proof of Concept)? While the POC technically stands for proving a concept, most times it is done as a proof of technology. We try to figure out a way to incorporate this newer idea into an existing IT infrastructure, often failing, and trying to make it work. I still remember the days in which we used to wait for the security admins to open the appropriate ports and give us the right access before we could even start to “install” our software to try things out. Those days are gone!

In the digital economy, once you get an idea, you cannot afford to sit around and build it for years, not even months, like legacy enterprise software.

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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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Going beyond the mobile app gold rush

Recently, I wrote a blog on What powers the mobile economy?which created lot of interesting conversations. A few large enterprise customers reached out to me and suggested they can relate to things I said in my post. In my follow-up conversations with them, a couple of more interesting views came up.

Sadhu-baba-with-mobile-funny

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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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What the Frack?

I was doing some research recently for an article in ONG (Oil & Natural Gas) sector practice that is making huge headlines recently called “fracking”.

For those who ask What the Frack?

Fracking, or Frack, (or hydraulic fracturing – oh my, how much we love shortening things into cute names) is a procedure in which essentially you are fracturing (or cracking) things with hydraulics hoping to find oil or gas. Essentially this gives us an opportunity to do horizontal drilling which was otherwise impossible.

Conventional places are running dry so we need to find new sources – oil out of sand, gas out of rocks. We are becoming God by performing these miracles!

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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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