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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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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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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Enterprise IOT: Mixed Model Architecture

– By Andy Thurai (@andythurai)

This article was originally published on VentureBeat.

Recently, there has been a lot of debate about how IoT (Internet of Things) affects your architecture, security model and your corporate liability issues. Many companies seem to think they can solve these problems by centralizing the solution, and thus collectively enforcing it in the hub, moving as far away from the data collection centers (not to be confused with data centers). There is also a lot of talk about hub-and-spoke model winning this battle. Recently, Sanjay Sarma of MIT, a pioneer in the IoT space, spoke on this very topic at MassTLC (where I was fortunate enough to present as well). But based on what I am seeing in the field, based on how the actual implementations work, I disagree with this one size fits all notion.

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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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Are you a “data liberal” or a “data conservative”?

– By Andy Thurai (@AndyThurai). This article was originally published on Xively blog site.

In the last decade, as a society, we had worked very hard toward “liberating our data” — unshackling it from the plethora of constraints unnecessarily imposed by I.T. In contrast to this, In the 90s and early 00s, data had been kept in the Stygian depths of the data warehouse, where only an elite few had access to, or had knowledge about it or the characteristics defining it.

Once we had the epiphany that we could glean amazing insights from data, even with our “junk” data, our efforts quickly refocused around working hard to expose data in every possible way. We exposed data at the bare bones level using the data APIs, or at a value added data platforms level, or even as industry based solutions platforms.

Thus far, we have spent a lot of time analyzing, finding patterns, or in other words, innovating, with a set of data that had been already collected. I see, however, many companies taking things to the next proverbial level.

In order to innovate, we must evolve to collect what matters to us the most as opposed to resign to just using what has been given to us. In other words, in order to invent, you need to start with an innovative data collection model. What this means is for us to move with speed and collect the specific data that will add value not only for us, but for our customers in a meaningful way.

Read more of this blog on Xively blog site.

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