With the rise of Big Data analytics and significant improvements in high-performance computing, it is likely that more knowledge-worker jobs will get displaced. Industry and academia are finding new ways of mining data and performing complex tasks previously done only by humans. IBM's Watson’s adroitness at Jeopardy may precede, by perhaps only a few years, general-purpose computing’s ability in diagnosing illnesses and processing complex business problems. Advanced image-processing capabilities can be applied to adding metadata to video files.

With the rise of Big Data analytics and significant improvements in high-performance computing, it is likely that more knowledge-worker jobs will get displaced. Industry and academia are finding new ways of mining data and performing complex tasks previously done only by humans.

Your smartphone dings to signal the arrival of an email. Upon seeing its title, "Build #452 failed," your stress level doubles. You approach Rae, a talented developer, and say, in a slightly ticked-off tone, "I notice the build broke."

The idea of having a standard for describing architectures was first debated in the 1990s.

Just a couple of years ago, we were witnessing -- and some of us were deploring -- the "irrational exuberance" as well as the uncontrolled fears of many customers and decision makers about cloud computing.

A paradox increasingly confronts a number of researchers and analysts: to derive value from data, you need a lot of it. We now have the tools to analyze these large amounts, but few institutions, at least in the private sector, are willing to make it available to others.

If you are a CIO, project manager, or chief architect involved in or contemplating a very large system replacement, you owe it to yourself to read Jerome (Jack) Israel's article in IEEE Computer entitled, " Why Can't the FBI Build a Case Management System?" It is a

For those not familiar with the structure of subways, the third rail is the one that carries the power for the trains. Under no circumstances do you ever want to touch it.

There is often much confusion between processes, value streams, and capabilities. While each is intended to describe business operations in some way, each has a different purpose, focus, and use.

Big Data describes the evolutionary results of digitization, storage growth, and processing capability. It encompasses the growth of data in volume, variety, and velocity resulting from the increasing amounts of digitized material and data generated on the Internet.

As more traditional enterprises start to move their Hadoop projects into production, they are confronting the big question: How do we ensure data security and compliance in Big Data environments like Hadoop?

While recently raking together a large pile of freshly fallen leaves from the London Plane trees in my yard, it occurred to me that they had a strong connection to the ongoing pitcher risk-reward controversy involving the Washington Nationals baseball team.

I have heard so many CIOs say they just want to be able to walk into the offices of their internal customers -- business-unit VPs and other executives -- and show them exactly the real, fully loaded costs of the systems they are using and the financial consequences of some of the demands they push on IT without full knowledge of costs.

My "baptism" in programming was based on a discussion scratched on the back of my user's used-up cigarette case.

There have been significant advances in process modeling tools over the past decade. But that doesn’t necessarily mean that companies are getting good value from the time and effort they have invested in process modeling.

In my last Advisor (“ Big Data Analytics in a Socially Infused Healthcare Enterprise”), I shared an account of leveraging Big Data analytics in a large healthcare IT organization.

A process, in my honest opinion, is the product of its era. It reflects the needs, the constraints, and the predicaments of its time. As those change, the process needs to evolve in tandem. If it does not, it is likely to become obsolete.

As we all know, Abraham Lincoln was largely self-taught in the midst of meager means and living on the frontiers in the US states of Kentucky, Indiana, and Illinois -- far from centers of learning and culture east of the Appalachian Mountains. For Lincoln, the book represented the path, and he sought them with great effort.

A process, in my honest opinion, is the product of its era. It reflects the needs, the constraints, and the predicaments of its time. As those change, the process needs to evolve in tandem. If it does not, it is likely to become obsolete.

Many of my long-time readers might remember that I live out in the woods in the US state of New Hampshire. Just recently, my 20-year-old garden tractor began to die. In looking at a replacement, I started to go over the options.

[From the Editor: This week's Cutter IT Advisor is from Cutter Senior Consultant Mitchell Ummel's introduction to the August 2012 issue of Cutter IT Journal, "Cloud Computing Standards" (Vol. 25, No. 8).

Back in January, when discussing trends for predictive analytics in 2012, I predicted that some of the issues holding organizations back from realizing their predictive analytics dreams would be offset by new options for implementation (see "The Year Ahead: Will 2012 Be a Breakout Year for Predictive Analyt

There's a critical moment in enterprise governance that ties to when we start taking decisive executive action. I like to think of it as the "lifeboat" moment. It's when we determine that the challenges we face are too great to overcome and that we need to start salvaging what we can before the entire organizational "ship" goes under.

I'm conflicted about this topic, so it's a good thing Cutter promotes debate. I'm having one with myself, and you can watch.

The 2012 Summer Olympics, officially "The Games of the XXX Olympiad" (I don't want the IOC branding police after me), have finally concluded, but the buzz about NBC's coverage in the US still goes on.