Back in the late 90's, there were a number of computer scienctists claiming to know java in hopes of landing a job for $80k+/year.  In fact, I know a woman you did just that:  land a project management position with a large telecom and have no experience whatsoever.  I guess the company figured that some talent was better than no talent and that, with some time and training, she would be productive.  Like all gravey train stories, that one, too, had an end.  After only a year, she was given a pink slip.

Not only are those days over, job prospects for the IT professional have become considerably more demanding.  Saying you know java today is like saying you know that you have expertise with the computer mouse; that's nice, but what else can you do.   This demand can be attributed to an increase in global competition along with the introduction of a number of varied technologies.   Take .NET, Python, Ruby, Spring, Hibernate ... as an example;  most of them, along with many others, are the backbone of the IT infrastructure of most mid-to-large scale US corporations.  Imagine the difficulty in finding the right mix of experience, knowledge and talent to support, maintain and devlop with such desparate technologies.

Well imagine no more.  According to the IT Hiring Index and Skills Report, seventy percent of CIO’s said it's challenging to find skilled professionals today.  If we add the rapid rate of technological innovation into the mix of factors affecting more businesses now than ever before, it’s understandable that the skill gap is widening.  Consider this as well:  the economic downturn has forced many potential retires to remain in the workforce.  This is detailed in MetLife's annual Study of Employee Benefits which states that“more than one-third of surveyed Baby Boomers (35%) say that as a result of economic conditions they plan to postpone their retirement.”  How then does the corporation hire new, more informed/better educated talent?    Indeed, the IT skills gap is ever widening.

In order to compensate for these skill discrepencies, many firms have resorted to hire the ideal candidates by demanding they possess a christmas wish list of expertise in a variety of different IT disciplines.  It would not be uncommon that such individuals have a strong programming background and are brilliant DBA's.  What about training?  That is certainly a way to diminish the skills gap.

Although reports made in May 2010 indicate that Android had outsold Apple iPhones, more recent and current reports of the 2^nd quarter of 2011 made by National Purchase Diary (NPD) on Mobile Phone Track service, which listed the top five selling smartphones in the United States for the months of April-June of 2011, indicate that Apple's iPhone 4 and iPhone 3GS outsold other Android phones on the market in the U. S. for the third calendar quarter of 2011. This was true for the previous quarter of the same year; The iPhone 4 held the top spot.  The fact that the iPhone 4 claimed top spot does not come as a surprise to the analysts; rather, it is a testament to them of how well the iPhone is revered among consumers. The iPhone 3GS, which came out in 2009 outsold newer Android phones with higher screen resolutions and more processing power. The list of the five top selling smartphones is depicted below:

1. Apple iPhone 4
2. Apple iPhone 3GS
3. HTC EVO 4G
4. Motorola Droid 3
5. Samsung Intensity II[1]

Apple’s iPhone also outsold Android devices7.8:1 at AT&T’s corporate retail stores in December. A source inside the Apple company told The Mac Observer that those stores sold some 981,000 iPhones between December 1st and December 27^th 2011, and that the Apple device accounted for some 66% of all device sales during that period (see the pie figure below) . Android devices, on the other hand, accounted for just 8.5% of sales during the same period.

According to the report, AT&T sold approximately 981,000 iPhones through AT&T corporate stores in the first 27 days of December, 2011 while 126,000 Android devices were sold during the same period. Even the basic flip and slider phones did better than Android, with 128,000 units sold.[2] However, it is important to understand that this is a report for one particular environment at a particular period in time. As the first iPhone carrier in the world, AT&T has been the dominant iPhone carrier in the U.S. since day one, and AT&T has consistently claimed that the iPhone is its best selling device.

Chart courtesy of Mac Observer: http://www.macobserver.com/tmo/article/iphone_crushes_android_at_att_corporate_stores_in_december/

A more recent report posted in ismashphone.com, dated January 25 2012, indicated that Apple sold 37 million iPhones in Q4 2011.  It appears that the iPhone 4S really helped take Apple’s handset past competing Android phones. According to research firm Kantar Worldpanel ComTech, Apple’s U.S. smartphone marketshare has doubled to 44.9 percent.[3] Meanwhile, Android marketshare in the U.S. dropped slightly to 44.8 percent. This report means that the iPhone has edged just a little bit past Android in U.S. marketshare. This is occurred after Apple’s Q1 2012 conference call, which saw themselling 37 million handsets. Meanwhile, it’s reported that marketers of Android devices, such as Motorola Mobility, HTC and Sony Ericsson saw drops this quarter.

Let’s face it, fad or not, companies are starting to ask themselves how they could possibly use machine learning and AI technologies in their organization. Many are being lured by the promise of profits by discovering winning patterns with algorithms that will enable solid predictions… The reality is that most technology and business professionals do not have sufficient understanding of how machine learning works and where it can be applied.  For a lot of firms, the focus still tends to be on small-scale changes instead of focusing on what really matters…tackling their approach to machine learning.

In the recent Wall Street Journal article, Machine Learning at Scale Remains Elusive for Many Firms, Steven Norton captures interesting comments from the industry’s data science experts. In the article, he quotes panelists from the MIT Digital Economy Conference in NYC, on businesses current practices with AI and machine learning. All agree on the fact that, for all the talk of Machine Learning and AI’s potential in the enterprise, many firms aren’t yet equipped to take advantage of it fully.

Panelist,  Michael Chui, partner at McKinsey Global Institute states that “If a company just mechanically says OK, I’ll automate this little activity here and this little activity there, rather than re-thinking the entire process and how it can be enabled by technology, they usually get very little value out of it. “Few companies have deployed these technologies in a core business process or at scale.”

Panelist, Hilary Mason, general manager at Cloudera Inc., had this to say, “With very few exceptions, every company we work with wants to start with a cost-savings application of automation.” “Most organizations are not set up to do this well.”