With the skyrocketing popularity of Android and iOS operating systems, software developers got a whole new arena opened up. Many of the programmers have progressed to concentrate solely onto Mobile Technology Development. This is mainly due to the high demand as well as numerous lucrative ideas left to explore in the Mobile App world.

Exponential growth of smartphone users

As per the survey by eMarketer, the number of smartphone users across the globe crossed 1 billion almost two years ago. The expected number of smartphone users by 2014 end is 1.75 billion.

With smartphones, iPads and Tablets getting more accessible and less expensive day by day, the development potential for mobile apps is truly vast. The under-penetration in emerging markets like India and China in Asia shows that there seems to still a lot of steam left in the mobile app development industry.

Machine learning systems are equipped with artificial intelligence engines that provide these systems with the capability of learning by themselves without having to write programs to do so. They adjust and change programs as a result of being exposed to big data sets. The process of doing so is similar to the data mining concept where the data set is searched for patterns. The difference is in how those patterns are used. Data mining's purpose is to enhance human comprehension and understanding. Machine learning's algorithms purpose is to adjust some program's action without human supervision, learning from past searches and also continuously forward as it's exposed to new data.

The News Feed service in Facebook is an example, automatically personalizing a user's feed from his interaction with his or her friend's posts. The "machine" uses statistical and predictive analysis that identify interaction patterns (skipped, like, read, comment) and uses the results to adjust the News Feed output continuously without human intervention. 

Impact on Existing and Emerging Markets

The NBA is using machine analytics created by a California-based startup to create predictive models that allow coaches to better discern a player's ability. Fed with many seasons of data, the machine can make predictions of a player's abilities. Players can have good days and bad days, get sick or lose motivation, but over time a good player will be good and a bad player can be spotted. By examining big data sets of individual performance over many seasons, the machine develops predictive models that feed into the coach’s decision-making process when faced with certain teams or particular situations. 

General Electric, who has been around for 119 years is spending millions of dollars in artificial intelligence learning systems. Its many years of data from oil exploration and jet engine research is being fed to an IBM-developed system to reduce maintenance costs, optimize performance and anticipate breakdowns.

Over a dozen banks in Europe replaced their human-based statistical modeling processes with machines. The new engines create recommendations for low-profit customers such as retail clients, small and medium-sized companies. The lower-cost, faster results approach allows the bank to create micro-target models for forecasting service cancellations and loan defaults and then how to act under those potential situations. As a result of these new models and inputs into decision making some banks have experienced new product sales increases of 10 percent, lower capital expenses and increased collections by 20 percent. 

Emerging markets and industries

By now we have seen how cell phones and emerging and developing economies go together. This relationship has generated big data sets that hold information about behaviors and mobility patterns. Machine learning examines and analyzes the data to extract information in usage patterns for these new and little understood emergent economies. Both private and public policymakers can use this information to assess technology-based programs proposed by public officials and technology companies can use it to focus on developing personalized services and investment decisions.

Machine learning service providers targeting emerging economies in this example focus on evaluating demographic and socio-economic indicators and its impact on the way people use mobile technologies. The socioeconomic status of an individual or a population can be used to understand its access and expectations on education, housing, health and vital utilities such as water and electricity. Predictive models can then be created around customer's purchasing power and marketing campaigns created to offer new products. Instead of relying exclusively on phone interviews, focus groups or other kinds of person-to-person interactions, auto-learning algorithms can also be applied to the huge amounts of data collected by other entities such as Google and Facebook.

A warning

Traditional industries trying to profit from emerging markets will see a slowdown unless they adapt to new competitive forces unleashed in part by new technologies such as artificial intelligence that offer unprecedented capabilities at a lower entry and support cost than before. But small high-tech based companies are introducing new flexible, adaptable business models more suitable to new high-risk markets. Digital platforms rely on algorithms to host at a low cost and with quality services thousands of small and mid-size enterprises in countries such as China, India, Central America and Asia. These collaborations based on new technologies and tools gives the emerging market enterprises the reach and resources needed to challenge traditional business model companies.

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.

Outsourcing IT needs in the corporate world has become extremely popular because it is cost-efficient and it gives IT resources to companies that may not otherwise be able to afford them. Another positive side effect of IT outsourcing is that it has brought many technology jobs back to the United States.

As convenient and patriotic as IT outsourcing has become, it also have several limitations that have caused many companies to re-think the idea of funding their own internal IT group. It is important for a company to be completely familiar with these limitations before developing any kind of company policy in regards to IT.

The Customer Could Outgrow The Outsource Company

When an IT outsourcing company first takes on a new client, the relationship is beneficial to both sides. But things can start to get inconvenient for the client when the client's business starts to outgrow the capabilities of the IT outsource group.

An IT outsourcing company can become entrenched in the daily routine of its clients, which is great at first. But when the IT company can no longer keep pace with the growth of its clients, then the clients are stuck trying to find a new solution and keep track of its own IT assets at the same time.

The IT Outsourcing Company Lacks The Necessary Technical Expertise

IT outsourcing clients like to believe that their support company knows everything there is to know about computers and the Internet. But every IT support company has its areas of expertise and they have the technical areas where they are not quite as strong. If the client starts to experience needs from the areas where the IT outsourcing company is not so strong, then that can become a significant business issue.

This problem can be amplified if the client is a small business experiencing growth. An IT outsourcing company is not as likely to bring on new personnel for a smaller client, which leaves the client without a solution.

The Client Losses A Measure Of Control Over Its Data

No matter how large or successful an IT outsourcing company may be, there will always be the limitation of client security and the protection of critical customer data. All it takes is one rogue employee of the IT outsourcing company to compromise all of the client's critical data.

Some IT outsourcing companies have safeguards put in place to try and prevent client data compromise, but those safeguards are limited by how much access the IT company has to the client network. In most cases, that access has to be comprehensive for the IT outsourcing company to be able to do its job.

Outsourcing IT responsibilities can take a lot of stress off of a client and allow that client to operate his business by focusing on his core competencies. But there are limitations to IT outsourcing that could make it necessary for a client to do his own IT support and pay the extra costs.