With the rise of the smart phone, many people who have long seen themselves as non-gamers have began to download and play to occupy themselves throughout the day. If you're a game developer who has a history of writing your code in C#, then perhaps this still emerging market is something you should consider taking advantage of. This, however, will require the familiarization with other programming languages.

One option for moving away from the C# language is to learn Java. Java is the programming used for apps on the android platform, billions of phones run on this programming language.

If you want to break into the android market, then learning Java is an absolute must.

There are both some pros and some cons to learning java. Firstly, if you already know C# or other languages and understand how they work, then java will be relatively easy to learn due to having similar, but quite simplified, syntax to C-based languages, the class library is large and standardized, but also very well written, and you might find that it will improve the performance and portability of your creations. Not to mention, learning java opens you up to the entirety of the android app and game market, a very large and still growing market that would otherwise stay closed off to you. That's too much ad and sale money to risk missing out on.

The few cons that come with learning the language is that, when coming from other languages, the syntax may take some getting used to. This is true for most languages. The other problem is that you must be careful with the specifics of how you write your code. While java can be written in a very streamlined fashion, it's also possible to write working, but bulky, code that will slow down your programs. Practice makes perfect, and the knowledge to avoid such pitfalls within the language.

If you wish to develop for the iOS on the other hand, knowledge of Objective C is required. The most compelling reason to learn Objective C is the market that it will open you up to. According to the website AndroidAuthority.com, in the article "Google play vs. Apple app store", users of iPhones and other iOS devices are much more likely to spend money on apps rather than downloading free ones.

Though learning Objective C might be a far jump from someone who currently writes in C#, it's certainly learn-able with a little bit of practice.

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The line between IT consulting and management consulting is quite often blurred, with overlaps between the two fields habitually happening. Worse still, most people do not understand who an IT consultant really is, or what he/she does. There are those who think the job entails fixing computers, others – selling computers and associated accessories. This is misleading though.

In a nutshell, IT consultants are professionals who aid businesses in deciding what computer tools and technologies are best placed to grow and sustain a profitable business. They work hand in hand with clients to help integrate IT systems into the latter’s business. They show clients how to use technology more efficiently, and in so doing, the client is able to get a higher return on their technology investments, and ultimately, increase the bottom-line.

IT consultants, or IT advisories, could work independently or for a consulting firm, with their clientele spread across all sorts of businesses and industries. Companies hire or contract the consulting firm to come in and analyze their IT systems and structure.

The job itself is not short of challenges, however, and the path to becoming a successful IT consultant is fraught with its fair share of ups and downs. But hey, which job isn’t? Experience is the best teacher they say, and only after you’ve worked as a consultant for a number of years will you finally gain invaluable understanding of what is expected of you. Learning from the experiences of those who’ve been in this business for long is a good starting point for those who decide to venture into the world of IT consultancy.

In recent decades, companies have become remarkably different than what they were in the past. The formal hierarchies through which support staff rose towards management positions are largely extinct. Offices are flat and open-plan collaborations between individuals with varying talent who may not ever physically occupy a corporate workspace. Many employed by companies today work from laptops nomadically instead. No one could complain that IT innovation hasn’t been profitable. It’s an industry that is forecasted to rake in $351 billion in 2018, according to recent statistics from the Consumer Technology Association (CTA). A leadership dilemma for mid-level IT managers in particular, however, has developed. Being in the middle has always been a professional gray area that only the most driven leverage towards successful outcomes for themselves professionally, but mid-level managers in IT need to develop key skills in order to drive the level of growth that the fast paced companies who employ them need. 

What is a middle manager’s role exactly? 

A typical middle manager in the IT industry is usually someone who has risen up the ranks from a technical related position due to their ability to envision a big picture of what’s required to drive projects forward. A successful middle manager is able to create cohesion across different areas of the company so that projects can be successfully completed. They’re also someone with the focus necessary to track the progress of complex processes and drive them forward at a fast pace as well as ensure that outcomes meet or exceed expectations.

What challenges do middle managers face in being successful in the IT industry today? 

While middle managers are responsible for the teams they oversee to reach key milestones in the life cycle of important projects, they struggle to assert their power to influence closure. Navigating the space between higher-ups and atomized work forces is no easy thing, especially now that workforces often consist of freelancers with unprecedented independence. 

What are the skills most needed for an IT manager to be effective? 

Being educated on a steady basis to handle the constant evolution of tech is absolutely essential if a middle manager expects to thrive professionally in a culture so knowledge oriented that evolves at such a rapid pace. A middle manager who doesn't talk the talk of support roles or understand the nuts and bolts of a project they’re in charge of reaching completion will not be able to catch errors or suggest adequate solutions when needed. 

How has the concept of middle management changed? 

Middle managers were basically once perceived of as supervisors who motivated and rewarded staff towards meeting goals. They coached. They toggled back and forth between the teams they watched over and upper management in an effort to keep everyone on the same page. It could be said that many got stuck between the lower and upper tier of their companies in doing so. While companies have always had to be result-oriented to be profitable, there’s a much higher expectation for what that means in the IT industry. Future mid-level managers will have to have the same skills as those whose performance they're tracking so they can determine if projects are being executed effectively. They also need to be able to know what new hires that are being on-boarded should know to get up to speed quickly, and that’s just a thumbnail sketch because IT companies are driven forward by skills that are not easy to master and demand constant rejuvenation in the form of education and training. It’s absolutely necessary for those responsible for teams that bring products and services to market to have similar skills in order to truly determine if they’re being deployed well. There’s a growing call for mid-level managers to receive more comprehensive leadership training as well, however. There’s a perception that upper and lower level managers have traditionally been given more attention than managers in the middle. Some say that better prepped middle managers make more valuable successors to higher management roles. That would be a great happy ending, but a growing number of companies in India’s tech sector complain that mid-level managers have lost their relevance in the scheme of the brave new world of IT and may soon be obsolete.

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.