The name placard in your cube might not say anything about sales, but the truth is that everyone, employed as such or not, is a salesperson at some point every single day. In the traditional sense, this could mean something like pitching your company’s solutions to a client. In the less-traditional sense, it could mean convincing your child to eat their vegetables. Yet for those two drastically different examples and everything in between, there is a constant for successful sellers: unveiling the “Why.”

Spending time and energy making prospects understand why you do what you do instead of exactly what it is you do or how you do it is not a new concept. But I’m a firm believer that proven concepts, no matter how old and frequently referenced they are, can’t be repeated enough. This idea has recently and fervently been popularized by marketer, author, and thinker extraordinaire Simon Sinek via his 2009 book, Start With Why. You can learn about him here on Wikipedia or here on his site. To begin, let me suggest that you watch Sinek’s TED talk on Starting With Why here on YouTube before reading any further. I’ll let him take care of the bulk of explaining the basics, and then will offer some ideas of my own to back this up in the real world and explore the best ways to start thinking this way and apply it to your business.

First, a little on me. After all, if I were to practice what Sinek preaches, it would follow that I explain why it is I’m writing this piece so that you, the reader, not only have a good reason to pay attention but also understand what drives me on a deeper level. So, who am I? I’m an entrepreneur in the music space. I do freelance work in the realms of copywriting, business development, and marketing for artists and industry / music-tech folks, but my main project is doing all of the above for a project I’ve been on the team for since day one called Presskit.to. In short, Presskit.to builds digital portfolios that artists of all kinds can use to represent themselves professionally when pitching their projects to gatekeepers like label reps, casting directors, managers, the press, etc. This core technology is also applicable to larger entertainment industry businesses and fine arts education institutions in enterprise formats, and solves a variety of the problems they’re facing.

Not interesting? I don’t blame you for thinking so, if you did. That’s because I just gave you a bland overview of what we do, instead of why we do it. What if, instead, I told you that myself and everyone I work with is an artist of some sort and believes that the most important thing you can do in life is create; that our technology exists to make creators’ careers more easily sustainable. Or, another approach, that we think the world is a better place when artists can make more art, and that because our technology was built to help artists win more business, we’re trying our best to do our part. Only you can be the judge, but I think that sort of pitch is more compelling. It touches on the emotions responsible for decision making that Sinek outlines in his Ted Talk, rather than the practical language-based reasons like pricing, technicalities, how everything works to accomplish given goals, etc. These things are on the outside of the golden circle Sinek shows us for a reason – they only really matter if you’ve aligned your beliefs with a client’s first. Otherwise these kind of tidbits are gobbledygook, and mind-numbingly boring gobbledygook at that.

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.

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.

If you're someone who's interested in computer programming, chances are you've considered pursuing a career in it. However, being a computer programmer is definitely not for everyone, as it takes some special characteristics to succeed as a computer programmer.

Good at Math

While you don't have to be a math genius in order to be a good computer programmer, being good at math really does help. In general, as long as you know your trigonometry and advanced high school algebra, you should be set for programming.

However, in a few instances, knowledge of more advanced math ends up being necessary. For example, for shader programming, you should be familiar with integration of multiple variables, matrix algebra, and basic differentiation. You will also require considerable math skills in order to program 3D.

Excellent Problem Solver

To be a successful computer programmer, you definitely need to be an excellent problem solver. It is vital for a computer programmer to break a problem down into small parts. They must then be able to decide the best way to approach individual pieces of the problem. Computer programmers also need to know how to anticipate and prevent potential problems. While problem-solving, they also need to keep in mind things like user experience and performance.

If you're not a good problem solver, knowing a particular language and syntax will be useless if you can't even identify the problem at hand. Therefore, excellent problem solving skills are a critical foundation for computer programming.

Patience

If you are not a patient person, you will quickly become very frustrated with computer programming. Problem-solving is not always easy and fast. In fact, it may take a very long time, especially if you're either inexperienced or working on an especially hard project.

Debugging after the coding process is also very frustrating and tedious. No matter how hard you try, you will always have bugs in your coding, and these bugs, while often easy to fix, tend to be very difficult to detect. Therefore, you will end up spending a lot of your time searching for bugs that take very little time to fix.

Well-Rounded Skills

Generally, computer programmers who are very skilled in one area tend to stick around longer than jack-of-all-trades, as specialized programmers are harder to replace with outsourcing than general programmers. Therefore, it will do you well to specialize in one area of computer programming.

However, while specializing is good, you should still know at least a little about everything, especially skills that relate to the area you specialize in. For example, if you're a core Java programmer, you should know about SQL programming and ideally a scripting language or some regular expressions.

As you can see, not everyone has what it takes to pursue computer programming as a career and succeed at it. In fact, just because you love to program doesn't mean it's a good career choice for you. However, if you feel that you possess all the characteristics listed above, then you should definitely consider computer programming as a career.