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.

Big data is now in an incredibly important part of how many major businesses function. Data analysis, or the finding of facts from large volumes of data, helps businesses make many of their important decisions. Companies that conduct business on a national or international scale rely on big data in order to plot the general direction of their business. The concept of big data can be very confusing due to the sheer scale of information involved.  By following a few simple guidelines, even the layman can understand big data and its impacts on everyday life.

What Exactly is Big Data?

Just about everyone can understand the concept of data. Data is information, and information is everywhere in the modern world. Anytime you use any piece of technology you are making use of data. Anytime you read a book, skim the newspaper or listen to music you are also making use of data. Your brain interprets and organizes data constantly from your senses and your thoughts.

Big data, much like its name infers, simply describes this same data on a large sale. The internet allowed the streaming, sharing and collecting of data on a scale never before imaginable and storage technology has allowed ever increasing hoards of data to be accumulated. In order for something to be considered “big data” it must be at least 10 terabytes or more of information. To put that in perspective, consider that 10 terabytes represents the entire printed collection of material in the Library of Congress. What’s even more remarkable is that many businesses work with far more than the minimum 10 terabytes of data. UPS stores over 16 petabytes of data about its packages and customers. That’s 16,000 terabytes or the equivalent to 1,600 printed libraries of congress. The sheer amount of that data is nearly impossible for a human to comprehend, and analysis of this data is only possible with computers.

How do Big Data Companies Emerge?

All of this information comes from everywhere on the internet. The majority of the useful data includes customer information, search engine logs, and entries on social media networks to name a few. This data is constantly generated by the internet at insane rates. Specified computers and software programs are created and operated by big data companies that collect and sort this information. These programs and hardware are so sophisticated and so specialized that entire companies can be dedicated to analyzing this data and then selling it to other companies. The raw data is distilled down into manageable reports that company executives can make use of when handling business decisions.

The Top Five:

These are the five biggest companies, according to Forbes, in the business of selling either raw data reports or analytics programs that help companies to compile their own reports.

1. Splunk
Splunk is currently valued at $186 million.  It is essentially a program service that allows companies to turn their own raw data collections into usable information.

2. Opera Solutions
Opera Solutions is valued at $118 million. It serves as a data science service that helps other companies to manage the raw data that pertains to them. They can offer either direct consultation or cloud-based service.

3. Mu Sigma
Mu Sigma is valued at $114 million.  It is a slightly smaller version of Opera Solutions, offering essentially the same types of services.

4. Palantir
Palantir is valued at $78 million.  It offers data analysis software to companies so they can manage their own raw data analysis.

5. Cloudera
Cloudera is valued at $61 million.  It offers services, software and training specifically related to the Apahce Hadoop-based programs.

The software and services provided by these companies impact nearly all major businesses, industries and products. They impact what business offer, where they offer them and how they advertise them to consumers. Every advertisement, new store opening or creation of a new product is at least somewhat related to big data analysis. It is the directional force of modern business.

Sources:
http://www.sas.com/en_us/insights/big-data/what-is-big-data.html

http://www.forbes.com/sites/gilpress/2013/02/22/top-ten-big-data-pure-plays/

http://www.whatsabyte.com/

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Java still has its place in the world of software development, but is it quickly becoming obsolete by the more dynamically enabled Python programming language? The issue is hotly contested by both sides of the debate. Java experts point out that Java is still being developed with more programmer friendly updates. Python users swear that Java can take up to ten times longer to develop. Managers that need to make the best decision for a company need concrete information so that an informed and rational decision can be made.

First, Java is a static typed language while Python is dynamically typed. Static typed languages require that each variable name must be tied to both a type and an object. Dynamically typed languages only require that a variable name only gets bound to an object. Immediately, this puts Python ahead of the game in terms of productivity since a static typed language requires several elements and can make errors in coding more likely.

Python uses a concise language while Java uses verbose language. Concise language, as the name suggests, gets straight to the point without extra words. Removing additional syntax can greatly reduce the amount of time required to program.  A simple call in Java, such as the ever notorious "Hello, World" requires three several lines of coding while Python requires a single sentence. Java requires the use of checked exceptions. If the exceptions are not caught or thrown out then the code fails to compile. In terms of language, Python certainly has surpassed Java in terms of brevity.

Additionally, while Java's string handling capabilities have improved they haven't yet matched the sophistication of Python's. Web applications rely upon fast load times and extraneous code can increase user wait time. Python optimizes code in ways that Java doesn't, and this can make Python a more efficient language. However, Java does run faster than Python and this can be a significant advantage for programmers using Java. When you factor in the need for a compiler for Java applications the speed factor cancels itself out leaving Python and Java at an impasse.

While a programmer will continue to argue for the language that makes it easiest based on the programmer's current level of knowledge, new software compiled with Python takes less time and provides a simplified coding language that reduces the chance for errors. When things go right, Java works well and there are no problems. However, when errors get introduced into the code, it can become extremely time consuming to locate and correct those errors. Python generally uses less code to begin with and makes it easier and more efficient to work with.

Ultimately, both languages have their own strengths and weaknesses. For creating simple applications, Python provides a simpler and more effective application. Larger applications can benefit from Java and the verbosity of the code actually makes it more compatible with future versions. Python code has been known to break with new releases. Ultimately, Python works best as a type of connecting language to conduct quick and dirty work that would be too intensive when using Java alone. In this sense, Java is a low-level implementation language. While both languages are continuing to develop, it's unlikely that one language will surpass the other for all programming needs in the near future.

In this tutorial I am going to give you a gentle introduction to network programming in Python. If you are new to programming or new to Python then that may seem like a daunting thought. But read on and you will be pleasantly surprised how easy it is.

Like most modern programming languages, Python was designed for networking from the very beginning, and thanks to that, a lot of the networking tasks you would want to accomplish with the language are made a whole lot easier.

Network communication is a large topic, but if it is something that interests you then read on because in this tutorial I will show you how to download a web page. I will show you how easy Python makes tasks like this.

Take a look at the following code:

import urllib
	
con = urllib.urlopen("http://hartmannsoftware.com")
page = con.read()
con.close()
print page