Last year, Hewlett-Packard purchased Autonomy for $10 billion, enduring a number of arrows from its competitor Oracle. What a difference a year is!

Since the purchase HP saw the leaving of Autonomy CEO Mike Lynch, which was not on friendly terms. It also saw the departure of the whole original management team and 20 percent of the staff. Now, the question is whether or not HP will see its move compensate or become another WebOS-style fiasco, marking the company needs to stay with hardware.

We are not all equally motivated. Some people have more self-drive than other people. This is why we find that some people always end up at the top even when the odds are against them. An employee, with this realization, through the Human Resource department, should be able to design efficient career development systems. For this system to work, the employer must understand the nature of the business environment in which they are operating.

Why Train Employees?

The purpose of training employees is to enable them to grow with time and increase their efficiency. The business world is quite dynamic, nothing stays the same for long. Training one’s employees allows them to keep abreast with the ever changing technological advancements and many other factors that are relevant to his/her line of work. Employees cannot be expected to solve all their employer’s expectations with static skills and techniques. Even the most updated technology becomes obsolete at some point.
People are the biggest assets in organizations. For an establishment to flourish, it is important that the employer understands certain key things that help spur their development.

In a report from the Harvard Business Review, “The Impact of Employee Engagement of Performance,” the most impactful employee drivers are:

When you think about the black market, I’m sure the majority of you will think of prohibition days.  When alcohol was made illegal, it did two things: It made the bad guys more money, and it put the average joe in a dangerous position while trying to acquire it.  Bring in the 21^stcentury. Sure, there still is a black market… but come on, who is afraid of mobsters anymore? Today, we have a gaming black market. It has been around for years, but will it survive? With more and more games moving towards auction houses, could game companies “tame” the gaming black market?

In the old days of gaming on the internet, we spent most of our online time playing hearts, spades… whatever we could do while connected to the internet. As the years went by, better and better games came about. Then, suddenly, interactive multiplayer games came into the picture. These interactive games, mainly MMORPGS, allowed for characters to pick up and keep randomly generated objects known as “loot”. This evolution of gaming created the black market.

In the eyes of the software companies, the game is only being leased/rented by the end user. You don’t actually have any rights to the game. This is where the market becomes black.  The software companies don’t want you making money of “virtual” goods that are housed on the software or servers of the game you are playing on.  The software companies, at this point, started to get smarter.

Where there is a demand…

The interpreted programming language Python has surged in popularity in recent years. Long beloved by system administrators and others who had good use for the way it made routine tasks easy to automate, it has gained traction in other sectors as well. In particular, it has become one of the most-used tools in the discipline of numerical computing and analysis. Being put to use for such heavy lifting has endowed the language with a great selection of powerful libraries and other tools that make it even more flexible. One upshot of this development has been that sophisticated business analysts have also come to see the language as a valuable tool for those own data analysis needs.

Greatly appreciated for its simplicity and elegance of syntax, Python makes an excellent first programming language for previously non-technical people. Many business analysts, in fact, have had success growing their skill sets in this way thanks to the language's tractability. Long beloved by specialized data scientists, the iPython interactive computing environment has also attracted great attention within the business analyst’s community. Its instant feedback and visualization options have made it easy for many analysts to become skilled Python programmers while doing valuable work along the way.

Using iPython and appropriate notebooks for it, for example, business analysts can easily make interactive use of such tools as cohort analysis and pivot tables. iPython makes it easy to benefit from real-time, interactive researches which produce immediately visible results, including charts and graphs suitable for use in other contexts. Through becoming familiar with this powerful interactive application, business analysts are also exposing themselves in a natural and productive way to the Python programming language itself.

Gaining proficiency with this language opens up further possibilities. While interactive analytic techniques are of great use to many business analysts, being able to create fully functioning, independent programs is of similar value. Becoming comfortable with Python allows analysts to tackle and plumb even larger data sets than would be possible through an interactive approach, as results can be allowed to accumulate over hours and days of processing time.

This ability can sometime allow business analysts to address the so-called "Big Data" questions that can otherwise seem the sole province of specialized data scientists. More important than this higher level of independence, perhaps, is the fact that this increased facility with data analysis and handling allows analysts to communicate more effectively with such stakeholders. Through learning a programming language which allows them to begin making independent inroads into such areas, business analysts gain a better perspective on these specialized domains, and this allows them to function as even more effective intermediaries.

Related:

Who Are the Main Players in Big Data?