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…

How Can Managers Work More Efficiently with IT?

Would you rather work under someone who is an excellent developer but lacks people skills or leadership capabilities - or for someone that has excellent people skills, communicates well, and is a great leader but has limited understanding of productive coding practices? That’s not to say that the choice is one or the other but in many professional situations it does.

Managing an IT staff comes with numerous challenges, especially if the manager has no previous experience with the coding necessary for completing the project. Managing a business and IT's execution of tasks vary greatly in required skill sets, but it's important to find a cohesive and cooperative middle ground in order to see a project to its end. To fully grasp the intricacies of IT's involvement in the project at hand, managers can do the following to help further their efforts.

Get a basic understanding of coding and technical practices necessary for the project at hand by taking the time to research and practice enough to get a grip on the concept. This will allow managers insight on what their IT folks are really working on daily. Expertise in a programming language is not required, only an overview of the stuff that matters, i.e. understanding the concept of OOP (Object Oriented Programming.) Having this knowledge cannot be overlooked and will gain respect among multiple spectrums in the organization.

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.

Python programming language is general purpose open source programming language. One of its main features is flexibility and ease of use. Python has a variety of useful set of utilities and libraries for data processing and analytical tasks. Currently due to the rise in demand of big data processing python has grown in popularity because its features are easy to use which are core to the processing of huge chunks of information.

Guido Van Rossum, the pioneer of python, introduced python in the year 1980 and then implemented it in 1989. The intention behind the development of python was to make it open source language that can also be used for commercial projects. The fundamental principle of python is to write the code that is easy to use, highly readable and embrace writing fewer lines of code for achieving a particular task. One of the most popular standard libraries which have ready to use tools for performing a various work is Python Package Index. It was introduced in January 2016 and contains more than 72,000 packages for third-party software usage.

Python plays a critical role in linking data to customers. Recently python has found few entry barriers and many people have had access to have experienced the power of python in the past. So, what makes python the best language for big data analytics?

One of the reasons to choose python is that python ecosystem is very vibrant, the ratings at Redmonk are a proof of the strength python community. The Redmonk ranking is based on StackOverflow discussions and contribution made in Github to determine the popularity of programming language on the method used by users to ask questions about Python and the number of the open source projects contributions.