Information Technology (IT) tools are here to support your business in the global market. Effective communication is key for IT and business experts to collaborate effectively in search of solutions. Consulting, reaching out for help to a third-party, can bridge the gap between your business marketing experts and IT operations experts, especially with the emergence of big data analytics and its implication on the global market. Having the right consultants equipped with business knowledge and data technology expertise can make a difference.

Your marketing organization is probably familiar with digital tools and conducting global research. Its results can uncover the journey customers take to purchase your products or use your services. It can highlight the pain points and frictions that prevent their experiences with you to be delightful and amazing. Armed with this knowledge and beautiful compelling presentations, marketing executives expect that IT operations leaders will translate these insights into actions.

But people in IT operations are too involved in meeting key performance indicators that have nothing to do with the end customers. Meeting requirements of faster and cheaper don't translate very well into customer satisfaction. A classic breakdown in communication is described in a Harvard Business Review article, “A Technique to Bridge the Gap Between Marketing and IT.” The author goes on to describe how a new CIO at a bank found IT to be focused on the internal organization as their customers, rather than the real end customer. Moreover, no one was looking at the incident reports which clearly showed that incidents were increasing. And nobody looked at what these incidents were doing to the bank’s customers. The startling and scary numbers of incidents were caught and addressed and brought down from 1,000 to 600 or (40%) and later to 450 per week.

Surprisingly, these type of seemingly isolated scenarios are still being discovered within organizations presently, sometimes internally, and through third party insights such as consultants.  By engaging consultants to provide a perspective based on what they’ve experienced before, they can often bring new and innovative ideas or possible challenges to the table that an internal processes probably wouldn’t have been able to see on their own.  Often, third party input can help to provide the translation needed to go from marketing research results into actions that IT operations can understand and make sense in their high-performance culture. When companies understand and use this knowledge to reassess how to improve their customer experiences, they work backward from what customers want to achieve significantly higher improvements. 

IT and business management are more and more being asked to move away from their traditional roles, such as IT being the "technology infrastructure gatekeeper", and instead become enablers across the enterprise of effective collaboration, big data consumers, and key players in driving desired business outcomes. Marketing leaders look to technology as a way to facilitate the customer's journey and his positive experience of it, bring more clients, and meet increasingly higher loyalty goals. They rely on IT projects to enable big data-based behavioral targeting anywhere in the global market. This means projects to analyze search engine results, improve website personalization and optimization, and building of mobile applications for a more personal experience. All these are projects that consultants with their communication, consulting and technical expertise are well prepared to help in order to bridge the expectation gap between IT and other business organizations.

In order to meet these 21st-century business challenges, Information Technology organizations have been transitioning from waterfall stage-gate project management approaches to agile development. The stage-gate method applies a step-by-step approach where waiting, reviewing and approving are required before moving to the next step in the project. Agile management emphasizes collaboration, no decision hierarchies, and few people roles for making quick, customer-focused small changes over time to deliver solutions that delight and amaze customers. Agile development has allowed many businesses to respond quickly to changing customer desires and expectations. But moving to continuous delivery is a struggle requiring focused, dedicated teams that are not well suited to the traditional matrix organization where people are resources whose time must be "chopped" into many pieces and shared among many projects. Agile teams meet frequently as often as daily but never waiting more than a week to do so.

Marketing people are externally focused. IT people are internally focused. The first works with customer emotions. The second works to increase efficiency. Big data analytic tools are used by the first and supported by the second. Consultants can be the glue that helps both come together in effective collaborations that deliver positive business outcomes in both global and local markets.

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.

Before we go to the list do you know what makes software skills most sought-after and hence more valuable than most of the other job skills? It is simply because unlike any other skill, software skills are global and are not at all location specific! With the evolution and penetration of internet technologies, the physical distance between the client and service provider hardly matters. So, with more advancement in technology, it is indeed going to rain opportunities on the right skilled developer. I’ll take the liberty to reproduce the following quotes here to prove my claims statistically:

Demand for “cloud-ready” IT workers will grow by 26 percent annually through 2015, with as many as 7 million cloud-related jobs available worldwide.

---IDC White Paper (November 2012).

In the United States, the IT sector is experiencing modest growth of IT jobs in general, with the average growth in IT employment between 1.1 and 2.7 percent per year through 2020.

---U.S. Bureau of Labor Statistics.

Python and Ruby, each with roots going back into the 1990s, are two of the most popular interpreted programming languages today. Ruby is most widely known as the language in which the ubiquitous Ruby on Rails web application framework is written, but it also has legions of fans that use it for things that have nothing to do with the web. Python is a big hit in the numerical and scientific computing communities at the present time, rapidly displacing such longtime stalwarts as R when it comes to these applications. It too, however, is also put to a myriad of other uses, and the two languages probably vie for the title when it comes to how flexible their users find them.

A Matter of Personality...

That isn't to say that there aren't some major, immediately noticeable, differences between the two programming tongues. Ruby is famous for its flexibility and eagerness to please; it is seen by many as a cleaned-up continuation of Perl's "Do What I Mean" philosophy, whereby the interpreter does its best to figure out the meaning of evening non-canonical syntactic constructs. In fact, the language's creator, Yukihiro Matsumoto, chose his brainchild's name in homage to that earlier language's gemstone-inspired moniker.

Python, on the other hand, takes a very different tact. In a famous Python Enhancement Proposal called "The Zen of Python," longtime Pythonista Tim Peters declared it to be preferable that there should only be a single obvious way to do anything. Python enthusiasts and programmers, then, generally prize unanimity of style over syntactic flexibility compared to those who choose Ruby, and this shows in the code they create. Even Python's whitespace-sensitive parsing has a feel of lending clarity through syntactical enforcement that is very much at odds with the much fuzzier style of typical Ruby code.

For example, Python's much-admired list comprehension feature serves as the most obvious way to build up certain kinds of lists according to initial conditions:

a = [x**3 for x in range(10,20)]
b = [y for y in a if y % 2 == 0]

first builds up a list of the cubes of all of the numbers between 10 and 19 (yes, 19), assigning the result to 'a'. A second list of those elements in 'a' which are even is then stored in 'b'. One natural way to do this in Ruby is probably:

a = (10..19).map {|x| x ** 3}
b = a.select {|y| y.even?}

but there are a number of obvious alternatives, such as:

a = (10..19).collect do |x|
x ** 3
end

b = a.find_all do |y|
y % 2 == 0
end

It tends to be a little easier to come up with equally viable, but syntactically distinct, solutions in Ruby compared to Python, even for relatively simple tasks like the above. That is not to say that Ruby is a messy language, either; it is merely that it is somewhat freer and more forgiving than Python is, and many consider Python's relative purity in this regard a real advantage when it comes to writing clear, easily understandable code.

And Somewhat One of Performance