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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How to Keep On Top Of the Latest Trends in Information Technology

The mainstay of a corporation is the data that it possesses. By data, I mean its customer base, information about the use of its products, employee roles and responsibilities, the development and maintenance of its product lines, demographics of supporters and naysayers, financial records, projected sales ... It is in the organization of this data that advancements to the bottom line are often realized i.e. the nuggets of gold are found. Defining what is important, properly cataloging the information, developing a comprehensive protocol to access and update this information and discerning how this data fits into the corporate venacular is basis of this data organization and may be the difference between moving ahead of the competition or being the one to fall behind.

Whenever we attempt to develop an Enterprise Rule Application, we must begin by harvesting the data upon which those rules are built. This is by no means an easy feat as it requires a thorough understanding of the business, industry, the players and their respective roles and the intent of the application. Depending upon the scope of this undertaking, it is almost always safe to say that no one individual is completely knowledgeable to all facets needed to comprise the entire application.

The intial stage of this endeavor is, obviously, to decide upon the intent of the application. This requires knowledge of what is essential, what is an add-on and which of all these requirements/options can be successfully implemented in the allotted period of time. The importance of this stage cannot be stressed enough; if the vision/goal cannot be articulated in a manner that all can understand, the knowledge tap will be opened to become the money drain. Different departments may compete for the same financial resources; management may be jockeying for their day in the sun; consulting corporations, eager to win the bid, may exaggerate their level of competency. These types of endeavors require those special skills of an individual or a team of very competent members to be/have a software architect, subject matter expert and business analyst.

Once the decision has been made and the application development stages have been defined, the next step is to determine which software development tools to employ. For the sake of this article, we will assume that the team has chosen an object oriented language such as Java and a variety of J EE components, a relationsional database and a vendor specific BRMS such as Blaze Advisor. Now, onto the point of this article.

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.

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Who Are the Main Players in Big Data?

In recent decades, companies have become remarkably different than what they were in the past. The formal hierarchies through which support staff rose towards management positions are largely extinct. Offices are flat and open-plan collaborations between individuals with varying talent who may not ever physically occupy a corporate workspace. Many employed by companies today work from laptops nomadically instead. No one could complain that IT innovation hasn’t been profitable. It’s an industry that is forecasted to rake in $351 billion in 2018, according to recent statistics from the Consumer Technology Association (CTA). A leadership dilemma for mid-level IT managers in particular, however, has developed. Being in the middle has always been a professional gray area that only the most driven leverage towards successful outcomes for themselves professionally, but mid-level managers in IT need to develop key skills in order to drive the level of growth that the fast paced companies who employ them need. 

What is a middle manager’s role exactly? 

A typical middle manager in the IT industry is usually someone who has risen up the ranks from a technical related position due to their ability to envision a big picture of what’s required to drive projects forward. A successful middle manager is able to create cohesion across different areas of the company so that projects can be successfully completed. They’re also someone with the focus necessary to track the progress of complex processes and drive them forward at a fast pace as well as ensure that outcomes meet or exceed expectations.

What challenges do middle managers face in being successful in the IT industry today? 

While middle managers are responsible for the teams they oversee to reach key milestones in the life cycle of important projects, they struggle to assert their power to influence closure. Navigating the space between higher-ups and atomized work forces is no easy thing, especially now that workforces often consist of freelancers with unprecedented independence. 

What are the skills most needed for an IT manager to be effective? 

Being educated on a steady basis to handle the constant evolution of tech is absolutely essential if a middle manager expects to thrive professionally in a culture so knowledge oriented that evolves at such a rapid pace. A middle manager who doesn't talk the talk of support roles or understand the nuts and bolts of a project they’re in charge of reaching completion will not be able to catch errors or suggest adequate solutions when needed. 

How has the concept of middle management changed? 

Middle managers were basically once perceived of as supervisors who motivated and rewarded staff towards meeting goals. They coached. They toggled back and forth between the teams they watched over and upper management in an effort to keep everyone on the same page. It could be said that many got stuck between the lower and upper tier of their companies in doing so. While companies have always had to be result-oriented to be profitable, there’s a much higher expectation for what that means in the IT industry. Future mid-level managers will have to have the same skills as those whose performance they're tracking so they can determine if projects are being executed effectively. They also need to be able to know what new hires that are being on-boarded should know to get up to speed quickly, and that’s just a thumbnail sketch because IT companies are driven forward by skills that are not easy to master and demand constant rejuvenation in the form of education and training. It’s absolutely necessary for those responsible for teams that bring products and services to market to have similar skills in order to truly determine if they’re being deployed well. There’s a growing call for mid-level managers to receive more comprehensive leadership training as well, however. There’s a perception that upper and lower level managers have traditionally been given more attention than managers in the middle. Some say that better prepped middle managers make more valuable successors to higher management roles. That would be a great happy ending, but a growing number of companies in India’s tech sector complain that mid-level managers have lost their relevance in the scheme of the brave new world of IT and may soon be obsolete.