Why Big Data is Important and how to manage

big data

Big Data* is not just the latest fad, it’s the future of how we are going to guide and grow business. It is the biggest game-changing opportunity for marketing and sales since the Internet went mainstream almost 20 years ago.

Some facts:

Every hour, enough information is consumed by Internet traffic to fill 7 million DVDs. Side by side, they’d scale Mount Everest 95 times.

By 2020, one third of all data will be stored or will have passed through the cloud, and we will have created 35 zettabytes of data.

We have produced more data in the last two years (≈3,500 exabytes) than in all of history prior to that.

30 billion pieces of content shared on Facebook every month

250€ billion potential annual value to Europe’s public sector administration from Big Data – more than GDP of Greece

Moreover, in the fall, Columbia will offer new master’s and certificate programs heavy on data. The University of San Francisco will soon graduate its charter class of students with a master’s in analytics. Other institutions teaching data science include New York University, Stanford, Northwestern, George Mason, Syracuse, University of California at Irvine and Indiana University.

Big data is turning to be a crucial issue for companies, government and whole society.

Why do you care about?

… because you simply are part of business team. If we are going to really capitalize on Big Data, we need to get to human insight at machine scale. We will need systems that not only perform data analysis, but then also communicate the results that they find a clear, concise narrative form. Also, algorithmic marketing is allowing companies to do things they couldn’t do before, and some early signs show it can deliver big value, especially in financial or information services. To go algorithmic, companies need to move from batch systems (where work is done at regular intervals) to algorithmic system (real-time updates)

In 2010, MIT Sloan Management Review, in collaboration with the IBM Institute for Business Value, conducted a survey of more than 3,000 business executives, managers and analysts One of the most significant findings is that there is a clear connection between performance and the competitive value of analytics

Is it relevant to you?

If you still wonder how this is related to you, then keep in mind that Big Data and Analytics need people with IT skills such as SQL, Hadoop, R etc. It request people with experience in Machine Learning, Statistics, Data Analysis, Data visualization, Algorithms, Artificial Intelligence, Visualization and design, and many other areas.

“That’s one of the challenges,” said Terence Parr, program director of the analytics and computer science programs at the University of San Francisco. “To be successful, you need to have a wide range of skills that doesn’t fit in one department.”

And if you are not convinced yet then let me inform you that in a globalization system in which unemployment is rising rapidly, in Data Analysis area something different happened. To meet demand from employers, the United States will need to increase the number of graduates with skills handling large amounts of data by as much as 60 percent, according to a report by McKinsey Global Institute. There will be almost half a million jobs in five years, and a shortage of up to 190,000 qualified data scientists, plus a need for 1.5 million executives and support staff who have an understanding of data.

North Carolina State University introduced a master’s in analytics in 2007. All 84 of last year’s graduates in the field had job offers, according to Michael Rappa, who conceived and directs the university’s Institute for Advanced Analytics. The average salary was $89,100, and more than $100,000 for those with prior work experience.

Harvard Business Review calls data science “the sexiest job in the 21st century,” and by most accounts this hot new field promises to revolutionize industries from business to government, health care to academia.

*Definition: “Big data is any data that is expensive to manage and hard to extract value from” – Michael Franklin

Big data and analytics