Content originally published in the eBook The definitive Big Data guide for beginners and upgraded to the blog
The world generates, every day, 2.5 quintillion bytes (1 quintillion equal to 10 raised to the 18th power). The most diverse daily actions of society (from user manifestations in social networks to corporate records and financial transactions) have become valuable data for companies that can use them to better know their customers, understand their buying behavior and even announce a crisis in the industry or migration of customers to competition.
In this increasingly dynamic scenario, having access - before rivals - to market changes is the undeniable threshold between living and dying in the corporate universe. And here's where the secret of success comes in working with Big Data .
A survey conducted by the University of Oxford, still in 2013, already revealed the power of Big Data in modern companies. The survey, conducted with 1,144 managers from 95 countries (including Brazil) showed that 53% of organizations were already using Big Data to understand and improve the customer experience . Even sports leagues, like the NBA , have done it.
The application possibilities of Big Data go far beyond the customer experience, in fact. For example, you can use this technology to increase IT infrastructure security, improve marketing ROI, reduce costs, optimize processes, and even predict market movements before the competition.
Yes, this is possible, and it is not done on the basis of intuition; is the power of IT allied to Statistics, Social Science and advanced mathematical programming. Not surprisingly, a recent study by IDC, a market research and consulting firm, showed that Big Data's global market is expected to grow 600% more than IT by 2018, moving the astounding $ 41.5 billion over the same period.
SECOND IDC RECENT STUDY, THE BIG DATA MARKET MUST GROW 600% MORE THAN IT BY 2018
However, even with its popularization and more than optimistic projections of Big Data's growth for the next few years, some basic questions about the subject are still common. What are the differences between Big Data and BI? How to use data collection and analysis in practice? What types of insights can be generated?
If you also have these doubts - whether you are a manager who wants to take your company into a new age, a professional seeking to specialize in Data Science , a teacher who wants to approach this new technology in the classroom with his students, or simply a curious about it - it's worth investing a few minutes of your day reading this true beginner's guide to Big Data!
Index (or 'what we'll cover in this super article'):
- The concept
- After all, what are the differences between Big Data and BI?
- How are these data transformed into insights?
- What insights can we get?
- Big Data Success Cases
- Conclusion
The concept
The term Big Data is as broad as its name implies. To contextualize it and make sense of it, dear reader, there is a brief explanation: we live in an Age in which, (each) year and a half, the same amount of data already created by mankind is generated in all times.
This current epoch, of generating immeasurable volumes of data by companies, people, and apparatus, is called the Big Data Age. The term Big Data also corresponds to the very absurd amount of data currently generated - the "Big Data".
One of Big Data's developments is the term Big Data Analytics , which refers to powerful software capable of handling this data to turn it into useful information for organizations.
Initiatives supported by Analytics enable you to analyze structured and unstructured data, such as call center records, social network and blog postings, CRM data, balance sheets and profit and loss statements. In this way it facilitates the discovery, in real time, of the opportunities that are beyond what human eyes can see in an organic way.
Also read: Do you know what Big Data Analytics is?
There are several versions about the origin of the Big Data concept, as well as the beginning of its applications. One of the best known refers to NASA, which began using Big Data in the early 1990s to describe huge, complex data sets that defied the conventional boundaries of computing at the time.
In this model, data capture, processing and analysis were done through high impact systems, involving the simultaneous work of numerous sciences. The purpose of these powerful software was to generate knowledge and intelligence from raw data that, alone, could not say.
Prior to Big Data, mathematical formulas, advanced probability and statistical techniques (such as frequency analysis, historical series, and moving average studies) were performed manually - thus dealing with a reduced capacity of variables. With the advent of high-capacity processors and impressive speed, it was possible to transpose all these calculations through software specially developed to turn these "trails" into powerful strategic information to any segment. A Big Data solution works with complex algorithms, aggregating data from diverse sources, relating them, and generating key conclusions for corporate decision making.
Today in Big Data solutions are used by the Treasury to avoid sonegações taxes for the weather to predict natural phenomena at retail to better see the reactions of your customers - you've seen how Amazon uses Big Data ? - by banks to offer personalized services and reduce churn rates (customer exit) for the area to develop attractive products strictly in accordance with what consumers expect ( to name just a few sectors). In short: all areas can take advantage of this technology.
0 comentários:
Postar um comentário