The amount of data generated by people, Internet-connected devices and companies is growing at an exponential rate. Financial institutions, companies and health service providers generate large quantities of data through their interactions with suppliers, patients, customers and employees. Beyond those interactions, large volumes of data are created through Internet searches, social networks, GPS systems and stock market transactions. This widespread production of data has resulted in the “data revolution” or the Age of Big Data.
The term “Big Data” is used to describe a universe of very large sets of data composed of a variety of elements. This gives way to a new generation of information technology designed to make available the increased processing speeds necessary to analyze and extract value from large sets of data, employing – of course – specialized materials and software. The phenomenon of Big Data not only refers to the explosion in the volume of data produced, which was made possible by the development of information storage and dissemination capacities on all sorts of platforms, but the term also refers to a second phenomenon, which involves newfound data processing capabilities.
In general terms, the concept of Big Data describes the current state of affairs in the world, in which there is a constant question of how to manage lumps of data in a better way, and how to make sense of the massive volume of data produced daily.
Data sources are multiplying: smartphones, tablets, social networks, web services and so on. Once these intelligent objects are connected to the Internet, they can feed data into enormous databases and communicate with other objects and humans [PRI 02]. This data must be processed and developed in order to become “intelligent” or “smart”. Intelligence, which can be brought out by using analysis techniques, can provide essential information that top management will require in order to determine strategies, boost operational performance and manage risks.
To this end, “data scientists” must pool their strengths in order to face the challenges of analyzing and processing large pools of data, gaining clarity and precision. Data scientists must make data “speak” by using statistical techniques and specialized software designed to organize, synthesize and translate the information that companies need to facilitate their individual decision-making processes.
1.1. Understanding the Big Data universe
The IT craze that has swept through our society has reached a new level of maturity. When we analyze this tendency, we cannot help being overwhelmed by the transformations that it has produced across all sectors. This massive wave developed very quickly and has resulted in new applications. Information and communication technologies (ICTs) and the advent of the Internet have triggered an explosion in the flow of information (Big Data). The world has become digital, and technological advances have multiplied points of access to data.
But, what exactly is Big Data? The concept really took off with the publication of three important reports from the McKinsey Institute:
- – Clouds, Big Data, and Smart Assets: Ten Tech-Enabled Business Trends to Watch [BUG 10];
- – Are You Ready for the Era of “Big Data”? [BRO 11];
- – Big Data: The Next Frontier for Innovation, Competition and Productivity [MAN 11].
“Big Data” describes: “a series of data, types of data, and tools to respond quickly to the growing amount of data that companies process throughout the world1”. The amount of data gathered, stored and processed by a wide range of companies has increased exponentially. This has partially benefited from an explosion in the amount of data resulting from web transactions, social media and bots.
The growth of available data in terms of quantity, diversity, access speed and value has been enormous, giving way to the “four Vs”: “Volume”, “Variety”, “Velocity” and “Value”2, that are used to define the term Big Data:
- – Volume: the advent of the Internet, with the wave of transformations in social media it has produced; data from device sensors; and an explosion of e-commerce all mean that industries are inundated with data that can be extremely valuable. All these new devices produce more and more data, and in turns, enrich the volume of existing data;
- – Variety: with the rise of Internet and Wi-Fi networks, smartphones, connected objects and social networks, more and more diverse data is produced. This data comes from different sources and varies in nature (SMSs, Tweets, social networks, messaging platforms, etc.);
- – Velocity: the speed at which data is produced, made available, and interpreted in real-time. The possibility of processing data in real-time represents a field of particular interest, since it allows companies to obtain results like personalized advertisements on websites, considering our purchase history, etc.;
- – Value: the objective of companies is to benefit from data, especially by making sense out of it.
The challenges of Big Data are related to the volume of data, its variety, the speed at which it is processed, and its value. Some scholars add another three “Vs”, namely3: “Variability”, “Veracity”, and “Visualization”.
The first V refers to data whose meaning evolves constantly. The second qualifies the result of the data’s use, since even though there is a general consensus about the potential value of Big Data, data has almost no value at all if it is not accurate. This, particularly, is the case for programs that involve automatic decision-making, or for data feeding into unmonitored machine learning algorithms. The last V, which touches on one of the greatest challenges of Big Data, has to do with the way in which the results of data processing (information) are presented in order to ensure superior clarity.
The expression “Big Data” represents a market in and of itself. Gilles Grapinet, deputy CEO of Atos notes that “with Big Data, organizations’ data has become a strategic asset. A giant source of unexpected resources has been discovered.” This enormous quantity of data is a valuable asset in our information society.
Big Data, therefore, represents a large discipline that is not limited to the technological aspect of things. During recent years, the concept has sparked growing interest from actors in the information management systems sector. The concept of the “four Vs” or even that of the “seven Vs” opens up new avenues for consideration and research, but they do not provide a clear definition of the phenomenon. The sum of these “Vs” gives way to new perspectives for new product creation through improved risk management and enhanced client targeting. Actions aimed at anticipating and reducing subscription suspensions or at making customers more loyal can also be envisioned.
The increase in the volume of data, processing speed and data diversity all present new challenges to companies and affect their decisi...
