Big Data: What Is It and What Is It Used For?

Having emerged in 1997 according to the Association for Computing Machinery, big data was created to allow the digital storage of an unspeakable amount of information. It is a valuable and essential tool that applies in both the private and public spheres. It is used, among other things, in the context of election campaigns, online sales and the banking sector. But also the energy sector, the economy and industry, culture…

However, although we are daily led to interfere with Data through our actions on social networks, or our transactions for example, it is a concept that may still seem vague to many. The purpose of this article is therefore to provide clarifications and useful information to help you better understand the whole notion of big data.

What exactly is big data?

Big data refers to a gigantic set of data from various sources. You should know that nearly 2.5 trillion bytes come every day from the videos we send, our messages, our publications, our GPS signals, records of our purchase transactions, the data transmitted by connected objects, and many more.

But big data also relates to company data such as databases, business processor histories, various documents and emails… This notion of big data is thus used to designate all the digital data that results from the use of new technologies. This, both for personal and professional purposes. Because of this large volume of information, it is impossible for the traditional tools intended for database management to process it.

Big data: what is it used for concretely?

All sectors without exception are now exploiting big data. Within companies and industries, dedicated processing and storage systems have become essential.

Meeting consumer expectations

The use of big data and analytics tools meets objectives inherent in improving the customer experience, M2M (Machine-to-Machine) exchanges or process optimization…

Allowing companies to make quick decisions, big data allows them to rely on the analysis of the resulting information. They can then draw conclusions about consumers’ expectations and needs. Similarly, the information collected can be used to suggest the creation of new products or to create targeted marketing campaigns aimed at increasing the conversion rate.

Analyze, prevent and manage risks

Many companies are guided by data in their evolution. Others, in specific industries such as banks, are using big data for risk management and fraud prevention.

The modeling of the information collected combined with analytical tools allows researchers, companies or administrations to carry out trend and/or predictive analyses with big data. The latter has made it possible to anticipate risks, to train profiles.

All areas are concerned

In the energy sector, new drilling areas can be discovered or operations on a power grid can be monitored using big data. It is also the latter that allows medical researchers to identify risk factors for a disease, establish reliable and accurate diagnoses or anticipate epidemics.

In addition, big data makes it possible to monitor certain phenomena in real time. This is the case for financial speculators who analyze data to create hypothetical bubbles. In transport, big data intervenes for a better management of supply chains and optimize delivery routes.

How to analyze the data collected?
Various techniques make it possible to analyze big data. For example, in order to compare the relevance of a product or service to consumers, companies use benchmarking. There is also marketing analysis, social network analysis or emotional analysis. The latter consists of reviewing the comments on the internet and from there, one can evaluate customer satisfaction.

How does big data storage and processing work?

Because of its characteristics defined by its variety, its velocity, and its enormous volume, big data requires an IT infrastructure adapted to its storage. Its processing requires the combination of thousands of data interacting on a cluster architecture. There are dedicated technologies such as Apache Spark or Hadoop. However, because of their cost, organizations tend to favor the public cloud.

It is for this reason that the rise of cloud computing and that of big data are closely linked. The majority of public cloud providers include big data processing and analysis, beyond storage. Among them, Google Cloud Dataproc, Microsoft Azure HADInsight.

The mastery of tools and techniques dedicated to the processing and exploitation of big data is today a skill highly sought after by companies. If you want to integrate the big data professions, specialized training such as the DataScientist allows you to learn the basics.

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