All about big data

Big data refers to the process of acquiring, storing, and analyzing large volumes of data that, given their size, cannot be processed in a conventional manner. But the concept does not only cover the processing of the information itself. It also refers to the technologies that enable this data processing, as well as how it is used.

Perhaps that is why, in order to understand how big data works, the first step is to be clear about where the data comes from. That information can come from a variety of sources. Something as trivial as writing a comment on a social network, clicking on a link, or answering a telephone survey generates a huge amount of data. But it's not just people who produce data; machines do it too. This is what is known as M2M (machine to machine), which is nothing more than data shared between machines, whether they are parking meters, electricity meters, automated irrigation systems, etc. Online transactions, web marketing, or biometric sources such as recognition sensors or scanners are also the source of very valuable information.

What sets big data apart is its unique characteristics, popularly coined around the 'v's of big data. The three main ones are volume, velocity, and variety. The first refers to the enormous amount of data generated continuously while velocity refers to the speed with which this data is generated and must be processed. Variety, on the other hand, implies that the data can be of different types and formats, from text and numbers to images and videos.

Over time, these three essential 'v's have been joined by others such as: veracity (closely related to the reliability of the data and, in turn, the potential of its applications), visualization (referring to the synthesizing capacity to show results in a clear and simple way), value (related to the challenges of big data to efficiently obtain valuable information from the data collected), or variability (referring to the versatility not only of the data but also of the processing procedures and tools themselves).

In short, big data is not just about the amount of data, but also about the ability to leverage it to obtain valuable information and make informed decisions.

When we talk about how big data works, we are talking about four key phases: acquisition, storage, processing, and value creation.

In the information gathering stage, data are obtained from various sources, both structured and unstructured. There are also different methods and techniques to collect them, such as Web Scraping or different APIs (Application Programming Interfaces) created for this specific purpose.

Then, in the storage stage, the data is stored in mass storage systems, such as databases or cloud storage systems. Once stored, the data undergoes processing and analysis, where advanced tools and techniques are used to identify hidden patterns, trends, and correlations. These insights translate into valuable information that big data companies can use to improve their operations, make strategic decisions, better understand their customers, and predict future trends.

Finally, the action or value-creation stage for big data involves taking actions based on the insights gained from data analysis. This can include personalizing customer experiences, optimizing business processes, detecting fraud, or anticipating product demand, among others. In this way, big data not only helps companies understand the past and present, but also enables them to better prepare for the future, making the most of their resources and making more informed and accurate decisions.

The impact of big data is present in a multitude of industries and applications with examples showing how companies are using it to improve their operations and generate value. Here are some easy-to-recognize examples.

• Retail and e-commerce. Large companies in the industry use big data to analyze customer buying behavior, provide personalized recommendations, and optimize the supply chain for more efficient delivery.
• Health. In the medical field, for example, one of the applications of big data has to do with its use to analyze large genomic and medical data sets to develop personalized treatments, predict disease outbreaks, or improve patient care.
• Finance. Banks and financial institutions use big data to detect fraud, predict risk, and make more informed investment decisions.
• Transportation and logistics. One of the main contributions for big data companies in the sector is the possibility to optimize routes, improve transport efficiency, and offer faster services.

Big data is transforming entire industries, creating new opportunities for growth and business and labor innovation. At Repsol, we are clear about this and have made digitalization one of the pillars of our strategy. Through our Data & Analytics & Artificial Intelligence Hub, we have developed ARiA, the big data and Artificial Intelligence cloud platform that centralizes all the company's data to develop analytical models and algorithms that help improve processes and decision-making. In addition to supporting various digital initiatives within the company, we have launched this pioneering tool in the sector, in order to help other companies to deploy and accelerate the use of big data and AI in their fields.

As a big data company, at Repsol we are committed to the professions of the future, by making them a reality. The company has positions ranging from translator analytics to data engineers, data scientists, and data managers, among others. Big data is not only changing the way companies operate, but also the way we work, interact, and live in the digital era. It is a phenomenon that is here to stay, and those who take advantage of it effectively will shape the future of business.