Understand how Big Data technology can store the amounts of data on a large scale beyond the capacity of conventional software to be captured. The Big Data concept also includes the infrastructures, technologies and services that have been created to manage this large amount of information.

According to IDC, the amount of data stored in the world is doubling every two years. The explosion of data that we are witnessing is a consequence of the digital revolution and the great adoption by citizens and companies of tools and technologies such as social networks, mobile devices, geolocation, and objects and sensors connected to the Network – the Internet of Things.

To give us an idea, every day we use many devices through which a huge amount of information is emitted: every time we click on a web page, we pay by credit card, we publish images on social networks, we turn on the GPS, etc. All these (and many more) actions produce massive data that must be treated.

We are therefore facing a new revolution that introduces great opportunities and, at the same time, important challenges for our companies. In this article, we will try to shed light on what Big Data concept is and what it is for.

What is Big Data technology and what is it using for?

In short, when we talk about Big Data we do not only refer to data but above all to the ability to exploit them to extract information and valuable knowledge for our business. The purpose of Big Data is to be able to design new products and services based on the new insights that we acquire about our customers, about our competition or the market in general.

Once the information is collected and stored, indicators should be extracted that may be useful for making decisions, even in real time.

The five “Vs” of Big Data

The first question that comes to mind when considering what Big Data technology is and what it is for is related to how much “big” data must be to be considered “Big”. Finally, the correct approach is not to establish a size at all, but relative. What may now seem like a large data size in two or three years may be normal or even irrelevant.

Most experts define the Big Data concept in terms of the five “Vs”:

• Volume: as we have seen, the amount of data is defined “Big” not when it exceeds a defined size, but when its storage, processing, and exploitation begins to be a challenge for an organization.
• Speed: the second feature of Big Data is related to the rate at which the data is being generated, which usually increases constantly and that needs a real-time response from companies.
• Variety: however, the main challenge of Big Data technology lays in the great difference of different formats in which we find the data and that can range from simple text to images, videos, spreadsheets, and entire databases.
• Truthfulness: in addition, the data must be reliable and must be kept clean. A large amount of data has no value if they are incorrect and can be highly damaging, especially in automated decision making.
• Value: finally, the data and its analysis have to generate a benefit for the companies.

Types of Big Data concept

When classifying the “big data” we can do it according to two criteria: origin and structure. Thus, according to its origin, the data can come from different sources, among others:

• Web and Social Networks: information available on the Internet as Web content, generated by users in their activity on social networks or search engine information.
• Machine-to-Machine (M2M): data generated from the communication between intelligent sensors integrated into everyday objects.
• Transactions: includes billing records, calls or transactions between accounts.
• Biometrics: data generated by identification technology of people through facial recognition, fingerprints or genetic information.
• Generated by people: through emails, messaging services or call recordings.
• Generated by both public and private organizations: data related to the environment, government statistics on population and economy, electronic clinical records, etc.

On the other hand, according to its structure, the data can be:

• Structured: data that has its format, size, and length defined, such as the relational database or Data Warehouse.
• Semi-structured: data stored according to a certain flexible structure and with defined metadata, such as XML and HTML, JSON, and spreadsheets (CSV, Excel).
• Unstructured: data without specific formats, such as text files (Word, PDF, emails) or multimedia content (audio, video, or images).

What is the use of Big Data technology in companies?

Once we have accepted that the data is here to stay, the next question is about the advantages that they can represent for our organization. In this sense, a study carried out by Bain & Company clearly demonstrates the competitive advantages that early adopters of Big Data concept can obtain. These companies have:

• Twice as likely to obtain a financial return higher than the average of its industries.
• Five times more likely to make decisions much faster than its competitors.
• Three times more likely to execute decisions as planned.
• Double the probability of making decisions based on data.

What is Big Data technology and what is it for? Real examples:

To understand what the Big Data is for, let’s see some real examples of its use:

• Marketing: customer segmentation. Many companies use massive data to adapt their products and services to the needs of their customers, optimize operations and infrastructures, and find new business fields.
• Sports: performance optimization. Devices such as smartwatch automatically record data such as calorie consumption or fitness levels.
• Public health: coding of genetic material. For example, there are platforms for analyzing Big Data concept that is dedicated to decoding DNA chains to better understand diseases and find new treatments.
• New technologies: development of autonomous devices. The analysis of massive data can contribute to improve machines and devices and make them more autonomous. An example is smart cars.
• Security: detection and prevention of crimes. The security forces use Big Data to locate criminals or prevent criminal activities such as cyber attacks.

Tools and solutions for the Big Data concept:

Big Data technology needs a new kind of tools that can encompass the complexity of unstructured and continuously expanding data. For this, traditional relational database technologies or RDBMS are not adequate. In addition, advanced analysis and visualization applications are needed in order to extract the full potential of the data and exploit it for our business objectives.

Let’s see some of the main tools below:

Hadoop: Hadoop it is an open source tool that allows us to manage large volumes of data, analyze them and later processes them. Hadoop implements MapReduce, a programming model that supports parallel computing over large collections of data.

NoSQL: These are systems that do not use SQL as query language, which, despite not being able to guarantee the integrity of the data (ACID principles: atomicity, consistency, integrity, and durability), allows them to obtain significant gains in scalability and performance when working with Big Data concept. One of the most popular NoSQL databases is MongoDB.

Spark: spark is an open source cluster computing framework that allows you to process data quickly. It allows writing applications in Java, Scala, Python, R and SQL and works on Hadoop, Apache Mesos, Kubernetes, as well as independently or in the cloud. You can access hundreds of data sources.

Storm: Storm is a free code distributed real-time computing system. Storm allows to process unlimited data flows in real time in a simple way, being able to be used with any programming language.

Hive: Hive is a Data Warehouse infrastructure built on Hadoop. It facilitates the reading, writing, and administration of large data sets that reside in distributed storage using SQL.

A: “A” is one of the programming languages most used in statistical analysis and data mining. It can be integrated with different databases and allows us to generate graphics with high quality.

D3.js: D3.js is a JavaScript library to produce dynamic and interactive visualizations of data in web browsers, using HTML, SVG and CSS.

4 key steps to get into the Big Data concept

In order to start enjoying the benefits of Big Data, any organization needs to have four key assets:

• First, the data. In an environment where the data is exploding, its availability does not seem to be the problem. What should concern us is rather to be able to maintain their quality, and know how to handle and exploit them correctly.
• Adequate analytical tools required for this. Those tools are not a barrier for companies today, due to the wide availability in the market of both proprietary and open source tools and platforms.
• Which brings us fully to the third fundamental asset, which is the human factor? Having the right professionals in our organization as data scientists and also experts in the legal implications of data management and privacy policy is emerging as the most important challenge.
• However, equipping ourselves with these three assets and putting them to work will not guarantee our success with Big Data either. To be true data-driven companies, we will need to carry out a radical transformation of our processes and business culture, to make the data truly stand at the center of our company and ensure that all departments, from IT to senior management, assume this new focus.

The challenges of Big Data technology

Nowadays no company can ignore Big Data and the implications it has on its business. However, it is a relatively new and constantly evolving concept, and there are many challenges that organizations face when dealing with big data.

Among them:

• Technology: Big Data tools like Hadoop are not so easy to administer and require specialized data professionals as well as important resources for maintenance.
• Scalability: a Big Data project can grow with great speed, so a company has to take it into account when allocating resources so that the project does not suffer interruptions and the analysis is continuous.
• Talent: the profiles required for Big Data are scarce and companies are faced with the challenge of finding the right professionals and, at the same time, to train their employees on this new paradigm.
• The actionable insights: in front of the amount of data, the challenge for a company is to identify clear business objectives and analyze the appropriate data to achieve them.
• Data quality: as we have seen before, it is necessary to keep data clean so that decision making is based on quality data.
• The costs: the data will continue to grow, so it is important to correctly size the costs of a Big Data project, taking into account both the facilities and its own personnel and the hiring of suppliers.
• Security: finally, it is necessary to maintain secure access to data, which is achieved with user authentication, access restrictions, and data encryption in transit or stored and complying with the main data protection regulations.

We have seen the great benefits of Big Data technology for companies, as well as the main challenges of its implementation. Those organizations that know how to take into account these factors will be able to launch a successful Big Data concept and obtain a significant competitive advantage when creating new products and services.