Have you ever wondered what Big Data is and what it is for? We understand as Big Data the amounts of large-scale data that exceed the capacity of conventional software to be captured, processed and stored in a reasonable time.
The Big Data concept also encompasses 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 attending 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.
Thus, understanding what Big Data is and what it is for implies also knowing the entire context of the data generation of which we are participants.
To get an idea, every day we use many devices through which a huge amount of information is issued: 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 is and what it is for.
What is Big Data and what is it for?
In short, when we talk about Big Data, we are not only referring to data but above all to the ability to exploit it to extract information and knowledge of value for our business. The purpose of Big Data is to be able to design new products and services based on the new insights we acquire about our customers, about our competition or the market in general.
Once the information has been collected and stored, indicators that can be useful for making decisions must be extracted, even in real-time. Therefore, the truth about what Big Data is and what it is for goes far beyond just thinking about “massive data.”
The five “Vs” of Big Data
The first question that comes to mind when considering what Big Data is and what it is for, is related to how much “big” the data has to be considered “Big”. Finally, the correct approach is not to establish a size at all, but relative. What can now seem like a large size of data, in two or three years it can be normal or even irrelevant? Most experts define Big Data in terms of the five “Vs”:
- Volume: As we have seen, the amount of data is defined as “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 characteristic of Big Data is related to the rate at which the data is being generated, which is constantly increasing and that needs a real-time response from companies.
- Variety: However, the main challenge of Big Data lies in the great difference of different formats in which we find the data and can range from simple text, images, videos, spreadsheets, and entire databases.
- Truthfulness: also, 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 companies.
Types of Big Data
To deepen what Big Data is and what it is for, it is also necessary to know that there are different types of data associated with this technique.
When classifying the “big data” we can do it according to two criteria: origin and structure. Thus, depending on their origin, the data can arrive 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 search 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 people identification technology 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 medical 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 relational databases 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: specific unformatted data, such as text files (Word, PDF, emails) or multimedia content (audio, video, or images).
What is Big Data for companies?
Once we have accepted that the data has come to stay, the next question is about the advantages they can represent for our organization. In this sense, a study carried out by Bain & Company demonstrates the competitive advantages that early adopters can obtain from Big Data. These companies that have understood what Big Data is and what it is for having:
- Twice as likely to obtain a higher financial return as the average of their industries.
- Five times more likely to make decisions much faster than its competitors.
- Three times more likely to execute decisions as planned.
- Twice as likely to make decisions based on data.
Real examples of what Big Data is and what it is for
To understand practically what Big Data is and what it is for, let’s look at 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 infrastructure, and find new business fields.
- Sports: performance optimization. Devices such as smartwatches automatically record data such as calorie consumption or fitness levels.
- Public health: coding of genetic material. For example, there are Big Data analysis platforms that are dedicated to decoding DNA strands 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: crime detection and prevention. Security forces use Big Data to locate criminals or prevent criminal activities such as cyber-attacks.
Tools to put into practice what Big Data is and what it is for in Companies
Big Data needs new tools and technologies that can cover the complexity of unstructured and continuously expanding data. For this, traditional relational database technologies or RDBMS are not suitable. Besides, advanced analysis and visualization applications are needed, to extract the full potential of the data and exploit it for our business objectives.
So, after understanding what Big Data is and what it is for, let’s look at some of its main tools:
- Hadoop: is an open-source tool that allows us to manage large volumes of data, as well as analyze and process them. Hadoop implements MapReduce, a programming model that supports parallel computing over large data collections.
- NoSQL: these are systems that do not use SQL as a 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. One of the most popular NoSQL databases is MongoDB.
- 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 both Hadoop, Apache Mesos, Kubernetes, as well as independently or in the cloud. You can access hundreds of data sources.
- Storm: is a real-time distributed free code computing system. Storm allows you to process unlimited real-time data streams easily and can be used with any programming language.
- 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: It is one of the most used programming languages in statistical analysis and data mining. It can be integrated with different databases and allows generating high-quality graphics.
4 Key Steps to Get on Big Data
To start enjoying the benefits of this technology after knowing what Big Data is and what it is for, any organization needs to have four key assets:
First, the data. In an environment where data is exploding, its availability does not seem to be the problem. What should concern us is rather being able to maintain their quality, and knowing how to handle and exploit them correctly.
For this, adequate analytical tools are needed, which also does not represent a barrier for companies today, due to the high 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 in our organization the right professionals, such as data scientists, but also experts in the legal implications of data management and your privacy are emerging as the most important challenge.
However, providing these three assets and putting them to work will not ensure 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 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
Today, no company can ignore the issue of what Big Data is and what it is for since the implications that this technology can have on business are many. However, it is a relatively new and constantly evolving concept, and there are few challenges that organizations face when it comes to relating to big data. Among them:
Big Data tools Hadoop cones are not so easy to manage and require specialized data professionals in addition to important resources for maintenance.
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.
The necessary profiles for Big Data are scarce and companies face the challenge of finding the right professionals and, at the same time, training their employees on this new paradigm.
The Actionable Insights
Against the amount of data, the challenge for a company is to identify clear business objectives and analyze the appropriate data to achieve them.
As we have seen before, it is necessary to keep the data clean so that the decision is based on quality data.
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 own personnel as well as the contracting of suppliers.
Finally, it is necessary to keep data access secure, 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 for companies, as well as the main challenges of its implementation. Now, you know what Big Data is and what it is for. Those organizations that know how to take these factors into account will be able to launch successful Big Data projects and gain a significant competitive advantage when creating new products and services.