Big Data: What it Is, What it is For and Why it is Important

Big Data: What it Is, What it is For and Why it is Important

When we hear a term repeatedly without having heard it before, it is a sign that something is breaking the rules. That is what has happened with Big Data, which has revolutionized both the business and digital sectors. Therefore, if you are not yet familiar with this concept, in this article we will explain what Big Data is, how it works, what it is for, and how it can change the course of your company. Keep reading and don’t miss it!


What is Big Data?

Big Data is the concept that encompasses huge volumes of data, both structured and unstructured. It is such a complex and large amount of data that none of the traditional data management tools can store or process it efficiently.


Today, almost more than 7 billion devices share information over the Internet. It is estimated that this figure will rise to 20,000 million in 2025. In this sense, Big Data is in charge of analyzing this ocean of data to turn it into the information that is transforming the world.

History of Big Data

If we Google the term Big Data, we will find different types of concepts, among which we will repeatedly see words such as “massive”, “large scale”, “large data sets”, “huge amounts of data”, “Petabytes” and “Exabytes“.


Do you know how much information an Exabyte contains? The answer is a trillion Gigabytes. An unimaginable amount of data for us as humans, but not for a machine. Large companies like Google tend to talk about Petabytes and Exabytes of information very frequently, and this is normal due to the amount of data they handle. On the other hand, if we lower the scale and start talking about SMEs, the common thing would be to talk about Gigabytes and Terabytes. That is, from Small Data.


The needs of giants like Google were increasing over time. At one point, they had to consider what to do with so much data and how to take advantage of it. This led them to understand that if they analyzed all the information they collected, they could better understand the market and create personalized strategies based on that data to better meet the needs of consumers.


Thus, all that information became the new key to making smart and correct decisions while minimizing risks. In addition, most importantly, they could predict consumer behavior and be at the exact moment when they wanted to satisfy a need.

How Big Data works

Big Data is very complex due to its diversity. This has created the need for systems capable of processing their structural and semantic differences. To do this, it requires specialized NoSQL databases that can store the data in a way that does not imply strict compliance with a particular model. This provides the flexibility to analyze seemingly disparate sources of information and end up gaining a holistic view of what is happening, how to act, and when to act.


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When collecting, processing, and analyzing Big Data, it is often referred to as operational data or analytical data that is stored according to different criteria.


On the one hand, operating systems manage large batches of data across multiple servers and include inputs such as inventory and customer or purchase data. We are talking about the day-to-day information of an organization.


On the other hand, analytical systems are more sophisticated. They are capable of processing complex data analysis and providing information for decision-making. They are often integrated into processes to maximize data collection and use.


What is Big Data for?

Every time we enter a web page we are providing a series of data about our online activity. For example, what do we use a site for, if we are regular visitors or what sites do we access, and how do we do it. Most people are not aware of the amount of information this brings.


This enormous amount of data reaches far beyond the moment in which we turn on our computers since when we walk on public roads we are just as exposed. And it is that, through the geolocation of our terminals, Wi-Fi networks, or surveillance cameras,  anyone can obtain a juicy database that will become useful information through Big Data. 


Gone is the thought that we are only unprotected when we provide our personal data when completing a form or registering to purchase the Internet. All this is something that perhaps most users overlook and that companies are taking advantage of, more and more, to get new business opportunities.


This new technology has become a great business route, since it allows companies to know their customers in-depth, in addition to deepening their needs and the way they act about products and services.


All business sectors use this new technique, not only communication and marketing companies, but also sectors such as medicine, physics, or sports, seeking the return on investment of their campaigns. It occurs, for example, when having a great variety of patient data, which allows improving their quality of life through the prevention and control of diseases.

Why Big Data is important for companies

Any device that is capable of storing and processing information is a generating source of data. What you have to do is organize them so that they become useful information for companies. In summary, the type of content that is interesting to analyze is:

  • Web content obtained from social networks.
  • M2M is the content that allows you to connect to other devices.
  • Invoice records and call details.
  • Biometric information, such as fingerprints or facial recognition.
  • Information such as emails, voice memos, and phone calls.

That is, regardless of how they are classified, we can find data everywhere. On our mobile phones, credit cards, software applications, vehicles, records, web pages …


Big Data is used by most industries to identify patterns and trends. Also to answer questions, detect market needs, demands, and obtain information about customers. Companies use this information to improve their business, understand customer decisions, conduct research, make forecasts, and especially learn how to target key audiences.

Advantages of using data in Big Data

As we have commented in the previous point, Big Data is enormously beneficial if it is used correctly. Organizations can take advantage of all the information provided to them to improve decision-making, be more efficient and optimize costs, as well as segment customers and get new sources of income. What’s more:

  • Increase productivity and efficiency, as the tools process data faster and make it easier for employees to do their jobs.
  • It allows you to improve decision-making, as it provides us with a more informed and reliable base.
  • Reduce costs, as increased productivity can lead to large cost savings and positively impact profitability.
  • It facilitates the detection of fraud and anomalies since they detect erroneous transactions or problems in activities.
  • Greater agility and speed of commercialization.
  • Improve customer service and user experience, as we have more information about what they like and what they don’t like.
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The types of data in Big Data

We can divide the data types in Big Data into:


This group collects all those data that can be stored, accessed, and processed in a fixed format. This type of data represents approximately 20% of the available data and includes numbers, dates, and groups of words. They are the ones we are most used to dealing with and are generally stored in databases.


On the other hand, unstructured data does not follow a specific format. They follow their original shape, as they were collected. To get an idea, if 20% of the data available by companies are structured, 80% is not. They do not have a specific format that allows us to store them traditionally, because the information cannot be broken down. For example, in this group would be emailed, PowerPoint, PDF files …


Semi-structured data falls in the middle. That is, they do not conform to the formal structure of data models associated with relational databases or other forms of data tables, but do contain labels or other markers to separate items and enforce hierarchies of records and fields. For example, JSON and HTML are forms of semi-structured data.

Data characteristics

Big Data started as the 3 Vs process: volume, speed, and variety. However, as it evolved, other Vs appeared: truthfulness, value, and variability. It may be that, when you are reading this post, they have even increased. What’s more, we will dare to add one: vision.


Let’s see what each one consists of:

  1. Volume: l to the amount of data handled.
  2. Speed: having the necessary infrastructure and processes to process data in an agile way and in the shortest possible time to apply change strategies.
  3. Variety: having different sources of data collection on different aspects related to business and consumers. Not only structured data but of different types: behavior, conversations, affinities, photos, videos, etc.
  4. Veracity: how accurate is the data we have. The higher the volume, the more work to organize that data.
  5. Value: knowing how to treat the data that is collected to extract a value from it that helps make the right decisions.
  6. Variability: the different interpretations that can result in the process.
  7. Vision: being able to have a clear vision of how to proceed based on the different patterns and interpretations of consumer behavior.

Challenges in the world of Big Data

Many companies get stuck in the initial stage of their Big Data projects. This is because they are not aware of the great challenges it poses and is not equipped for it.

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One of the main reasons is the lack of understanding and training in the field. Probably the majority of non-specialist employees in this field do not know what data is, how it is processed or stored.

Another challenge is knowing how to properly store this data. The amount of data that accumulates in the centers and databases of the companies increase rapidly and as it does so it becomes more difficult to process.


When it comes to tools, organizations are often confused when selecting the best one for Big Data storage and analysis. If the right one is not chosen, it will end up wasting money, time, effort, and hours of work.


To run these technologies and tools, companies need to hire skilled and knowledgeable data professionals. This includes both experienced data scientists and data analysts and data engineers. The reality is that currently there are not enough experts in the sector to cover this need.

What is Data Governance or Data Governance

Another concept that is heard more and more is that of Data Governance or Data Governance. It is the process of managing the availability, usability, integrity, and security of data in business systems. It is based on internal data standards and policies that also control its use.


The Data Governance ensures that data is consistent and reliable and which are not used improperly. As organizations adapt to new data privacy regulations and increasingly rely on data analytics to help optimize operations and drive business decision-making, the process becomes increasingly critical.


A well-designed Data Governance program includes a governance team, a steering committee that acts as the governing body, and a group of data stewards. They all work together to create data standards and policies, as well as implementation and compliance procedures that are primarily carried out by data administrators. In addition to IT and data management teams, executives and other representatives from a company’s business operations also participate.


It is a base component in a data management strategy. Without good data governance, customer names could appear differently in sales, logistics, and customer service systems, and complicate data integration efforts.


Big data examples

The video-on-demand subscription service platform Netflix is one of the companies that makes the most of Big Data. The company monitors the number of reproductions made by each of its users and analyzes their evaluations, the support they use (from where they access their content), their geographical location, or the day and time of viewing. With all this information, he builds a complete profile of his subscribers.


Other examples of the use of Big Data are:

  • Discover the buying habits of consumers
  • Offer personalized marketing
  • Fuel optimization tools for the transportation industry
  • Monitoring of health conditions through data
  • Live road maps for autonomous vehicles
  • Personalized health plans for cancer patients
  • Predictive inventory order
  • Cybersecurity protocols and real-time data monitoring

The Big Data expert profile has become the profession of fashion. And it is that in recent years the demand for these profiles by companies and organizations has grown by leaps and bounds.


Due to the digital transformation, companies are faced with an amount of data that they have never had to deal with before, which is why they need experts who know how to manage it and, above all, analyze it.


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