Big Data has become a key term not only for technology professionals but also for managers and decision-makers. The concept represents the exponential amount of data generated at all times, moving the technological world. It can also be said that it is associated with the idea of digital transformation and the application of new technologies in different contexts.

Big Data analytics is a task that brings several benefits, such as cost reduction and more intelligence for management, but requires specific methods to do so. Thus, it is necessary to learn more about the subject in order to take advantage of these advantages and drive innovation. Check out the following topics and clarify your questions!

What is Big Data?

The term means a massive amount of data, structured or not, that is generated continuously. It is related to modern technologies since the use of technological tools is precisely what drives this generation.

The more people use the internet, computers, and smartphones, the more information is generated and consequently aggregated into the whole called Big Data.

The relevance of this expression to the business world is the proper use of this data. If this is an infinite source of information, it should be possible to extract something useful from it to optimize the management and administration of a company. Thus, by using such data, the company is able to improve its decisions and base them objectively, reducing the risk of errors.

That is, leaders are able to provide context to choices by optimizing results. This is possible with specific data analysis tools, responsible for collecting, organizing, and extracting knowledge from these bases.


Among the most common sources are company information (employees, financial, etc.), customer data (social media and Google), and peripheral devices.

This brings us to another feature of Big Data: the data is generated by different sources, which makes its analysis even more complex, but also enhances its benefits. After all, sources can be crossed to smarter conclusions.

It can be said that this information is converted into valuable insights for administrators, and it is as if, before each decision, leaders were able to view in a dashboard all the errors and hits related to that choice. By relying on data, you can get clear answers that guide the directions.

5 V’s of Big Data

Let’s better understand the concept with the five Big Data V’s.


One of the characteristics of this dataset is its volume. This information is usually available in various locations on the internet, so it constitutes a huge and unlimited basis. That is, the big difference between them to the common bases is their massive capacity.


Similarly, this data is generated at a great speed, in real-time, and at all times. This is associated with internet and technology consumption. Because they always use these tools to solve day-to-day problems, they are always feeding the large information base.

Users are looking for greater practicality in the use of technologies, and data generation accompanies this immediacy. Thus, the focus is to describe what happens now and what is relevant at the moment.


The data are also extremely varied and can appear in several formats. Big Data consists of images, videos, documents, audios, report information, among others.

Because they originate from different sources, they are generated in different contexts and with different purposes. This includes the fact that they can be structured, as in traditional databases, or not.

This is a factor that makes the analysis more complex, however, also richer. The more perspectives you can collect, the more relevant the insights generated.


In this large set, there is true information and others do not. It is up to analysts to perform thorough filtering and the separation of what is relevant and what is true since the opposite can be extremely harmful.


The goal of Big Data analytics is to extract value from this information. One of the characteristics of this massive amount is the fact that they offer this possibility.

You can search for relevant insights and generate business strategies from questions asked to the data. In this way, they can be considered an asset of the company, as well as other equipment that has financial value.

What are the advantages of data analysis?

In this topic, we’ll look at the benefits of big data analytics and the opportunities that can be leveraged.


One of the main advantages of relying on a Big Data analysis is predictability. The investigation of this information allows the prediction of the future and the anticipation of possibilities that are yet to come – the so-called predictive analysis.  

This analysis is based on the detection of patterns and the crossing of information from the past, thus allowing the evaluation of the probability of something happening again. This gives decision-makers more power and optimizes their choices.

This characteristic also helps to scale efforts and investments, as well as contributes to the structuring of growth goals and strategies. In this way, the company knows well where it can go and what it can explore.

Cost reduction

Analyzing this massive amount of information also creates cost savings. Because the company loses less time on approaches that do not work, resources are allocated to what is indeed relevant. The analysis generates a greater knowledge of the market and the segment in which the organization operates, and this contributes to the efficiency of investments.

Internally, management gains the ability to better understand business data and reduce spending. Everything is more easily controlled, which is crucial for financial control. This cost reduction happens as a consequence of the other points we will mention in this topic.

Competitive advantage

It is also worth mentioning the increase in competitive advantage. Since it knows the market better, the company can focus on more efficient and effective approaches to conquer new spaces. This makes it possible to stand out in the midst of the competition. Actions get smarter and more proactive, supporting innovation.

With predictive analysis, risks are mitigated. Therefore, it is possible to invest in new ideas and support new production methods, without fear of losses.

More customer satisfaction

Big Data also allows the company to better know its customers and work on strategies to retain them. It is possible, for example, to gather data about your habits, behaviors, and preferences in order to make recommendations to them.

That is, based on knowledge about a particular profile, the company offers some related product or service, in a segmented and specific way.

Thus, the conversation with the consumer is more effective, starting from advisory support to their needs. In this way, the organization gets the support of this client and stands out in the market for the way it relates to its audience.

Evolution and growth

Another factor about these large amounts of data is the ability generated to track and monitor results in real-time. This creates insights that enable evolution and growth, even in the midst of crises. After all, you can study the current results and improve the approaches/products/services according to the possible flaws found.


This technology is also critical to optimize information security, with proactive analysis of possible risks. Predictive research allows you to identify unusual and strange patterns, as well as track suspicious activities, in order to notify leaders. Whenever there is the possibility of fraud, for example, management can quantify and monitor the relevant points to prevent these situations.

With this predictability, the creation of contingency plans becomes more intuitive. If the company prepares better for threats, it remains robust and consistent in the market. In this way, it can focus on the core of the business and growth goals, without stopping to solve unexpected operational problems.


This data analysis requires that information be concentrated in a specific location in order to be understood. The crossing of the various sources is only possible because the data is found and combined. As a result, the company has better communication between sectors, as well as smarter strategies that understand all aspects of the business.

How does it differ from traditional data analysis?

The characteristics we mentioned already differentiate big data a lot and show that it is a differentiated analysis. The variety, for example, along with the speed at which data is generated contributes a sizable increase in its complexity. Therefore, the analyst should perform cleanings and filtrations in order to understand what they mean.

While in traditional analysis, it is possible to perform a division of data between structured and unstructured, Big Data is characterized by being mixed. This emphasizes the greater need for accuracy. This data requires agile, real-time processing, generating results, and quick insights for managers.

Thus, the main difference is that this massive amount requires analysts to use specific tools to handle the data. After all, it is necessary to count on applications that are elastic and flexible, that is, automatically grow and accept various types of files.

This software makes the information communicate with each other and offers some value in the set. In addition, this type of tool uses special, more complex and robust statistical methods than the algorithms of a traditional analysis.

Why has Big Data become so popular?

This term became quite popular because people began to realize the importance of data. In the modern context of digital transformation, technologies play an increasingly important role in people’s lives, in all aspects. Whether to provide leisure or boost business, users use the same equipment.

Because of this, we have generated more information than we have generated in the entire history of mankind. Several companies try to quantify this with research, demonstrating the frightening amount we’re dealing with. Seagate, for example, predicted that  in 2025 we will produce 163 Zettabytes per day, a number 10 times higher than in 2016.

When they realized the potential of this huge mass, many leaders began to pay more attention to the need for their use in business, as we talked about in the first topic.

Another reason for the popularity of Big Data is the fact that it dialogues perfectly with the technologies that are currently in vogue, being the basis for them: Artificial Intelligence, the Internet of Things,  Business Intelligence, Cloud Computing, among others.

Thus, Big Data is  the term that centralizes the concerns of stakeholders in times of digital transformation. If they focus on implementing a good framework to handle this information, companies will be in parallel preparing for other tools and innovations.

What areas can use Big Data?

Let’s deepen the study on this concept, investigating its applications in different areas.


In marketing, Big Data can provide rich opportunities for management. You can study user behavior to establish targeted and personalized communication, as well as an experience that is fully focused on their needs. In addition, this analysis helps create effective promotions and strategies to sell more and attract customers at the exact moment they need it.

Campaigns can be analyzed in greater depth to extract valuable insights for the industry. Predictive analysis allows quantifying the probability of hit and error, which helps to adjust approaches. Thus, it is feasible to optimize the results, hit the missed points and evolve further to ensure greater reach and efficiency.


In logistics, this analysis generates a greater control of the stock of products, since it confers predictability for management. Leadership can predict demands and work to supply them, optimizing what goes in and out of the company’s storage. Thus, you can perform efficient management, focused on saving only what is necessary, without the risk of having goods in stock.


In the financial sector, data analysis acts as a security enhancer. According to what we have already discussed, the information helps prevent fraud and ensure reliability in the management of finances.

In this way, it is possible to ensure compliance between the records and see this area with a more complete view, which helps to identify errors and adjust what is necessary.


In healthcare, Big Data is already driving innovations to optimize the relationship with patients. The focus is to be proactive.

Predictive analyses let physicians know when patients are more prone to certain diseases, which already helps with follow-up in order to avoid future complications. This also helps to optimize the use of medicines, with greater clarification about the problems to be treated.

In other words, the integration of various information about patients allows professionals to know them better, which contributes to the efficiency of care. Thus, it is possible to mitigate errors in treatments and prescriptions.

Traffic management

Another application for Big Data is traffic management. In this area, it is possible to analyze the flow of vehicles on busy streets and extract information about safety and handling. Thus, it is also feasible to work to prevent accidents and more complex problems in these places, optimizing the quality of life of those who traffic.


In the investment market, a common application is in risk analysis. This helps companies that work by advising potential investors and providing tips and insights into opportunities.

With Big Data,they get a more complete view, which enables the identification of patterns and the prediction of success of strategies. By uniting information from various sources, analysts come to accurate directions.


We can see big data implications in the industrial sectors as well. In this field, technology cooperates with increased intelligence and the integration of relevant information to optimize the day-to-day on the shop floor. One of the examples is predictive maintenance, which makes it possible to know when the equipment will present problems, making it possible to perform preventive treatments.

Another advantage in this area is the forecasting of production demands. This analysis helps to direct industrial efforts, establishing a clear focus and generating more transparency in the relationship with those involved. In addition, the industry is impacted by the benefits in logistics, which we have already cited.

It is also worth mentioning the support to the Internet of Things in the monitoring of production and monitoring of product  quality. In this way, it is feasible to avoid operational downtime and ensure smarter and more effective workflows.

How to implement this technology?

We’ll look at the following topics on how you can implement this technology.


Initially, it is critical to plan the steps well and organize the big data implementation process. At that stage, the manager shall define the strategy and objectives of the use of the data. Thus, it is possible to establish a clear direction and seek concrete results.

This step helps define the purpose of data use and organize team expectations with greater predictability. That is, with this, the company can better understand how big data will be inserted in daily tasks and how it will contribute to the improvement of internal processes.

Data culture

It is also interesting to implement a data culture to improve the collection and organization of information. With this mindset, the company will begin to prioritize their use for decision making and constant evolution, eliminating the focus on intuition.

In this sense, it is worth thinking about how to obtain this data and how it will be made analysis of them for the generation of insights.

Support team

To ensure success in strategies, the company must count on the support of people. Therefore, it is ideal to assemble a team capable of boosting results and providing the necessary knowledge base. With people aligned and engaged, it is possible to work focused on objectives and reduce time and errors in the deployment of technology.

Security and privacy

When we talk about data management, it is essential to stress the need for security and privacy care. After all, if your company is going to manage more customer data, it needs to have their consent and use that information only for specific needs, which should be informed to the owners.

Thus, they ensure compliance with the laws on the subject and avoid major problems and risks with safety. Protection strategies need to be strengthened by eliminating flawed points in this direction. As in this area any error can be fatal, the goal is to be accurate and ensure broad and complete care.


Another important part of the implementation is the choice and configuration of the appropriate data analysis tools. The correct software will allow the company to succeed in the strategy and take advantage of the benefits of using this information.

Therefore, analyze well and look for options that adapt to the needs of the company, with good additional resources. It is interesting to seek a complete solution for collection, organization, and monitoring.

Is my company ready for Big Data?

To prepare for Big Data, a company needs to generate data. That is, it is necessary to create a structure of generation of information in bulk, with a collection of several relevant points. In other words, the company should be able to monitor all processes in order to use them as valuable sources of information.

For this, it is worth using management software, such as an ERP and CRM. Both help to obtain data about the internal operations of all sectors and about the relationship with customers, respectively. These applications control processes and generate information about them to make them easier to view and administer.

However, the idea is to go beyond this internal data and merge with external information, from public sources, for example, and data from the Internet. In this way, you can prepare the internal systems for analysis and get value from this large mass of data.

Big Data is a relevant technology that should remain on the radar of managers in the coming years. The benefits are broad and impact the entire company, from relationship with consumers to customer relationships. With the set of steps mentioned, companies are able to implement a data culture that helps optimize decisions and reduce errors and costs.

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