What Is Big Data and How Does It Work [+ The 5 Vs Explained] (2024)

If there is a word in the dictionary that can constantly send a chill down the spines of many professionals, it’s “analysis”.

We can already see some people running and hiding, changing screens, or going to get a coffee…

But don’t be scared!

Apparently, it’s not just in the marketing world that analyzing data creates some discomfort.

Analytics is becoming more and more present in the daily routines of various segments, and one of the main reasons for this is the constant growth of Big Data.

More than 2.5 exabytes (or 25,000,000,000,000,000,000 bytes) are created each day, and that number has nearly doubled every 3 years since 1980.

All the time, more and more companies are understanding how — and why — Big Data matters, and adopting strategies that involve more data analysis and insights from that analysis.

It’s time for you to do the same.

In this article we will explain what is Big Data and other points that constantly lead to doubts:

    Are you ready? Let’s start, then!

    What Is Big Data?

    Big Data is the information set present on servers and company databases, which can be accessed and have interconnections between them.

    That is, the data that’s available on the world wide web and can be accessed remotely.

    To make it clearer, YouTube is an example of BD, because it makes several videos present in a database available for users to access.

    Another example is Wikipedia, with articles available on databases for online consultation.

    However, Big Data is not just about databases and online information.

    According to Forbes Magazine contributor Lisa Arthur, many CMOs and CIOs agree that Big Data is any and all data that can be collected about a company or a topic.

    It is a collection of information from traditional and digital sources, inside and outside your company, which represents a source of continuous discovery and analysis.

    Although digital media reigns supreme when it comes to data accumulation, the biggest companies in the market affirm that they mustn’t leave out:

    • Non-digital sales information.
    • Financial records.
    • Interaction channels, such as call centers and even telemarketing.

    All of those things can be data sources that might help your business grow.

    What Is The History Of Big Data?

    Even though the term “Big Data” is relatively new, the premise of gathering and storing information is quite old.

    Think about libraries, for example.

    However, the concept took on a new approach in the early 2000s, when the analyst Doug Laney wrote an article that is today the best-known definition of Big Data.

    Doug separated his idea into 5 Vs, which we’ll see below.

    The increasing amount of available data and just-in-time business models have made it essential to have a way of analyzing large amounts of data in real-time.

    That is precisely the main difference between applying the idea of BD in your company and storing information without any kind of organization or analysis.

    What Are The 5 Vs Of Big Data?

    Currently, we can divide Big Data into 5 Vs that form the basis for implementing the concept in any company.

    They are: Volume, Velocity, Variety, Veracity, and Value.

    We will explain each of them in detail below.


    Volume is the starting point for understanding BD.

    The 2.5 quintillion data created each day bring an impressive amount of information from the most diverse sources.

    Like social media, website and blog interactions, purchase history, clicks, and even from tracking leads and customers.

    The volume of data influences two main points: storage and analysis.

    With the daily expansion of servers, it became much easier to store huge amounts of data, especially with file compression and the possibility of making data available in the cloud.

    Analysis, on the other hand, becomes increasingly simpler, especially with the use of tools tailor-made for Big Data.


    Data is transferred, stored, and downloaded at lightning speeds.

    And we’re not talking about the speed of your internet connection, but how this content is worked on, updated, and expanded quickly.

    More and more sources provide data, which makes it necessary to manage this information in real-time, quickly, and securely.


    Data comes in all shapes, colors, and sizes.

    It can be spreadsheets, structured data, or just text documents, videos, and images.

    It is necessary to understand the existing varieties and how each should be analyzed and stored.


    The speed and variety of data are constantly growing, but these elements also change and are not necessarily consistent.

    Imagine social media — how do they generate information on a regular basis?

    They are totally dependent on user actions and, even though it might seem that way, we are not connected 24 hours a day.

    The idea of veracity can also be applied to daily, seasonal, and event-specific data, which generates a large volume of information and can result in management challenges.


    With such a huge amount of data, you’ll likely lose track of everything when you really need it.

    One of the main reasons for that is how hard it is to connect and transform information across different platforms.

    Therefore, it is necessary to connect and correlate the elements, create hierarchies and multiple linkages to the data — in other words, create links between them.

    Why Was Big Data Created?

    The incredibly high volume of information generated daily — and accumulated over the past few years — has come to be seen as a source of insight rather than just a bunch of data.

    Therefore, it was necessary to think of an analysis model that would help companies find valuable ideas among so many bytes.

    Big Data was that solution.

    It allows organizations to discover opportunities not only where they are clear, but also by correlating and cross-referencing complex data, and through the decoupage of structured, unstructured, and multi-structured data.

    How To Interpret Data?

    You’ve probably already understood the importance of data to Big Data. After all, data has been mentioned here several times.

    But there is more than one type of data.

    Basically, any information can be defined like this, but there are different formats that can facilitate, hinder, or direct the analysis made by companies.

    Data is divided into two types: structured and unstructured. Let’s get to know a little more about each of them.

    Unstructured data

    Over the years, databases have become able to interpret data more easily.

    This happened largely due to the fact that these databases store similar information, such as spreadsheets, calculations, functions, and numbers, which facilitates the way in which this content is analyzed.

    However, new data formats have arrived — and they are not as easily interpreted as before.

    These are called unstructured data, information that is not organized or that is not easily understood by traditional databases and known data formats.

    In general, these elements are predominantly texts. Blog metadata, images, and tweets are examples of unstructured data.

    Structured (or multi-structured) data

    On the other hand, some data formats can be easily recognized by databases, facilitating analysis and processing.

    They are called structured (or multi-structured) data.

    These types of data are derived from interactions between people and machines, such as web applications and social media.

    A good example is data that results from the behavior of users on the web — also known as logs.

    It is a mixture of text, images, and data structured like forms or transactional information.

    Digital advancement has transformed much of this data, bringing even more formats to those considered structured.

    With the constant evolution of the relations between brands, consumers, and platforms, the trend is that these types of data will continue to evolve and change.

    What Is The Job Market For Big Data Like?

    The insertion of Big Data in companies generated the need for professionals specialized in inferences from data, mainly in the area of ​​statistics.

    However, specializing only in statistics is not exactly what companies prefer.

    One of the main characteristics of BD is the possibility of finding opportunities to increase sales; therefore, analysts must have knowledge of the area in which they are working.

    A Big Data analyst working in marketing, for example, needs to be familiar with:

    What Is Big Data and How Does It Work [+ The 5 Vs Explained] (2)

    • Lead generation.
    • Content Marketing.
    • Email Marketing.
    • Social media.

    If the professional is a part of the sales team, then they need to understand sales cycles, customer lifetime value (CLTV), and all other processes involved.

    Most likely, this is the reason why several MBAs on Big Data exist, not only focusing on the technical and theoretical sides, but also addressing issues related to administration, marketing, logistics, and several other segments.

    So if you want to enter the market — or are looking for qualified professionals — remember that plurality is one of the keys to doing well in Big Data.

    What Is The Connection Between Big Data And Marketing?

    Make no mistake: marketing (and sales) can benefit from Big Data as much as any other sector.

    Maybe even more than any other.

    That’s because companies that base their marketing on data — also known as data-driven marketing —, without a shadow of a doubt, are the ones that dominate the market.

    The most effective marketing teams are those that collect data gathered from leads, user behavior, and team experiences, and turn it into actions that help achieve results.

    We have separated 5 aspects of marketing that are favored with the use of Big Data.

    Check it out!

    Data interpretation

    Companies that make decisions based on data are happier.

    It’s true!

    They are happier because their resolutions are based on analytics, data, and reports that help them be effective.

    A decision is more likely to bring the expected results when it is based on data that proves its projected efficiency.

    A study carried out by KPMG Capital showed that 99% of respondents believe in Big Data as an important part of decision making.

    However, 85% of them claim that they have difficulty interpreting data and only 25% apply the insights gained through data analysis.

    Therefore, a qualified professional is needed to properly interpret data and help the company make the right decisions.

    For this, the tools are as important as the professionals.

    Most of them are data organization and decoupage platforms, which facilitate their inference in the near future.

    Market analysis

    The professionals’ intuition and experience have guided companies to success.

    However, the path has not always been so smooth. Lots of errors, testing, and money were spent in the process.

    This happened because you can’t guess what your audience wants — and Big Data comes in handy at this point.

    Personas — semi-fictional profiles — are indispensable to a quality Digital Marketing strategy, and to create good personas you need solid information about what they like, where they are, and what content they want to consume.

    That’s when finding behavioral patterns among your company’s customers and how they act within the market would be very interesting, wouldn’t it?

    Descriptive analysis of events, correlations, and ideas generated from data are the knowledge sources that Big Data provides for your marketing strategy.

    Purchasing experience

    The decision-making process behind purchasing can be a big mystery.

    What makes a person choose your company?

    There can be many reasons, and if you’re involved in the world of trade marketing, direct selling, or multilevel marketing, Big Data can be your best friend — and you might not even know that.

    Analyzing data helps your business find the best means of product distribution, point of sales (PoS) techniques, and providing the purchasing experience that the customer expects.

    In multi-level markets, there is a high demand for customization, and you need strategic knowledge to meet your persona’s needs.

    More data sources for your business

    In the early days of Big Data, data sources were limited to information generated by a few companies in a few programs.

    Clearly, we’ve seen how the volume of referrals has increased exponentially over the years. And that’s not at all negative.

    Of course, now it takes an expert to get the really relevant statistics from the available data, but at the same time, the variety of these elements is ever higher.

    Twitter, Facebook, Instagram, and various apps provide data on a daily basis.

    A retailer can use weather data to buy the right products at the right time.

    An event planner can choose the best date for an action based on weather forecasts, and so on.

    All sources of information help your company make the best decisions, find the best solutions for your customers, and drive sales.

    Weblogs have expanded to become the content of social media, data from Business Intelligence (BI) tools, reports, macroeconomic indicators, and much more.

    More and more sources provide relevant information for companies.

    Marketing automation

    Good old marketing automation is not indispensable just for successful Content Marketing and Inbound Marketing strategies.

    It also is an additional data source and can make interesting analyses for companies.

    If we think about it, automation platforms store information about the user, behavior characteristics, and even campaign and action revenue metrics.

    Therefore, it’s another part of Big Data that is highly relevant to your company.

    In addition, marketing automation tools have many integrations that help you have an ever broader view of the material you have available for analysis.

    What Is Big Data Analytics?

    Surely you’ve already understood that you need to analyze in order to grow.

    In fact, it was this reasoning that motivated the growth of the concept of Big Data so much. Therefore, the analytical side cannot be left out.

    Big Data is the set of data within datasets, whereas Big Data Analytics is about getting hands-on and finding insights into market trends, consumer behavior, and their expectations.

    What analytics does is take all of the data sources that we’ve seen before — the raw material of it all — and create correlations, inferences, and links between the data.

    Thus, you can find metrics relevant to your company, use numbers to make decisions, and create more efficient strategies.

    Here’s what you’ll need to know:

    Identifying patterns

    Patterns are very important. Your audience patterns help you build your persona.

    Users who behave similarly and become customers give you insight into the actions you should take to gain more similar customers.

    Remarketing is a good example of these patterns and analytics.

    After becoming interested in a book, how many similar offers have you received after that? This is one of the applications of the knowledge obtained through this diagnosis.


    Knowing and understanding what your competition is doing is very important to the success of your company.

    You won’t necessarily attack all your competitors, but the real advantage lies in learning from their mistakes.

    Data analysis is an excellent tool to find the best paths to take, based on the actions of your competitors that didn’t get the expected results.

    Most of these insights come from the behavior of your audience and help you create specific promotions for your market.

    Big data analytics in marketing and sales

    Metrics are highly important to both teams.

    Marketing needs to understand which actions are generating the most results — in terms of leads, ROI, and traffic — and the sales team needs to find the actions and processes that convert the most leads into customers.

    Analysis is already part of the daily life of most of these teams, but if it’s not part of your company’s team, it’s time to start!

    Big Data helps companies understand patterns of behavior and responses to actions taken.

    Many companies use this analysis to:

    • Reduce their customer acquisition cost (CAC).
    • Increase customer lifetime value (CLTV).
    • Optimize pricing and discounts.

    The secret of successful companies is to make Big Data Analytics an active part of the teams’ daily lives.

    Walmart created Walmart Global Tech, which helps the company create tools for connecting with the public through data collected about customers.

    MIT used the MIT Media Lab to map how many people were in the largest US shopping center at any given time during Black Friday.

    Nike uses its running app to encourage users to play sports and share it on social media, and collects various business-relevant information.

    Pretty interesting, isn’t it?

    Wrap Up: Data As Business Opportunities

    The idea of ​​Big Data seeks to transform everything that companies collect in their customers’ daily lives — in the form of data and information — into valuable knowledge for campaigns and strategies.

    With so much content available, both from your own customers and from visitors and strangers, not taking the chance to improve your business really isn’t the best option.

    But you don’t have to be part of a big organization to apply the idea of Big Data.

    Free tools such as Google Analytics and even social media management, Email Marketing, and Marketing Automation tools can yield interesting insights.

    That’s why we’ve prepared a special post for you with all you need to know about Marketing Analytics and how to apply data to marketing strategies. Happy reading, and see you next time!

    What Is Big Data and How Does It Work [+ The 5 Vs Explained] (2024)
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