What is Big Data? [A Beginner-Friеndly Guide]

Daniel Attoe
Daniel Attoe

Updated · Jul 19, 2022


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“Big Data” has been in use for close to three decades. For much of that time, it was considered an industry buzzword - a term scoffed at by data management specialists. 

It’s not quite the same these days. 

Let’s consider the question: what is Big Data?

This article will discuss its types, components, characteristics, pros, and cons to paint a general overview of Big Data

What is Big Data? 

Big Data refers to vast amounts of structured, semi-structured, and unstructured data sets. They’re most often too large or complex to be handled by conventional data-processing and management methods. Big Data is massive, diverse, and it grows at a very rapid pace. 

Because of the complexity, velocity, and size of Big Data, it’s impossible to collect, process, and store it using traditional application software.

Data is an essential business tool. Organizations need this valuable asset to address problems they face and solve them effectively. 

Gaining valuable insights from data has become increasingly complex, as it has been increasing dramatically over the years. This is why Big Data analytics is such a rapidly growing field.

Types of Big Data 

This data comes in a raw form before it can be handled appropriately. There are three types relating to its structure. 

Let’s have a brief look at the different types of Big Data.

Structured Data

This form of data can be accessed, processed, and stored in a fixed format. Structured data is well defined and usually follows a consistent order. It can easily be accessed from databases or data warehouses

Structured data is the most straightforward form of Big Data to analyze. This is because it’s already well organized and defined by fixed parameters. In essence, this form of Big Data requires little or no preprocessing.

Unstructured Data

Not all data is as nicely formatted and easy to analyze. Big Data statistics show structured data only constitutes between 10% to 20% of generated data.

Most of the remainder is what we refer to as unstructured data. Unlike structured, this type is unorganized and has no fixed format. It requires a ton of processing before it can be interpretable.

Examples of unstructured Big Data include that which is sourced from medical records, social media, mobile devices, images, video and audio content, even survey responses.

Semi-structured Data

In the area between structured and unstructured data lives a semi-structured type. While this form of information shares a few similar features as structured data, it doesn’t have a definite structure. Semi-structured doesn’t conform to a strict format. However, it’s easier to process than unstructured.

An example of semi-structured data is NoSQL documents; they use keywords to simplify processing and data analysis

Characteristics of Big Data 

Seven characteristics sum up Big Data. Volume, variety, and velocity make up the major characteristics. However, data practitioners have recently added four more V’s to the list: veracity, value, variability, and visualization.


Unsurprisingly, Big Data is big. The high volume of generated data is its main characteristic. 

We generate over 2.5 quintillion bytes of data every day, according to growth statistics. To put things in perspective, it would take a person 181 years to download all the data on the internet at a download speed of 44 Mbps.


Data doesn't just come from uploading pictures or making posts. It’s generated from a wide variety of sources. Together, these sources produce Big Data that can be collected, stored, processed, and then analyzed.

Variety is also one of the biggest challenges. Data professionals have to make sense of largely unstructured information pouring in from several sources.


Velocity refers to the rapid pace that Big Data is created. Vast amounts are generated from various sources at neck-breaking speed. This speed has exploded in recent times and isn’t slowing down soon. 

To prove that point, statistics show that Twitter users post more than half a million tweets every minute. Another example is Google, which sees close to 4 million internet searches every minute. 


The accuracy of the analytics of Big Data hinges, in no small measure, on the reliability of the data itself. Data with low veracity damage the validity of the insights derived from it. It’s crucial that the source it’s collected from is trustworthy. 

Eliminating things like bias, inconsistencies, and duplication will improve veracity.


What improvements can organizations make with the help of Big Data? What insights can they get that will address and solve their problems? There’s no point collecting and storing large heaps of data if they won’t convert into insight. This is because data in itself is inherently useless. 

Proper analytics helps companies to extract the benefits contained within Big Data.


The colossal volume of Big Data makes it impossible for humans to interpret it. Visualization involves using powerful tools to create visual representations of the data. These representations are materialized in either complex graphs or charts. 

They transform the data into readily understandable information that helps companies deduce valuable insights.


In this context, variability refers to the number of inconsistencies in Big Data. It has to do with data whose meaning changes constantly. 

This makes technology like Natural Language Processing (NLP) so difficult. Data scientists must account for contextual words and phrases, homonyms, colloquialisms, and other expressions whose meanings aren’t literal.

Big Data Examples

Let’s look at some principle examples of Big Data.

Social Media 

Social media is one of the most robust sources of Big Data. Interactions on sites and apps like Facebook, YouTube, Twitter, and TikTok generate a massive amount of data every minute. This emanates from activities like posts, comments, likes, and media uploads. It generates a considerable volume of fast-moving data in the form of text, images, videos, voice, and sound. 

Social media data is crucial for making decisions for many of today’s businesses. Although, virtually all data collected is unstructured or semi-structured, which presents a challenge for analytics.

Internet Clickstream 

Clickstream data are detailed records of users’ clicks as they perform tasks on the internet. They include details on the websites they visit, the times spent on each site, and more. Because it isn’t as disorganized as social media data, it doesn’t require the same processing effort.

When considered in aggregate, this data offers valuable insights into the behavior of users, their interests, and the ways they engage with websites and ads. It’s vital for businesses helping to create and manage their digital strategies.

Internet of Things

Internet of Things (IoT) is one of the prominent sources feeding Big Data. Over the last few years, it has gone from large to massive, with this technology’s role in collecting data increasing significantly. 

Sensors and processors found in cameras, smart cars, video games, wearables, smart home systems, and more collect enormous amounts of data. Currently, there are estimated to be close to 36 billion “things” worldwide that feed into IoT. By 2025, that number is expected to rise to 75 billion.

The application of Big Data sourced from IoT is far-reaching, covering various sectors.


Currently, Netflix has upwards of 200 million subscribers, and that figure keeps growing. The streaming service collects data on all of those subscribers. This means that it has to deal with data generated around search queries, watch history, time spent watching content, device identifiers, and more.

As you can imagine, that adds up to quite a lot. This data allows the platform to personalize content recommendations, a major driving force for its growth and dominance.


Big Data plays a huge part in healthcare. For instance, the Mayo Clinic, an academic medical center, receives over one million patients a year. This means that a ton of data pours in relating to them. 

Analyzing Big Data helps the platform detect conditions in patients, deliver more significant patient insights and enhance medical outcomes. 

Pros and Cons of Big Data 

Organizations are finding Big Data essential for extracting beneficial insights. But that doesn’t mean that there are no downsides to consider.

Improved Decision Making - Pro

Businesses invest in data because of how essential it is for decision-making. With even more of it in Big Data, they can interpret patterns and insights into what consumers want and how they behave. This forms a basis for operational and strategic business decisions.

Increased Productivity - Pro

Tools used in the analysis of large data, such as Hadoop and Spark, allow data professionals to analyze higher volumes of data at faster speeds. This increases their personal productivity. But that productivity can also spread around the organization due to the insights.

Lower Costs - Pro

Increased operational efficiency and productivity inevitably lead to lower costs. The use of Big Data for predictions and making better decisions means that companies can reduce wastage significantly. This is because they can find more efficient ways of doing things and implement preventive mechanisms.

Higher Revenues - Pro

When you put together better decision-making, increased levels of productivity, and significant drops in cost, increased revenue is inevitable. Besides just saving money, businesses can also snatch a higher market share and increase profits due to Big Data analytics.

Data Quality - Con

As established previously, the insights an organization gains from Big Data is only as good as the quality of the data it uses. Because a vast chunk of Big Data is unstructured, efforts must be made to ensure they’re accurate and in a format ready for analysis. This is usually a slow, arduous process.

Talent Shortage - Con

To reap its benefits, organizations require the skills that data scientists and other professionals bring to the table. There’s a dual problem of higher demand than supply and the high salary expectations for those needed to deal with the data.

Cost Issues - Con

In the long term, Big Data will reduce costs. However, there’s the problem of an initially high cost of set-up. Infrastructure, such as Big Data storage and management systems, salaries for the relevant professionals, and other expenses could prove too much for many businesses.

Wrap Up

By now, you know what Big Data is, its characteristics, and its role for businesses and organizations. Big Data is no longer a buzzword; it’s currently a very in-demand technology. Organizations are looking for ways to improve their strategies to move to the front of the line or stay ahead of the competition. Big Data can be instrumental in helping them achieve just that.


What is Big Data and why is it important?

Big Data refers to huge quantities of data increasing exponentially and cannot be handled by standard data tools. Big Data helps businesses and other organizations generate valuable insights.

How much is Big Data?

There’s no official definition for Big Data in terms of size. However, it operates in terabytes (or more) of data.

What can you do with Big Data?

There are lots of practical Big Data applications. These include location tracking, fraud detection and handling, advertisements, precision medicine, machine learning and artificial intelligence, and so much more.



Daniel Attoe

Daniel Attoe

Daniel is an Economics grad who fell in love with tech. His love for books and reading pushed him into picking up the pen - and keyboard. Also a data analyst, he's taking that leap into data science and machine learning. When not writing or studying, chances are that you'll catch him watching football or face-deep in an epic fantasy novel.

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