What Is Big Data? How Massive Datasets Are Reshaping the World

What’s your take on Bigfoot? If you’ve read Pierre Boulle’s “Planet of the Apes” or watched the four-part movie series (I absolutely loved it), the portrayal of walking and talking gorillas and orangutans might fit the description of the mythical creature.

The truth is, I don’t think Bigfoot exists. If you’re up for the ultimate mystery project, big data could help you confirm Bigfoot’s existence and prove me wrong.

Thousands of passionate Bigfoot hunters have collected a gigantic database of Bigfoot clues, including footprints, eyewitness reports, pictures and videos, and hair samples. You can refer to this database as Bigfoot’s “big data.”

Big data refers to large and diverse sets of data that can’t be processed by your typical data processing tools. Examples include medical records, financial data, and weather patterns.

With the help of big data tools, you could crack the code of Bigfoot’s existence. Artificial intelligence (AI), for example, might reveal patterns in Bigfoot sightings — anything is possible with big data. Maybe Bigfoot is just really, really into privacy.

Unraveling the “Bigfoot Mystery” isn’t the only thing you can achieve with big data — it plays a pivotal role in the digital age, as it helps businesses make informed decisions that lead to innovation and growth. No, I’m not throwing these words like confetti — big data is a big deal. To shed light on it, we create approximately 402 million TB of data daily. Yes, that’s a lot of data!

This includes data from social media, transactional data, and customer interactions. Businesses use this somewhat “messy” data to extract meaningful insights and make data-driven decisions.

There’s a reason why I call big data messy: because it is. It’s time for a bookish definition: big data refers to humongous, complex datasets that can’t be stored, managed, or analyzed by your average data processing tool. You need to assemble the Avengers of data processing to tackle it.

The 5 Vs of Big Data

If you show a data scientist the “V” sign, they’re likely to think of 7–8 things. The first two are obvious to you and me: peace and victory. The next five, which are big data characteristics… not so much. Let’s explore them! Also, if you’re wondering about the eighth one, my lips are sealed.

• Volume: Big data is HUGE. I mean, we’re talking about datasets in petabytes and exabytes!
• Velocity: “To Infinity and Beyond!” You can think of big data as a never-ending stream of information flowing at lightning-quick speeds.
• Variety: Big data is honorable — it accepts all shapes and sizes of data types (structured, unstructured, and semi-structured).
• Veracity: While I believe in equality, not all data created is equal. Some data is reliable, but you might need to treat some with caution.
• Value: It’s not just about collecting a bucketload of data, but how you extract valuable insights from it.

While it might seem challenging to make sense of these big data characteristics, they’re what makes it so powerful.

How These Characteristics Shape Data Management

The five Vs of big data highlight the opportunities you have to manage and leverage large datasets and, of course, the challenges you could face. If we take volume, for example, storing, processing, and analyzing exabytes of data is far from easy. But this also means there’s a wealth of information at your disposal if managed correctly.

Let’s also consider the velocity of incoming data, as it is in the case of real-time data. If you have the right tools in your kit, you can analyze this data quickly for timely decision-making. Just imagine how useful big data is in trading!

I’m going to reference Bigfoot for the final time. Since there isn’t a centralized, official repository for Bigfoot data, let’s say you get your hands on all the data out there by hook or crook.

This includes information from the Bigfoot Field Researchers Organization (BFRO), the Bigfoot Research Database, YouTube, and social media. You now have Bigfoot big data, but how do you make use of it? Here are some of the technologies and tools you can use:

Data Storage and Processing Platforms

If you’re a PB&J sandwich person, you might be tempted to use Apache Spark on top of Apache Hadoop (you can use each independently as well).

With the help of this combination, you can store massive amounts of data, break them down into manageable chunks, and process them in double-quick time. Hadoop is the heavy-lifter in this equation, while Spark provides fast and flexible processing.

You could also opt for a cloud-based solution (for a less hands-on approach) and harness virtually unlimited storage and processing power.

Data Analytics Tools

Not all superheroes wear capes. My dad is my superhero, for example, and he doesn’t have the physical might to match Superman. But he definitely has the heart and brain.

For a data scientist, machine learning frameworks such as TensorFlow and Scikit-learn and visualization tools like Tableau and Power BI are no less than superheroes. In fact, data scientists may consider themselves superheroes and these data analytics tools as their trusted sidekicks!

After all, they use machine learning frameworks to train computers to think for themselves and visualization tools to transform raw numbers into interactive, easy-to-understand visuals. Respect.

Role of Artificial Intelligence in Big Data

To a data scientist, AI is like a magic wand, as it turns complex, almost nonsensical data into something actionable (predict future trends and behaviors, detect anomalies, analyze sentiment, etc.).

While I don’t recommend swearing by AI (at least not during its nascent stages of development), it can help you make decisions with confidence.

When was the last time you purchased something from an eCommerce store? I bought a car phone holder from Amazon a couple of days ago. The funny thing is, I bought one a few months ago but lost it (it was brand new).

What’s even funnier is I forgot all about my evident need for a car phone holder until Amazon suggested a product in the “Deals for you” section of the homepage.

I don’t know whether to be grateful or creeped out. What I do know is big data played a hand in this recommendation. How? Hold your horses.

Being big has its struggles. I’m not the biggest man (by height and weight) in my neighborhood, but I understand how difficult it is for big people to live normal lives. Just look at Shaq — a normal water bottle looks like a toothpick in his hand!

Imagine being rich enough to afford a Ferrari but not being able to enjoy it due to your size. “Big” data has its challenges as well. While it’s as “rich” as Shaq, it has four fundamental challenges.

Data Privacy and Security

Let’s say there’s a huge data breach at a major healthcare provider. Can you imagine the chaos the hackers could cause?

They’d have access to thousands, if not millions, of health-related customer information.

As long as these firms are following a strict privacy-first approach, all is well and good.

The ethical side of things is a big concern, too — I don’t want any company selling my data to another. Neither should you.

Data Quality

When’s the last time you filled a form with a bogus name, like “Harry Potter?”

Big data tools can only do their magic if the quality of the data fed to them is complete, consistent, and accurate.

It’s simple, really — garbage in, garbage out.

I get that you might want to have a little fun at a restaurant and not reveal your real name to the person at the reception.

But be true to yourself when you’re providing personal details where necessary.

Infrastructure and Scalability

Big data is, well, big. It costs a lot of money to store big data datasets, and processing them isn’t a piece of cake.

You’ve got to have the right infrastructure in place — you can’t fit an ocean in a bathtub!

Don’t let this put you off — innovation doesn’t come easy, and big data is at the forefront.

Thankfully, cloud storage is a viable option for businesses that don’t want to maintain their own infrastructure.

Talent Gap

I’m not sure if this will remain a problem in the coming years, but for now, yes, there’s a shortage of skilled data scientists and analysts.

Your healthcare specialist certainly doesn’t have the expertise to leverage big data by themselves! If you think about it, “big data” has been around for years, but we’ve never really put it to use.

One of my best friends is studying to be a data scientist, and there are several others who are taking such courses.

I consider this more a boon than a bane — we’re soon going to have a young crop of talent with the world at their feet.

Honestly, I don’t enjoy talking about the challenges of anything because it dampens the mood. But hey, I’m as real as it gets, and I always say things as they are. If you’re an aspiring data scientist or analyst, the future of big data is… big. Okay, let’s use the word “bright” instead. Yes, the future of big data is bright.

Trends in Big Data Technology

I’m sure you’ve heard of or read about real-time analytics and edge computing at some point, so let’s not go there. There’s this great misconception that the blockchain industry as a whole is fraudulent. That’s not the case.

Blockchain is used in just about every industry, and big data could be one of the biggest winners. One of the challenges I explored earlier was data privacy and security. Using blockchain will add trust and transparency to the data game.

How? Do some research. You’ll enjoy this read.

Big Data and Sustainability

Do you want to go green? I recommend starting small — carry a jute bag with you whenever you go shopping, for example. Sure, your Prada bags stand out in the shopping mall — but it’s about what’s on the inside, not the outside, right?

Businesses are using data to go green. With the help of big data, they’re monitoring the environment, optimizing energy use, and shrinking their carbon footprints. Let’s make a change together.

The Impact of Regulations on Big Data

Businesses don’t often play fair — it’s a cutthroat game. Thanks to the influence of laws like GDPR and CCPA on data handling practices, the big data playground comes with rules businesses must follow.

With the help of these regulations, your data will be guaranteed a privacy-first future. Of course, some businesses might “cheat” — trust the ref to show them a red card.

If you’re a business owner and can get your hands on a skilled data scientist or a talented professional who can learn the ropes of big data management and processing, you should be able to maximize profits.

By studying customer buying patterns, for example, you could replenish your inventory accordingly. And I’m not just talking about online stores. Big data can be used just about everywhere.

If you’re enthused by big data after reading my piece (which means I’ve done my job), there are several free or relatively cheap big data courses on the market (explore Coursera) and it doesn’t hurt to earn knowledge, so spare some time if you can and explore the depths of big data.

While the ocean of big data is deeper than the Mariana Trench, you should be able to guide your submarine through its depths if you dedicate yourself to a life of learning.