Big Data: Definition, Examples, and Types

How big is really big?

What if you wake up one day and find an army of stegosaurus outside your house?

You'd run for your life. But where will certain businesses hide when they encounter truckloads of big data?

On any normal day, a business is exposed to different variants of complex data sets in the name of big data. It can be either clickstreams on their website, social media likes and shares, or hard information like machine setup time, machine model number, model type, and engine information. Arrangements and labeling this data with big data software are important to make future business predictions.

Be it any data, from raking in social media traffic, engineering data, or hard data like production cost, setup times, and inventory tracking, it can be fed to high-performing machine learning algorithms in ERP applications to make products function smoothly.

To understand the sheer scale of big data, we first need to look into its history and how far we have come in such a short period of time.

History of big data

The practice of gathering and storing large amounts of information and attempting to make sense of that information has been around for centuries. For example, the U.S. Census Bureau started recording population data on punch cards in 1790, creating about 500 punches a day. Fast forward 100 years, the “Tabulating Machine” processed information on these punch cards hundreds of times faster than humans could.

The early traces of big data can be found in the finance sector. With the growth of financial data during economic liberalization, many financial firms learned to use big data to their advantage. Risk numbers, credit scores, bank statements, and general ledgers came under the criteria of big data, which were managed using relational databases.

In 2005, social media apps like Facebook, Netflix, and Twitter presented a new angle to big data. A lot of video content was now streamed live and distributed to the audience to cater for engagement. Social engagement was a real-time insight into consumer behavior and sentiment, leading to the expansion of big data. 

In March 2009, Apache launched Cassandra, a highly scalable and cross-functional No-SQL database to manage, store and retrieve big data. It was designed to handle large amounts of data across ERPs and commodity servers without any risk of failure. Apache launched a second open-source database management platform, Hadoop, 

With the 2011 launch of Hadoop by Apache, a powerful open-source framework for storing large databases and running applications. Hadoop is a multi-cloud environment that syncs with cloud environments to protect and secure big data.

The Internet of Things (IoT) revolutionized big data in 2014. In an internet-connected world, more businesses decided to shift spending toward big data to reduce operational costs, boost efficiency, and develop new products and services.

 Now, the scope of big data is nearly endless. Researchers in scientific domains use real-time data to look at electricity consumption, pollution, traffic, and much more. Emerging technologies like artificial intelligence and machine learning are harnessing big data for future automation and helping humans unveil new solutions.

These milestones were made possible when the world decided to go digital.

Six V’s of big data

Born in the financial and economic sector, big data slowly began its renaissance into other sectors like e-commerce, automotive, supply chain, and logistics. Mostly, the occurrence of big data depends on six influential factors.

1. Volume

Big data is classified as a huge volume of low-density, unstructured data that needs to be treated, programmed, and validated. Organizations deal with terabytes, zettabytes, and petabytes of data from different attributes like social, consumer channels, engineering, product, quality assurance, and so on.

There’s a lot of data out there -- an almost incomprehensible amount. According to the latest estimates, 328.77 million terabytes of data are generated, monitored, and consumed every day. If you put this number into perspective, it is like traveling across the entire milky way galaxy.

If you think these numbers are incomprehensible, get a load of this; A report commissioned by Seagate and performed by IDC estimates that by 2025, the digital universe will reach 163 zettabytes of data or 163 trillion gigabytes.

2. Velocity

Velocity is the rate at which data is transmitted over mobile and LAN networks. With the increase in technologies like the Internet of Things and 5G edge computing, data can be transmitted over large premises. It is converted into digital signals and transmitted over transmission control protocol (TCP) or internet protocol (IP) providers. The recipient converts digital into analog and writes it out on disk or stores it in memory.

The rate at which the Internet of Things (IoT) is automating major operations in the world is dumbfounding. According to a G2 stat article, 55.7 billion connected IoT devices will generate almost 80 zettabytes of data. 

I love analogies. So for me, the big data universe is expanding much like our physical universe of stars, planets, galaxies, and dark matter.

Big data technologies and metadata (data about data) paired with different types of AI and machine learning will be used to their full potential to make the universe a self-assist machine.

3. Value

Big data needs to be highly valuable to the business's cause. Whatever influx you generate needs to sync with your overall ERP implementation. This data will answer all your business problems in the long run. Databases should be storable, cloud compliant, retrievable, and shareable with external stakeholders. Data is a complicated road to tread. Sometimes, valuable data can be mistaken for outliers due to their unstructured form. It is imperative to derive its complete value to ensure you don't lose out on even a grain of valuable data. This can be done through machine learning software or cross-training product and data teams. 

Value is the most straightforward V of big data. It asks, “How can we use this data to extract something meaningful for our users and the business?” Big data won’t bring much value if it’s being analyzed without purpose.

Trust a reliable data attribution source while collecting data for your organization. Your data needs to tell a story about your organization’s value in the consumer market. Reciprocation of consumers and their preference for your brand in terms of website cookies, likes, comments, and shares are what you need to work on to predict future brand trends.

4. Veracity 

High velocity, high quality, and highly scalable datasets are only preferred for making optimal business decisions. Only highly attainable and tangible data can be fed as training input data and produce meaningful results. 

Veracity refers to the accuracy of data. Not all data is precise or consistent, and with the growth of big data, it’s becoming harder to determine which data actually brings value. A good example of inconsistent data is social media data, which is often volatile and trending one way or another. Consistent data would be weather forecasts, which are much easier to predict and track.

5. Variability

The most interesting trait of big data is that it is variable. A consumer can prefer one commodity but shift to a completely different purchase the next second. The subscription models or licenses on the internet change based on consumer interest. Determining how fast your big data spins is a great way to learn brand behavior.

For example, if you are predicting trends from a patient's medical record, a piece of data may align with the salts prescribed to them for a current set of symptoms they're facing. The medical history may be a composition of n number of clinical salts the patient might have consumed over the years. To study the course of the next possible round of diagnosis, you need to process and treat old data. The variability of medical data helps create nanobots, a growing healthcare and medical science era today. 

6. Variety

Variety of big data refers to the structured, unstructured, and semi-structured big data that is stored in data lakes and warehouses. It can be integers, arrays, strings, floats, doubles, or booleans. In the past, data could be collected from databases and spreadsheets, but now a huge tide of social media traffic has bought in heterogeneous data types. Likes, comments, shares, discounts, engagement, SMS, video, and audio formats are a few examples of alarming volumes of social data need additional processing to derive value.

How does big data work?

The big data market is accelerating at seriously mind-boggling speeds. In 2014, big data was just an $18.3 billion market.  According to the survey by Markets and Markets, with the rise of cloud storage and network connectivity, big data would see its biggest-ever jump in data volume. The revenue associated with big data was $162.1 billion in 2021 and is poised to reach $273.4 billion by 2026 at a CAGR of 11.0%.

One of the main reasons for this acceleration can be tied to the internet of things (IoT). For better or worse, humans are constantly engaged with internet-connected devices or remote automation that contribute to the constant flow of data. The IoT market size is expected to peak at $650.5 billion dollars by 2026, growing at a steady rate every year. 

The devices we own today, like smartphones, laptops, tablets, smart televisions, gaming consoles, smartwatches, your Amazon Echo, and even self-assist vehicles like Tesla Autopilot, will be standardized in the future. Technologies like object recognition and mixed reality would easily teleport a user between real and digital environments.

The hardware itself allows for more efficient ways to share data, but the real volume of big data comes from the ways we interact with these devices. For example, a wearable device, like a smartwatch, may gather all types of data on you. This device can track heart rate, steps, sleep quality, blood pressure, and SPO2 levels.

Easy availability of data also leads to cross-utilization between industries. Biometric pressure used to forecast weather conditions can be taken as variables by automobiles to design tornado-proof cars. Radioactive neurons used to design chemotherapy or other immunotherapies could also be a pharmaceutical drug a patient can consume as painless medication. 

As the big data unfurls and spreads its blanket, more machine learning and deep learning algorithms will use it to make fast, efficient, and accurate predictions. Its easy availability can stand out as a real challenge for the future of mankind.  

Types of big data

We know that the influx of more devices, platforms, and storage options will increase not only the volume of data but also the ways in which it can be stored, trained, and produced.

But not all data is created equal. By this, I mean that the way you’ll store and search for an ID number in a relational database is completely different than extracting traffic numbers for video content. 

One type of data is what we call structured, and another is called unstructured. But there’s also a third type of data called semi-structured. Let’s examine the differences between each data type.

Structured data

Structured data, for the most part, is highly organized in a relational database. Relational data is stored in form of (structured query language) SQL queries. If you needed to access a piece of information within the database, you could easily do so with a quick "select*from query.

Structured data is actually quite similar to machine language, or the only language a computer is capable of understanding. This type of data sits neatly in a fixed field within a record or file. It constitutes the first layer of database network architecture, where data is neatly managed and stored in large structured databases to create feature tables.

One of the most common examples of structured data is something you’d see in a spreadsheet. If you’re on the phone with a student loan representative and they ask you for your personal identification, chances are they’re working with structured data. These are dependent variables of an Excel sheet used to create data relationships and predicted values for regression.

Unstructured data

It would be nice if all data could be neatly structured, but human-generated data like photos on social media, voicemails, text messages, and more are highly unstructured and don't comply with one single data type.

As a matter of fact, 80-90 percent of all data is unstructured -- which makes sense why we’ve only been able to “tag” 3 percent of the world’s data. But what does unstructured refer to? It means data that isn’t easily identifiable by machine language and doesn’t conform to a standard database or spreadsheet.

You may be surprised, but most unstructured data is actually text-heavy. It can be a bout of comments made on a data poll, automated discount workflows running on an e-commerce website, and account-based marketing for every consumer preference. Whatever the metrics might be, it is hard to dissect them in order to gauge consumer interest and drive potential revenue.

There’s also machine-generated unstructured data, which is easier for machines to process. An example would be satellite images capturing weather forecasts or a brand running specific monthly subscription plans for which a consumer can go. 

Semi-structured data

The third type of data falls somewhere between structured and unstructured, also known as semi-structured data.

Things like XML sitemaps, RSS feeds, or emails are examples of semi-structured data because while they contain tags such as dates, times, website information, metadata, and sender/receiver information, their language isn’t structured. These documents contain textual information on basic attributes of any website, like domain registration, domain score, headers and subheaders, URLs (no-follow and do-follow), essential files for Google crawler, and so on. 

Types of big data analytics

Big data analytics is a way to extract features and data relationships from large volumes of data, sort them based on features, and use them in training modules to extract quick and accurate outputs. 

Companies nowadays use business intelligence software like Power BI to analyze important decisions, manage data sources and take supportive vendor actions. Vendor complaints and support data can also be tackled coherently with Power BI, which provides immersive insight into product drawbacks and failures. 

Big data analytics also looks at more raw data to uncover hidden patterns, market trends, and customer preferences to make informed predictions.

Descriptive analysis

The descriptive analysis technique creates simple reports, graphs, and other data visualizations which allow companies to understand what happened at a particular point. It’s important to note that descriptive analysis only pertains to events that happened in the past.

It happens to repurpose your data in probability distributions, alpha levels, confidence graphs, and bar charts to determine what action is influential and what hypothesis stands true to the data analysis. 

Diagnostic analysis

The diagnostic analysis technique gives deeper insight into a specific problem, whereas descriptive analysis is more of an overview. Companies can use diagnostic analysis to understand why a problem occurred. This analysis is a bit more complex and may even incorporate aspects of AI or machine learning.

Companies run full-length health diagnoses and monitoring of machine learning models to check their applicability in different business applications.  Due to the original pitfalls and resource consumption in the diagnosis stage, companies are opting for machine learning operationalization of MLOps to run full-fledged ML automation that saves time, bandwidth, cost, and resources.

Predictive analytics

Predictive analytics is a shorthand for the machine learning algorithm since it converts expected trends into observed data. It is the language used by business analysts to describe the uncovering of data trends, datasets, and decisive techniques to make business predictions.

Predictive analytics is a form of advanced analytics that spots trends and anomalies in data to balance outputs. For example, in disaster forecasting, predictive analysis can measure the temperature of tectonic plates, biometric pressure, and other related factors to predict the occurrence of earthquakes.

By pairing advanced predictive algorithms with AI and machine learning, companies may be able to predict what will likely happen next. Being able to give an informed answer about the future can bring a ton of value to a business. Predictive analytics is useful for demand forecasting, risk planning, and disaster recovery. 

Prescriptive analysis

The prescriptive analysis technique is extremely complex, which is why it is not yet widely incorporated. While other analytic tools can be used to draw your own conclusions, the prescriptive analysis provides you with actual answers. A high level of machine learning maturity and infrastructure bandwidth is needed for these reports.

Big data examples

Data is entwined in nearly every part of our society nowadays. Whether it’s a user updating their Facebook status through a mobile device, or a business harnessing data to improve product functionality, we’re all contributing to the universe of big data.

In a Tableau-sponsored report by the Economist Intelligence Unit, 76 percent of respondents said data analytics helps them make better decisions. More data-driven companies across all industries are constantly emerging. Here’s what some industries plan to do with all this data.

Telecommunications

With billions of mobile users worldwide, telecom is ripe for big data innovation. Using big data analytics, service providers could recover from a network outage faster by pinpointing its root cause with real-time data. Analytics can also be applied to discover more accurate and personalized ways to bill customers. Sentiment data from social media, geospatial data, and other mobile data can be used to offer targeted media and entertainment options.

Financial Services

More banks are moving away from being product-centric and are focusing on being customer-centric. Big data can help segment customer preferences through an omnichannel communication approach. The most obvious use of big data in financial services is fraud detection and prevention. Big data analytics and machine learning can study a customer’s tendencies and distinguish them from unusual account activities or behaviors.

The three most popular use cases of big data in financial and banking services are:

• Explosive data growth
• Fraud and risk detection
• Sales tax and compliance regulations 

Healthcare

We mentioned how smartwatch data could be used for personalized patient care and customized healthcare insurance rates. Predictive analysis can have phenomenal applications in the healthcare analytics industry – allowing for earlier diagnosis of diseases and painless ways to provide treatment. Taking a sneak peek into an erstwhile patient's background, history of allergies and diseases, and treatment cycles can design a prolonged diagnosis for a patient with better chances of recovery from a disease. 

Look at how blood-swimming nanobots injected into medical capsules will travel through an endoscopic tract of humans and kill affected cells.

                                                                                 Source: G2

Education

One educational model doesn’t suit all students. Some are visual learners; others are audio learners. Some prefer online, while others thrive during in-person lectures. Big data analytics can be used to build more customized learning models for all students. Big data is also used on college campuses to reduce dropout rates by identifying risk factors for students falling behind in their classes.

Big data builds experiential learning environments to train students in real-time by clubbing physical and digital environments into one 3D simulation. Look at this example:

                                                                               Source: NTLTP

Future of big data

The big data market has undergone massive growth for a reason. More companies are realizing the importance of taking a data-driven marketing and overall business approach not only for internal processes but also for improving the experiences of their customers.

Emerging technologies like AI, machine learning, and NLP utilize big data to break ground on new products, user experiences, cost efficiencies, and more.

So where do we go from here? What is the future of big data? Though the picture isn’t fully clear, we do have some ideas.

Going off of IDC’s research, we can predict that IoT is driving most of this growth. By 2025, the total base of installed IoT units will touch 30.9 billion units, a massive increase from 13.8 billion units in 2021!  Home and network automation will touch a new high, binding the global workforce into one hypersphere of shared data.

One of the main reasons for this spike in interactions is the rise of voice recognition and conversational UI. Do you enjoy chatting with Siri or Alexa? Good news: prepare to make many more of these friends in the near future.

But IoT won’t just increase user-to-device interactions; it’ll also play a crucial role in machine-to-machine (M2M) interactions. Sensors will be a driving technology linking machines to the internet. We’ll use data from M2M interactions to monitor the human impact on the environment, forest fires, earthquakes, and other forces of nature.

While big data will still be crucial for sales, marketing, and product development, the stakes are higher when we rely on data for things like self-driving cars or automated mass transit. For this dream to become a reality, the data veracity of different business strategies and opportunity plans needs to be captured, analyzed, and translated into decisions.

"Big" is an understatement for data

The emergence of big data has put customer-centricity at the forefront. Big data is helping businesses make faster, more calculated decisions. Using big data analytics, we can predict where future problems and how to tackle them with agile solutions. This has surely put us on a roadmap of accelerated innovation.

Learn how data warehousing tackles customer grievances and helpdesk escalations much more efficiently than traditional query management systems. 

This article was originally published in 2018. The content has been updated with new information.