Big data refers to large and complex datasets that contain structured, semi-structured, or unstructured data—and it's generated continuously, across every part of a business, by people and machines. A single company can produce billions of data points every day, which is far more than traditional databases and tools were ever built to handle. So the challenge isn't producing the data—it's storing, organizing, and analyzing that data with specialized tools to enable better data-driven decision-making.
Key takeaways
- The value of big data comes from its quality and analysis, not the sheer volume of it.
- Big data, such as emails, images, support calls, and sensor feeds can’t be stored in traditional, relational databases because most of it doesn’t fit neatly in rows and columns.
- Storing big data and using it are two different things—data lakes and warehouses archive data at rest, while operational databases like MongoDB act on it in real time, catching fraud or generating recommendations.
- Clean data is what makes big data usable—duplicates, gaps, and errors lead to confident, wrong answers, which is why data teams spend most of their time preparing data before they ever analyze it.
Table of contents
- What makes data “big” data?
- What are the five Vs of big data?
- How are structured, semi-structured, and unstructured data used in big data?
- Why can’t traditional data processing tools handle big data?
- What is big data used for?
- What are the challenges of big data?
- How does MongoDB handle big data?
- How to get started with big data?
- FAQs
- Related resources
What makes data “big” data?
Big data refers to data that’s too large, moves too fast, or is too diverse for traditional data processing tools to store and process. There is no benchmark that signifies when data turns into big data. Several hundred gigabytes of messy, fast-moving data can qualify as big data while many terabytes of clean, static data won’t. The tell-tale sign is when ordinary tools—a relational database on a single server, a spreadsheet, or an overnight report—can’t keep up.
Big data vs. big data analytics: Big data and big data analytics are easy to confuse, but they’re not the same thing. Big data is the raw material, while big data analytics is the work of collecting, cleaning, and analyzing it to find patterns. This guide is about big data itself. For a detailed look at big data analytics, see What Is Big Data Analytics?
What are the five Vs of big data?
Big data is usually described by five characteristics, the five Vs of big data: volume, velocity, variety, veracity, and value. The first four describe what makes big data hard to store and manage. The fifth—value—describes what you get when you manage the first four properly.
Volume: Volume is how much data there is. The world creates around 402 million terabytes of data every day (Statista)—and every transaction, click, sensor reading, and support call adds to a pile that's far more than any one server can hold.
That kind of volume can't be handled by adding a bigger hard drive or a faster server; it needs to be spread across multiple machines.
Velocity: Velocity is how fast data arrives. Nearly 30% of the world's data is now generated in real time (IDC): a payment swipe, GPS data, a sensor reading on a factory line all arrive in constant data streams. The faster data comes in, the less time there is to respond—whether that's catching fraud, flagging a failing machine, or adjusting a price. So for time-sensitive data uses like fraud detection or personalization, big data must be processed as it arrives, not hours later in an overnight batch.
Variety: Roughly 80–90% of the data a business collects today is unstructured, and it's growing about three times faster than the neat, structured kind (Gartner). Scanned contracts, product photos, social media, call-center audio, support chats, and sensor logs pile up faster than most businesses can sort them into rows and columns. This diversity in data format—and the difficulty of storing and using it—are the main reasons why big data needs different tools.
Veracity: Veracity is about whether you can trust the data or not. Poor data quality costs the average organization $12.9 million a year (Gartner). Big data is messy by default—duplicates, gaps, typos, records from two systems that don't agree—and the errors aren’t easy to find.
Bad data doesn’t flag itself; that’s what makes it dangerous. It produces answers that look confident but are wrong, so every decision built on it inherits the mistake.
Value: Value is the payoff of carefully managing the other four Vs—catching fraud before it hits, retaining customers, and spotting trends early enough to get ahead of them. Good data matters more than lots of data.
How are structured, semi-structured, and unstructured data used in big data?
Big data uses three types of data—traditional structured data, semi-structured data, and unstructured data—and each type plays a different role.
Most big data is unstructured, and extracting insight from it—customer sentiment, buying patterns, why customers cancel—takes machine learning, like natural language processing for text and image analysis for the rest. The biggest payoff often comes from combining it with structured data: sales records show a customer left, the support-call transcript shows they were frustrated with billing.
Why can’t traditional data processing tools handle big data?
Traditional databases can't handle big data because they were built for a different kind of data—smaller, uniform, and predictable. Big data is none of those things: it comes in too many formats to fit a fixed structure, and it's too large for any single machine.
Even with cloud computing, modern storage, and the analytical tools businesses depend on, getting value out of big data is hard for many organizations. Making it usable isn't just about buying more storage—the tools, the skills, and the underlying data architecture all have to change.
Problem #1: A fixed structure
A traditional, relational database has a locked-in schema—rigid columns and specific data types that work for uniform records but struggle with big data and complex data. That’s okay when all the data looks the same, but when one customer’s profile only contains structured data (name, email, order history), and another adds unstructured data like video reviews, support-call recordings, and chat messages, they can’t be stored in the same way.
So either the database gets rebuilt every time the data changes, or the pieces that didn’t fit the rigid columns—video, audio, chat logs—get left behind.
Problem #2: A single machine
The usual fix for more data is a bigger server (more memory, more processing power), but a single machine eventually runs out of space, and bigger ones are costly. Big data needs to spread across many standard servers—adding capacity by adding machines rather than replacing them with bigger ones.
Most traditional data management systems weren’t built for that. They pull answers by joining related tables together, and when those tables sit on different machines, every query has to reach across the network—which is slow and only gets slower as you add machines. Organizations need infrastructure that can store data efficiently as data volume grows.
DEEP DIVE
Adding power to one server is called vertical scaling; spreading data across many is horizontal scaling. Often, relational databases scale vertically, which is why they hit a ceiling with big data. NoSQL databases like MongoDB scale horizontally through sharding—automatically splitting a large dataset across many servers so no single machine holds all the data. This works because related data usually lives together in one document, so fewer cross-table joins are needed.
What is big data used for?
Big data powers the tools people use every day, across most every industry. Below are four of the most common uses.
Catching fraud: Uncovering fraud as it’s happening—and stopping it—means scanning and analyzing millions of transactions at once to identify patterns. Anything that doesn’t fit gets flagged: an unfamiliar location, a sudden run of activity, or a large purchase. Each one has to be checked in milliseconds before it’s cleared.
Predicting failures and bottlenecks: Manufacturers read sensor data off their equipment to catch the early signs of a breakdown and service it before it fails. The same analysis finds bottlenecks in a supply chain and smooths out logistics, which is where a lot of the operational efficiency and savings come from.
Personalizing healthcare: Big data lets hospitals see patterns no single chart would reveal. By combining records, monitoring devices, and managing test results across thousands of patients, they can flag who's likely to be readmitted, who's at risk for a complication, and who'd benefit from a different treatment.
Recommending what’s next: Every "you might also like" result runs on big data. By reading what millions of people click, watch, and purchase, apps can show users the next thing in real time—a related product, the next episode, or the songs that might keep them listening.
THE PAYOFF
The payoff is measurable. Companies that pair big data with AI report beating their peers on operational efficiency (81% versus 58%), revenue growth (77% versus 61%), and customer experience (77% versus 45%) (Harvard Business Review). The same approach powers predictive models in healthcare, from forecasting patient risk to tailoring treatment. For more, see big data examples.
What are the challenges of big data?
The challenges of big data are cost, a shortage of skilled talent, data trapped in silos, security and privacy risk, and keeping pace with changing technology. None of these are reasons to avoid big data—but each one takes planning.
- The costs keep climbing. Storage, processing, and the skilled professionals to run them all add up, and the billing is ongoing. Cloud computing lowers the upfront costs, but it doesn't make it free.
- The talent is scarce. Skilled data scientists, data engineers, and data analysts are in short supply and expensive to keep. Much of the work is unglamorous, too—data scientists spend much of their time preparing data before it's usable.
- Data gets trapped in silos. When data comes from hundreds of data sources, it ends up scattered across disconnected systems—the same customer in two places, with numbers that don't match. Pulling data from multiple sources together is slow, and often the biggest barrier to using big data.
- Security and privacy raise the stakes. Pulling all that sensitive data into one place makes it a bigger target, which is why security concerns rank near the top for big data teams. It also has to follow privacy rules that vary by region and keep changing—and building protection in early is cheaper than adding it later.
- The technology keeps moving. A decade ago, Hadoop was the standard for storing and processing data; then Apache Spark, known for fast in-memory processing; now it's an ever-changing mix of big data tools. Choosing what fits today and what's ahead is ongoing work.
None of these are reasons to avoid big data—they're reasons to be deliberate about how it's stored and managed. The right database removes a lot of the friction, which is where an operational platform like MongoDB comes in.
DEEP DIVE
The amount of data the world produces is growing exponentially — by most estimates, doubling every few years. Storage isn't a one-time purchase but a moving target, which is why big data systems are built to expand by adding servers, not replacing them.
How does MongoDB handle big data?
MongoDB is a NoSQL database built for big data in two ways. It stores structured and unstructured data together in flexible, JSON-like records—so data that doesn’t fit into a rigid schema isn't a problem. And sharding spreads a dataset across many servers, so volume that might overwhelm a single machine will be easily handled.
From there, MongoDB's aggregation pipeline processes operational data directly within a single query, while MongoDB Vector Search enables semantic search and AI-powered features like retrieval-augmented generation (RAG) on that same data—without requiring syncs to a separate system.
MongoDB is an operational database, which is a different job from storage. Most companies also use more than one kind of data store: a data lake is a low-cost store that holds massive amounts of raw structured, semi-structured, and unstructured data in whatever format it arrives, and scales easily as the volume grows; a data warehouse cleans and organizes it for reporting and heavy analysis; and a data lakehouse combines the two. All three are built to analyze data at rest. MongoDB works differently: it acts on data while it’s still moving, and can run real-time analytics on that live data instead of waiting for a batch job.
How to get started with big data
Getting started with big data comes down to one rule: start with the business goal, not the technology. Decide what you actually want the data collected across your systems to do, so spending on big data technologies, people, and infrastructure goes toward real priorities instead of tools you may not need.
Once the goal is clear, many big data organizations set up a center of excellence—a small central team that shares skills and standards across the company, so every group isn't solving the same problems alone. The hardest part is usually cultural: building a data-driven culture where decision-making comes from data and evidence instead of gut instinct, and where business users can get the data they need. The change is toughest when legacy policies and long-standing attitudes are embedded in the organization.
The fastest way to see how a flexible database handles big data is to try one. Spin up a free MongoDB Atlas cluster—no credit card required.