While enterprise organizations have long collected data, the arrival of the internet and other connected technologies significantly increased the volume and variety of data available, birthing the concept of “big data.”

Today, businesses collect large amounts of data—often measured in terabytes or petabytes—on everything from customer transactions and social media impressions to internal processes and proprietary research.

Over the past decade, this information has fueled digital transformation across industries. In fact, big data has earned the nickname “the new oil” for its role driving business growth and innovation.

Data science and, more specifically, big data analytics help organizations make sense of big data’s large and diverse data sets. These fields use advanced tools such as machine learning to uncover patterns, extract insights and predict outcomes. 

In recent years, the rise of artificial intelligence (AI) and machine learning has further increased the focus on big data. These systems rely on large, high-quality datasets to train models and improve predictive algorithms.

Traditional data and big data differ mainly in the types of data involved, the amount of data handled and the tools required to analyze them.

Traditional data primarily consists of structured data stored in relational databases. These databases organize data into clearly defined tables, making it easy to query using standard tools like SQL. Traditional data analytics typically involves statistical methods and is well-suited for datasets with predictable formats and relatively small sizes.

Big data, on the other hand, encompasses massive datasets in various formats, including structured, semi-structured and unstructured data. This complexity demands advanced analytical approaches—such as machine learning, data mining and data visualization—to extract meaningful insights. The sheer volume of big data also requires distributed processing systems to handle the data efficiently at scale. 

The "V's of Big Data"—volume, velocity, variety, veracity and value—are the five characteristics that make big data unique from other kinds of data. These attributes explain how big data differs from traditional datasets and what’s needed to manage it effectively. 

Big data is “big” because there’s more of it. The massive amount of data generated today—from web apps, Internet of Things (IoT) devices, transaction records and more—can be hard for any organization to manage. Traditional data storage and processing systems often struggle to handle it at scale.

Big data solutions, including cloud-based storage, can help organizations store and manage these ever-larger datasets and make sure valuable information is not lost to storage limits.

Velocity is the speed at which data flows into a system and big data moves quickly.

Today, data arrives faster than ever, from real-time social media updates to high-frequency stock trading records. This rapid data influx provides opportunities for timely insights that support quick decision-making. To handle this, organizations use tools like stream processing frameworks and in-memory systems to capture, analyze and act on data in near real-time.

Variety refers to the many different formats that big data can take.

Along with traditional structured data, big data can include unstructured data, such as free-form text, images and videos. It can also include semi-structured data, such as JSON and XML files, that have some organizational properties but no strict schema.

Managing this variety requires flexible solutions like NoSQL databases and data lakes with schema-on-read frameworks, which can store and integrate multiple data formats for more comprehensive data analysis. 

Veracity refers to the accuracy and reliability of data. Because big data comes in such great quantities and from various sources, it can contain noise or errors, which can lead to poor decision-making.

Big data requires organizations to implement processes for ensuring data quality and accuracy. Organizations often use data cleaning, validation and verification tools to filter out inaccuracies and improve the quality of their analysis.

Value refers to the real-world benefits organizations can get from big data. These benefits include everything from optimizing business operations to identifying new marketing opportunities. Big data analytics is critical for this process, often relying on advanced analytics, machine learning and AI to transform raw information into actionable insights.

The term "big data" is often used broadly, creating ambiguity around its exact meaning.

Big data is more than just massive amounts of information. Rather, it is an intricate ecosystem of technologies, methodologies and processes used to capture, store, manage and analyze vast volumes of diverse data.

The concept of big data first emerged in the mid-1990s when advances in digital technologies meant organizations began producing data at unprecedented rates. Initially, these datasets were smaller, typically structured and stored in traditional formats.

However, as the internet grew and digital connectivity spread, big data was truly born. An explosion of new data sources, from online transactions and social media interactions to mobile phones and IoT devices, created a rapidly growing pool of information.

This surge in the variety and volume of data drove organizations to find new ways to process and manage data efficiently. Early solutions like Hadoop introduced distributed data processing, where data is stored across multiple servers, or "clusters," instead of a single system.

This distributed approach allows for parallel processing—meaning organizations can process large datasets more efficiently by dividing the workload across clusters—and remains critical to this day.

Newer tools like Apache Spark, the open-source analytics engine, introduced in-memory computing. This allows data to be processed directly in the system's main memory (RAM) for much faster processing times than traditional disk storage reading.

As the volume of big data grew, organizations also sought new storage solutions. Data lakes became critical as scalable repositories for structured, semi-structured and unstructured data, offering a flexible storage solution without requiring predefined schemas (see “Big data storage” below for more information).

Cloud computing also emerged to revolutionize the big data ecosystem. Leading cloud providers began to offer scalable, cost-effective storage and processing options.

Organizations could avoid the significant investment required for on-premises hardware. Instead, they could scale data storage and processing power up or down as needed, paying only for the resources they use. 

This flexibility democratized access to data science and analytics, making insights available to organizations of all sizes—not just large enterprises with substantial IT budgets.

The result is that big data is now a critical asset for organizations across various sectors, driving initiatives in business intelligence, artificial intelligence and machine learning.

Big data management is the systematic process of data collection, data processing and data analysis that organizations use to transform raw data into actionable insights.

Central to this process is data engineering, which makes sure that data pipelines, storage systems and integrations can operate efficiently and at scale.

This stage involves capturing the large volumes of information from various sources that constitute big data.

To handle the speed and diversity of incoming data, organizations often rely on specialized big data technologies and processes such as Apache Kafka for real-time data streaming and Apache NiFi for data flow automation.

These tools help organizations capture data from multiple sources—either in real-time streams or periodic batches—and make sure it remains accurate and consistent as it moves through the data pipeline.

As data flows into structured storage and processing environments, data integration tools can also help unify datasets from different sources, creating a single, comprehensive view that supports analysis.

This stage also involves capturing metadata—information about the data’s origin, format and other characteristics. Metadata can provide essential context for future organizing and processing data down the line.

Maintaining high data quality is critical at this stage. Large datasets can be prone to errors and inaccuracies that might affect the reliability of future insights. Validation and cleansing procedures, such as schema validation and deduplication, can help to address errors, resolve inconsistencies and fill in missing information.

Once data is collected, it must be housed somewhere. The three primary storage solutions for big data are data lakes, data warehouses and data lakehouses.

Data lakes are low-cost storage environments designed to handle massive amounts of raw structured and unstructured data. Data lakes generally don’t clean, validate or normalize data. Instead, they store data in its native format, which means they can accommodate many different types of data and scale easily.

Data lakes are ideal for applications where the volume, variety and velocity of big data are high and real-time performance is less important. They’re commonly used to support AI training, machine learning and big data analytics. Data lakes can also serve as general-purpose storage spaces for all big data, which can be moved from the lake to different applications as needed.

Data warehouses aggregate data from multiple sources into a single, central and consistent data store. They also clean data and prepare it so that it is ready for use, often by transforming the data into a relational format. Data warehouses are built to support data analytics, business intelligence and data science efforts.

Because warehouses enforce a strict schema, storage costs can be high. Instead of being a general-purpose big data storage solution, warehouses are mainly used to make some subset of big data readily available to business users for BI and analysis.

Data lakehouses combine the flexibility of data lakes with the structure and querying capabilities of data warehouses, enabling organizations to harness the best of both solution types in a unified platform. Lakehouses are a relatively recent development, but they are becoming increasingly popular because they eliminate the need to maintain two disparate data systems.

Choosing between lakes, warehouses and lakehouses depends on the type and purpose of the data and the business’s needs for the data. Data lakes excel in flexibility and cheap storage, whereas data warehouses provide faster, more efficient querying. Lakehouses combine features of the two but can be complex to set up and maintain.

Many organizations use two or all three of these solutions in combination. For example, a bank might use a data lake to store transaction records and raw customer data while utilizing a data warehouse to support fast access to financial summaries and regulatory reports.

Big data analytics are the processes organizations use to derive value from their big data. Big data analytics involves using machine learning, data mining and statistical analysis tools to identify patterns, correlations and trends within large datasets.

With big data analytics, businesses can leverage vast amounts of information to discover new insights and gain a competitive advantage. That is, they can move beyond traditional reporting to predictive and prescriptive insights. 

For instance, analyzing data from diverse sources can help an organization make proactive business decisions, like personalized product recommendations and tailored healthcare solutions.

Ultimately, decisions like these can improve customer satisfaction, increase revenue and drive innovation.

Organizations can use a variety of big data processing tools to transform raw data into valuable insights. 

The three primary big data technologies used for data processing include: 

• Hadoop
• Apache Spark
• NoSQL databases

Hadoop is an open-source framework that enables the distributed storage and processing of large datasets across clusters of computers. This framework allows the Hadoop Distributed File System (HDFS) to efficiently manage large amounts of data.

Hadoop’s scalability makes it ideal for organizations that need to process massive datasets on a budget. For instance, a phone company might use Hadoop to process and store call records across distributed servers for a more cost-effective network performance analysis.

Apache Spark is known for its speed and simplicity, particularly when it comes to real-time data analytics. Because of its in-memory processing capabilities, it excels in data mining, predictive analytics and data science tasks. Organizations generally turn to it for applications that require rapid data processing, such as live-stream analytics.

For example, a streaming platform might use Spark to process user activity in real time to track viewer habits and make instant recommendations.

NoSQL databases are designed to handle unstructured data, making them a flexible choice for big data applications. Unlike relational databases, NoSQL solutions—such as document, key-value and graph databases—can scale horizontally. This flexibility makes them critical for storing data that doesn’t fit neatly into tables.

For example, an e-commerce company might use a NoSQL document database to manage and store product descriptions, images and customer reviews.

Big data has transformed how organizations gather insights and make strategic decisions.

A study by Harvard Business Review found that data-driven companies are more profitable and innovative than their peers.^{1 O}rganizations effectively leveraging big data and AI reported outperforming their peers in key business metrics, including operational efficiency (81% vs. 58%), revenue growth (77% vs. 61%) and customer experience (77% vs. 45%).

Below are some of big data’s most significant benefits and use cases.

• Improved decision-making: Analyzing vast datasets allows organizations to uncover patterns and trends that lead to more informed decisions. For instance, a grocery chain can use sales data and weather forecasts to predict demand for seasonal products, helping to stock stores accordingly and reduce waste.

• Enhanced customer experience: Big data enables companies to understand customer behavior at a more granular level, paving the way for more tailored interactions. For instance, big data analytics can help identify customers who frequently buy skincare products from a specific brand. The brand can use this information to help target campaigns for limited-time sales or special offers on similar products.

• Increased operational efficiency: Real-time data allows organizations to streamline operations and reduce waste. In manufacturing, for example, organizations can analyze real-time sensor data to predict equipment failures before they occur. This process, known as predictive maintenance, can help prevent downtime and reduce maintenance costs.

• Responsive product development: Big data insights help companies respond customer needs and guide product improvements. For example, if multiple users report that a specific feature in a smartphone drains battery life too quickly, developers can prioritize optimizing that feature in the next software update.

• Optimized pricing: Big data enables organizations to refine pricing strategies based on real-time market conditions. For example, an airline can use insights derived from big data to adjust ticket prices dynamically, responding to demand shifts and competitor pricing.

• Enhanced risk management and fraud detection: Big data allows organizations to identify and monitor risks proactively. Banks, for instance, analyze transaction patterns to detect potential fraud. If a customer's credit card is used for an unusual high-value purchase in another country, the bank can flag the transaction and notify the customer for verification.

• Healthcare innovation: Healthcare providers can use big data to make sense of patient records, genetic information and data from wearable devices. For example, a continuous glucose monitor for a diabetic patient can track blood sugar levels in real-time, allowing healthcare providers to detect dangerous spikes or drops and adjust treatment plans accordingly. 

While big data offers immense potential, it also comes with significant challenges, especially around its scale and speed.

Some of the biggest challenges of big data include:

• Data quality and management: Connecting datapoints and keeping data accurate can be a complex undertaking especially with massive amounts of information constantly streaming in from social media, IoT devices and other sources. For example, a logistics company may struggle to integrate GPS data from its fleet with customer feedback and warehouse inventory to get a precise view of delivery performance.

• Scalability: As data grows, organizations must expand storage and processing systems to keep up. For instance, a streaming platform analyzing millions of daily viewer interactions may need to constantly add to its storage and compute power to handle demand. Cloud services can offer more scalable alternatives to on-premises solutions, but managing high volumes and velocities of data can still be difficult.

• Privacy and security: Regulations like GDPR and HIPAA require strict data privacy and security measures, such as strong access controls and encryption to prevent unauthorized access to patient records. Complying with these mandates can be tough when datasets are massive and constantly evolving.

• Integration complexity: Combining different types of data from multiple sources can be technically demanding. For instance, a retail chain may struggle to consolidate structured sales records with unstructured customer reviews and semi-structured supplier data for a comprehensive view of product performance.

• Skilled workforce: Big data work requires specialized skills in data science, engineering and analytics. Many organizations face ongoing challenges finding professionals like data analysts and other specialists who can manage and interpret large datasets. For example, a financial institution might struggle to hire data scientists skilled in both machine learning and financial modeling to analyze transaction data and predict market trends.

72% of top-performing CEOs agree that having a competitive advantage depends on having the most advanced generative AI. Such cutting-edge AI requires, first and foremost, large amounts of high-quality data.

Advanced AI systems and machine learning models, such as large language models (LLMs), rely on a process called deep learning.

Deep learning uses extensive, unlabeled datasets to train models to perform complex tasks such as image and speech recognition. Big data provides the volume (large data quantities), variety (diverse data types) and veracity (data quality) needed for deep learning.

With this foundation, machine learning algorithms can identify patterns, develop insights and enable predictive decision-making to drive innovation, enhance customer experiences and maintain a competitive edge.