---
title: What is big data analytics?
url: https://www.vmware.com/topics/big-data-analytics
description: Learn how big data analytics helps organizations turn raw data into business insights.
---

# What is big data analytics?

Big data analytics is the process of analyzing vast amounts of complex data to derive actionable business insights, such as market trends, customer preferences, and other correlations and hidden patterns. Organizations rely on big data analytics to support strategic, data-driven decisions, applying sophisticated technologies, such as behavioral and machine learning algorithms, to data generated from a wide variety of sources, including web, email, social media, mobile, and the Internet of Things (networked devices, sensors, etc.).

## Why organizations need big data analytics

Organizations today are drowning in data but starving for insights. The ability to analyze all that data at scale greatly expands the questions organizations can ask and answer about all aspects of their business, enabling them to convert raw information into decisive action.

Key benefits provided by big data analytics include:

- **Faster, better-informed decisions:** Large datasets reveal patterns and correlations that smaller samples miss, giving decision-makers a more complete and accurate picture.
- **Proactive risk management:** Predictive models surface threats and vulnerabilities before they materialize, shifting organizations from reactive response to preventive action.
- **Operational efficiency:** Analytics identifies redundancies, bottlenecks and waste across complex environments, enabling teams to do more with existing resources.
- **Customer and market intelligence:** Behavioral data at scale uncovers preferences, trends and opportunities that inform product enhancements and more targeted engagement.

## The history of big data analytics and the role of cloud computing

Data has always been a business asset, but prior to big data analytics, businesses didn’t have the capacity or the infrastructure needed to extract its full value.

### How we got here

In the early 2000s, the explosion of internet activity, e-commerce, and connected devices pushed data volumes beyond what traditional databases could handle. Open-source frameworks like Hadoop emerged to address the problem, enabling distributed storage and processing across commodity hardware.

Spark, NoSQL databases, and data lakes came next to further extend capacity, but deployment remained complex and resource-intensive. Only large enterprises with dedicated infrastructure teams could adopt them.

### The cloud inflection point

Cloud computing changed the economics of big data. By abstracting infrastructure complexity and shifting to consumption-based pricing, cloud platforms made enterprise-grade analytics accessible without the capital investment of on-premises buildouts. Storage became elastic, processing became on-demand, and the integration of pre-built machine learning services lowered the barrier to advanced analysis.

## Big data vs. traditional data: The five Vs

To fully understand big data and analytics, you first need to recognize what makes big data fundamentally different from traditional data, and why traditional databases and compute resources are often insufficient for the required processing.

Traditional data relies on structured, clean information stored in relational databases and managed with standard SQL. In contrast, big data handles vast amounts of complex, unstructured information, requiring distributed processing systems and machine learning to uncover hidden, predictive patterns.

The scope of the challenges and the opportunities presented by big data analytics are defined by the following dimensions (the five Vs):

- **Volume:** Enterprises today collect data from thousands of endpoints, transactions and sensors, generating datasets far too large for conventional storage and processing systems to handle.
- **Velocity:** The speed at which data is generated, from real-time event logs to continuous sensor feeds, demands infrastructure that can ingest and process it in real time.
- **Variety:** Big data comes in many forms: structured records, unstructured text, images, video, and machine-generated logs. Effective analytics requires systems that can normalize and analyze all of it.
- **Veracity:** Veracity refers to data accuracy and consistency. Any data processing needs to filter out noise, resolve conflicts, and ensure that the insights derived are based on reliable inputs.
- **Value:** Value is the measure of whether big data analytics translates into better decisions, reduced risk, or meaningful operational improvements.

![5Vs](https://www.vmware.com/media/blt8c9a8aaca0ffd4ac/blt4fedc1f0510ec450/6a5fb653dd5f0390f3e4dd8d/5Vs.png)

## Types of big data analytics

Big data analytics uses a progression of methods to convert raw data into recommendations. Each method builds on the last, moving from understanding what happened to determining what to do next.

- **Descriptive analytics:** As the starting point, descriptive analytics summarizes historical data to establish a baseline. It turns raw data into readable patterns to answer starting point questions such as what happened, when, and at what frequency.
- **Diagnostic analytics:** Diagnostic analytics drills into those patterns to identify root causes, correlations, and contributing factors behind a given outcome.
- **Predictive analytics:** With a clear picture of the past, predictive analytics in big data uses statistical models and machine learning to forecast what's likely to happen next.
- **Prescriptive analytics:** Prescriptive analytics is the final and most advanced form. It recommends specific actions to influence outcomes, weighing variables and tradeoffs to guide better decisions.

![Types of Big Data Analytics](https://www.vmware.com/media/blt8c9a8aaca0ffd4ac/blt275bc793c12ec513/6a5fb07682d5738b4b785f29/Types_of_Big_Data_Analytics.png)

## Big data analytics use cases

Big data analytics is embedded in critical workflows across virtually every industry. Security applications lead the way, but the underlying capabilities extend across domains.

- **Endpoint security:** Detecting known and unknown threats by correlating telemetry across millions of devices, flagging anomalous behavior, and accelerating incident response.
- [Threat intelligence:](https://www.broadcom.com/topics/threat-intelligence): Aggregating data from global sources to identify emerging attack patterns and inform defensive posture in real time.
- **Financial services:** Fraud detection, credit risk modeling, and real-time transaction monitoring at massive scale.
- **Healthcare:** Patient outcome analysis, clinical research, and operational planning across large, complex datasets.
- **Retail and e-commerce:** Demand forecasting, inventory optimization, and personalized customer experiences driven by behavioral data.
- **Manufacturing:** Predictive maintenance, supply chain visibility, and quality control through continuous sensor data analysis.
- **Telecommunications:** Network performance monitoring, churn prediction, and capacity planning across high-volume infrastructure.

## The future of big data analytics: Where the market is heading

Even as big data tools and capabilities have become table stakes, current trends reflect a market moving toward greater automation, accessibility and intelligence:

- **AI-augmented analytics:** Machine learning is increasingly embedded in the analytics layer, automating pattern detection, anomaly identification, and insight generation at scale.
- **Real-time and streaming analytics:** Batch processing is giving way to continuous analysis, as organizations demand insights derived from data in motion, not just data at rest.
- **Data fabric and mesh architectures:** Enterprises are moving away from centralized data lakes toward distributed, domain-oriented data ownership models that improve governance without sacrificing accessibility.
- [Multi-cloud](https://www.vmware.com/topics/multi-cloud) **and** [hybrid](https://www.vmware.com/topics/hybrid-cloud) **deployments:** Workloads increasingly span complex, hybrid environments, from on premises to private cloud to multiple public clouds, requiring big data analytics solutions that operate consistently across all of them.

The organizations best positioned to capitalize are those that treat analytics infrastructure as a core operational capability.

## How VMware products can help

[VMware Tanzu Greenplum](https://www.vmware.com/products/app-platform/tanzu-data-intelligence/greenplum) is a data warehouse, analytics and AI platform that allows you to unify all your data, transforming it into actionable insights and maintaining a single source of truth. The platform can support your big data analytics efforts with:

- **Speed and scale:** In-database analytics and AI, plus query and data ingestion for petabyte-size data sets
- **Productivity:** Diverse data types on a single platform (structured, semi-structured, unstructured, vector or geospatial graph), wherever the data is located
- **Flexibility:** Deployment on any infrastructure type with optimizations for bare metal, [public cloud](https://www.vmware.com/topics/public-cloud), and [vSphere-based](https://www.vmware.com/products/cloud-infrastructure/vsphere) [private cloud](https://www.vmware.com/topics/private-cloud)
- **Resilience:** Based on OSS Postgres, a time-tested and proven platform with features including redundant components, remote [disaster recovery](https://www.vmware.com/topics/disaster-recovery), enhanced security, and 24x7 enterprise support.

## Recommended for You

- [Application Modernization](https://www.vmware.com/topics/application-modernization)
- [Cloud Scalability](https://www.vmware.com/topics/cloud-scalability)
- [Cloud Economics](https://www.vmware.com/topics/cloud-economics)
- [Distributed Cloud](https://www.vmware.com/topics/distributed-cloud)

## Related Products and Solutions

- [VMware Tanzu Greenplum](https://www.vmware.com/products/app-platform/tanzu-data-intelligence/greenplum)
- [VMware Tanzu Data Intelligence](https://www.vmware.com/products/app-platform/tanzu-data-intelligence)
- [VMware Tanzu Data Lake](https://www.vmware.com/products/app-platform/tanzu-data-intelligence/data-lake)

## Also of Interest

- [What is Platform Engineering?](https://www.vmware.com/topics/platform-engineering)
- [What is Network Traffic Analysis?](https://www.vmware.com/topics/network-traffic-analysis)
- [What is Application Analytics? Definition & FAQs](https://www.vmware.com/topics/application-analytics)