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Big Data: What It Is, Why It Matters, How It Works | Built In
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# Big Data.

## Big Data: What It Is, Why It Matters, How It Works

## Big Data Definition

Big data refers to massive, complex data sets that are rapidly generated and transmitted from a wide variety of sources. Big data sets can be structured, semi-structured and unstructured, and they are frequently analyzed to discover applicable patterns and insights about user and machine activity.

Big Data Overview

Big Data Uses, Challenges, Technologies

History of Big Data

Big Data Overview

![data over city skyline](/sites/www.builtin.com/files/inline-images/big-data_1.jpg)Image: Shutterstock

## What Is Big Data?

Big data refers to large, diverse data sets made up of structured, unstructured and semi-structured data. This data is generated continuously and always growing in size, which makes it too high in volume, complexity and speed to be processed by traditional data management systems. Big data is used across almost every industry to draw insights, perform analytics, train [artificial intelligence](https://builtin.com/artificial-intelligence) and [machine learning](https://builtin.com/machine-learning) models, as well as help make data-driven business decisions.

## Why Is Big Data Important?

Data is generated anytime we open an app, use a search engine or simply travel place to place with our mobile devices. The result? Massive collections of valuable information that companies and organizations manage, store, visualize and analyze.

Traditional data tools aren’t equipped to handle this kind of complexity and volume, which has led to a slew of specialized [big data software platforms](https://builtin.com/big-data/big-data-platform) designed to manage the load.

Though the large-scale nature of big data can be overwhelming, this amount of data provides a heap of information for organizations to use to their advantage. Big data sets can be mined to deduce patterns about their original sources, creating insights for improving business efficiency or predicting future business outcomes.

As a result, big data analytics is used in nearly every industry to identify patterns and trends, answer questions, gain insights into customers and tackle complex problems. [Companies and organizations use the information](https://builtin.com/big-data/big-data-companies-roundup) for a multitude of reasons like automating processes, optimizing costs, understanding customer behavior, making forecasts and targeting key audiences for advertising.

## The 3 V’s of Big Data

Big data is [commonly characterized](https://journals.sagepub.com/doi/pdf/10.1177/2053951716631130) by three V’s:

### Volume

Volume refers to the huge amount of data that’s generated and stored. While traditional data is measured in familiar sizes like megabytes, gigabytes and terabytes, big data is stored in petabytes and zettabytes.

### Variety

Variety refers to the different types of data being collected from various sources, including text, video, images and audio. Most data is unstructured, meaning it’s unorganized and difficult for conventional data tools to analyze. Everything from emails and videos to scientific and meteorological data can constitute a big data stream, each with their own unique attributes.

### Velocity

Big data is generated, processed and analyzed at high speeds. Companies and organizations must have the capabilities to harness this data and generate insights from it in real-time, otherwise it’s not very useful. Real-time processing allows decision makers to act quickly.

## How Big Data Works

Big data is produced from multiple data sources like mobile apps, social media, emails, transactions or [Internet of Things (IoT)](https://builtin.com/internet-things) sensors, resulting in a continuous stream of varied digital material. The diversity and constant growth of big data makes it inherently difficult to extract tangible value from it in its raw state. This results in the need to use specialized big data tools and systems, which help collect, store and ultimately translate this data into usable information. These systems make big data work by applying three main actions — integration, management and analysis.

### 1\. Integration

Big data first needs to be gathered from its various sources. This can be done in the form of [web scraping](https://builtin.com/big-data/what-is-web-scraping) or by accessing databases, data warehouses, APIs and other data logs. Once collected, this data can be ingested into a big data pipeline architecture, where it is prepared for processing.

Big data is often raw upon collection, meaning it is in its original, unprocessed state. Processing big data involves cleaning, transforming and aggregating this raw data to prepare it for storage and analysis.

### 2\. Management

Once processed, big data is stored and managed within [the cloud](https://builtin.com/cloud-computing) or on-premises storage servers (or both). In general, big data typically requires [NoSQL databases](https://builtin.com/data-science/sql-vs-nosql) that can store the data in a scalable way, and that doesn’t require strict adherence to a particular model. This provides the flexibility needed to cohesively analyze disparate sources of data and gain a holistic view of what is happening, how to act and when to act on data.

### 3\. Analysis

Analysis is one the final steps of the big data lifecycle, where the data is explored and analyzed to find applicable insights, trends and patterns. This is frequently carried out using big data analytics tools and software. Once useful information is found, it can be applied to make business decisions and communicated to stakeholders in the form of data visualizations.

Big Data Uses, Challenges, Technologies

![IT professional walking among computer servers](/sites/www.builtin.com/files/inline-images/big-data-uses.jpg)Image: Shutterstock

## Uses of Big Data

Here are a few [examples of industries](https://builtin.com/big-data/big-data-examples-applications) where the big data revolution is already underway:

### Finance

Finance and insurance industries [utilize big data](https://builtin.com/data-science/finance-data-science) and predictive analytics for fraud detection, risk assessments, credit rankings, brokerage services and blockchain technology, among other uses. [Financial institutions](https://builtin.com/big-data/big-data-finance-banking-applications) also use big data to enhance their cybersecurity efforts and personalize financial decisions for customers.

### Healthcare

Hospitals, researchers and pharmaceutical companies [adopt big data solutions](https://builtin.com/big-data/big-data-in-healthcare) to improve and advance healthcare. With access to vast amounts of patient and population data, healthcare is enhancing treatments, performing more effective research on diseases like cancer and Alzheimer’s, developing new drugs, and gaining critical insights on patterns within population health.

### Education

Using [big data in education](https://builtin.com/big-data/big-data-in-education) allows educational institutions and professionals to better understand student patterns and create relevant educational programs. This can help in personalizing lesson plans, predicting learning outcomes and tracking school resources to reduce operational costs.

### Retail

[Retail utilizes big data](https://builtin.com/big-data/big-data-ecommerce-retail-companies) by collecting large amounts of customer data through purchase and transaction histories. Information from this data is used to predict future consumer behavior and personalize the shopping experience.

### Government

[Big data in government](https://builtin.com/big-data/big-data-examples-applications) can work to gather insights on citizens from public financial, health and demographic data and adjust government actions accordingly. Certain legislation, financial procedures or crisis response plans can be enacted based on these big data insights.

### Marketing

[Big data in marketing](https://builtin.com/big-data/big-data-marketing) helps provide an overview of user and consumer behavior for businesses. Data gathered from these parties can reveal insights on market trends or buyer behavior, which can be used to direct marketing campaigns and optimize marketing strategies.

### Media

If you’ve ever used Netflix, Hulu or any other streaming services that [provide recommendations](https://builtin.com/data-science/recommender-systems), you’ve witnessed big data at work. Media companies analyze our reading, viewing and listening habits to build individualized experiences. Netflix even uses data on [graphics, titles and colors](https://www.wired.com/insights/2014/03/big-data-lessons-netflix/) to make decisions about customer preferences.

## Big Data Challenges

### 1\. Volume and Complexity of Data

Big data is massive, complicated and ever growing. This makes it difficult in nature to capture, organize and understand, especially as time goes on. In order to manage big data, new technologies have to be developed indefinitely and organizational [big data strategies](https://builtin.com/articles/big-data-strategy) have to continually adapt.

### 2\. Integration and Processing Requirements

Aside from storage challenges, big data also has to be properly processed, cleaned and formatted to make it useful for analysis. This can take a considerable amount of time and effort due to big data’s size, multiple data sources and combinations of structured, unstructured and semi-structured data. Processing efforts and identifying what information is useful can also be compounded in the case of excess noisy data or data corruption.

### 3\. Cybersecurity and Privacy Risks

Big data systems can sometimes handle sensitive or personal user information, making them vulnerable to cybersecurity attacks or privacy breaches. As more personal data resides in big data storage, and at such massive scales, this raises the difficulty and costs of safeguarding this data from criminals. Additionally, how businesses collect personal data through big data systems [may not comply](https://builtin.com/data-science/dangers-of-too-much-data) with regional data collection laws or regulations, leading to a breach of privacy for affected users.

## Big Data Technologies

Big data technologies describe the tools used to handle and manage data at enormous scales. These technologies include those used for big data analytics, collection, mining, storage and visualization.

### Data Analysis Tools

[Data analysis tools](https://builtin.com/big-data/data-analysis-tools) involve software that can be used for big data analytics, where relevant insights, correlations and patterns are identified within given data.

### Big Data Tools

Big data tools refer to any [data platform](https://builtin.com/big-data/big-data-platform), [database](https://builtin.com/data-science/database), [business intelligence tool](https://builtin.com/software-engineering-perspectives/business-intelligence-tools) or application where large data sets are stored, processed or analyzed.

### Data Visualization Tools

[Data visualization tools](https://builtin.com/data-science/data-visualization-tools) help to display the findings extracted from big data analytics in the form of charts, graphs or dashboards.

History of Big Data

![a female data scientist from the 1950s](/sites/www.builtin.com/files/inline-images/big-data-history.jpg)Image: Shutterstock / Built In

## History of Big Data

“Big data” as a term became popularized [in the mid-1990s](https://journals.sagepub.com/doi/pdf/10.1177/2053951716631130) by computer scientist John Mashey, as Mashey used the term to refer to handling and analyzing massive data sets. In 2001, Gartner analyst Doug Laney characterized big data as having three main traits of volume, velocity and variety, which came to be known as the three V’s of big data. Starting in the 2000s, companies began conducting big data research and developing solutions to handle the influx of information coming from the internet and web applications.

Google created the Google File System [in 2003](https://static.googleusercontent.com/media/research.google.com/en//archive/gfs-sosp2003.pdf) and MapReduce [in 2004](https://static.googleusercontent.com/media/research.google.com/en//archive/mapreduce-osdi04.pdf), both systems meant to help process large data sets. Using Google’s research on these technologies, software designer Doug Cutting and computer scientist Mike Cafarella developed Apache Hadoop in 2005, a software framework used to store and process big data sets for applications. In 2006, Amazon released Amazon Web Services (AWS), an on-demand cloud computing service that became a popular option to store data without using physical hardware.

In the 2010s, big data gained more prevalence as mobile device and tablet adoption increased. According to IBM [as of 2020](https://newsroom.ibm.com/IBM-research?item=32682), humans produce 2.5 quintillion bytes of data on a daily basis, with the world expected to produce 175 zettabytes of data by 2025. As connected devices and internet usage continue to grow, so will big data and its possibilities for enhanced analytics and real-time insights.

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