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Big data is a term used for the examination of large amounts of data to uncover hidden patterns, correlations and other insights.

With today’s technology, it’s possible to analyze data and get answers from it almost immediately – an effort that’s slower and less efficient with more traditional business intelligence solutions.

History and evolution of big data

The concept of big data has been around for years; most organizations now understand that if they capture all the data that streams into their businesses (potentially in real time), they can apply analytics and get significant value from it. This is particularly true when using sophisticated techniques like artificial intelligence. But even in the 1950s, decades before anyone uttered the term “big data,” businesses were using basic analytics (essentially, numbers in a spreadsheet that were manually examined) to uncover insights and trends.

Some of the best benefits of big data analytics are speed and efficiency. Just a few years ago, businesses gathered information, ran analytics and unearthed information that could be used for future decisions. Today, businesses can collect data in real time and analyze big data to make immediate, better-informed decisions. The ability to work faster – and stay agile – gives organizations a competitive edge they didn’t have before.

Why is big data analytics important?

Big data analytics helps organizations harness their data and use it to identify new opportunities. That, in turn, leads to smarter business moves, more efficient operations, higher profits and happier customers. Businesses that use big data with advanced analytics gain value in many ways, such as:

Reducing cost. Big data technologies like cloud-based analytics can significantly reduce costs when it comes to storing large amounts of data (for example, a data lake). Plus, big data analytics helps organizations find more efficient ways of doing business.

Making faster, better decisions. The speed of in-memory analytics – combined with the ability to analyze new sources of data, such as streaming data from IoT – helps businesses analyze information immediately and make fast, informed decisions.

Developing and marketing new products and services. Being able to gauge customer needs and customer satisfaction through analytics empowers businesses to give customers what they want, when they want it. With big data analytics, more companies have an opportunity to develop innovative new products to meet customers’ changing needs.

Big data analytics in today’s world

Most organizations have big data. And many understand the need to harness that data and extract value from it. But how? These resources cover the latest thinking on the intersection of big data and analytics.

How machines learn human language

Big data increases constantly – including social media, emails, texts, sensor data and more. With natural language processing, machines can sift through volumes of big data to uncover trends, analyze sentiment and identify correlations.

Big data analytics builds a better world

SAS is passionate about using advanced analytics to improve our future – whether addressing problems related to poverty, disease, hunger, illiteracy, climate change or education.

Alternative data: Risky or essential?

Alternative data is often unstructured big data of limited use in raw form. Learn why it’s so important to analyze this data to get a comprehensive and current picture of the changing business world.

How it works and key technologies

There’s no single technology that encompasses big data analytics. Of course, there’s advanced analytics that can be applied to big data, but in reality several types of technology work together to help you get the most value from your information. Here are the biggest players:

Cloud computing.

A subscription-based delivery model, cloud computing provides the scalability, fast delivery and IT efficiencies required for effective big data analytics. Because it removes many physical and financial barriers to aligning IT needs with evolving business goals, it is appealing to organizations of all sizes.

Data management.

Data needs to be high quality and well-governed before it can be reliably analyzed. With data constantly flowing in and out of an organization, it's important to establish repeatable processes to build and maintain standards for data quality. Once data is reliable, organizations should establish a master data management program that gets the entire enterprise on the same page.

Data mining.

Data mining technology helps you examine large amounts of data to discover patterns in the data – and this information can be used for further analysis to help answer complex business questions. With data mining software, you can sift through all the chaotic and repetitive noise in data, pinpoint what's relevant, use that information to assess likely outcomes, and then accelerate the pace of making informed decisions.

Data storage, including the data lake and data warehouse. It's vital to be able to store vast amounts of structured and unstructured data – so business users and data scientists can access and use the data as needed. A data lake rapidly ingests large amounts of raw data in its native format. It’s ideal for storing unstructured big data like social media content, images, voice and streaming data. A data warehouse stores large amounts of structured data in a central database. The two storage methods are complementary; many organizations use both.

Hadoop.

This open-source software framework facilitates storing large amounts of data and allows running parallel applications on commodity hardware clusters. It has become a key technology for doing business due to the constant increase of data volumes and varieties, and its distributed computing model processes big data fast. An additional benefit is that Hadoop's open-source framework is free and uses commodity hardware to store and process large quantities of data.

In-memory analytics.

By analyzing data from system memory (instead of from your hard disk drive), you can derive immediate insights from your data and act on them quickly. This technology is able to remove data prep and analytical processing latencies to test new scenarios and create models; it's not only an easy way for organizations to stay agile and make better business decisions, it also enables them to run iterative and interactive analytics scenarios.

Machine learning.

Machine learning, a specific subset of AI that trains a machine how to learn, makes it possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results – even on a very large scale. And by building precise models, an organization has a better chance of identifying profitable opportunities – or avoiding unknown risks.

Predictive analytics.

Predictive analytics technology uses data, statistical algorithms and machine-learning techniques to identify the likelihood of future outcomes based on historical data. It's all about providing the best assessment of what will happen in the future, so organizations can feel more confident that they're making the best possible business decision. Some of the most common applications of predictive analytics include fraud detection, risk, operations and marketing.

Text mining.

With text mining technology, you can analyze text data from the web, comment fields, books and other text-based sources to uncover insights you hadn't noticed before. Text mining uses machine learning or natural language processing technology to comb through documents – emails, blogs, Twitter feeds, surveys, competitive intelligence and more – to help you analyze large amounts of information and discover new topics and term relationships.