Big Data Analytics

What it is and why it matters

Big data analytics examines large amounts of data to uncover hidden patterns, correlations and other insights. With today’s technology, it’s possible to analyze your 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 analytics

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, they can apply analytics and get significant value from it. 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.

The new benefits that big data analytics brings to the table, however, are speed and efficiency. Whereas a few years ago a business would have gathered information, run analytics and unearthed information that could be used for future decisions, today that business can identify insights for immediate decisions. The ability to work faster – and stay agile – gives organizations a competitive edge they didn’t have before.

 

The Importance of Big Data Analytics Graphic

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. In his report Big Data in Big Companies, IIA Director of Research Tom Davenport interviewed more than 50 businesses to understand how they used big data. He found they got value in the following ways:

  1. Cost reduction. Big data technologies such as Hadoop and cloud-based analytics bring significant cost advantages when it comes to storing large amounts of data – plus they can identify more efficient ways of doing business.
  2. Faster, better decision making. With the speed of Hadoop and in-memory analytics, combined with the ability to analyze new sources of data, businesses are able to analyze information immediately – and make decisions based on what they’ve learned.
  3. New products and services. With the ability to gauge customer needs and satisfaction through analytics comes the power to give customers what they want. Davenport points out that with big data analytics, more companies are creating new products to meet customers’ needs.

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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.

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Big data meets big data analytics

This white paper gives you a basic overview of how analytics affects big data – plus it outlines three technologies necessary for extracting value from that data.

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Bringing the power of SAS® to Hadoop

Want to get even more value from Hadoop? This paper presents the SAS portfolio of solutions that help you apply business analytics to Hadoop.

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Health care and big data analytics

A big data boom is on the horizon, so it’s more important than ever to take control of your health information. This webinar explains how big data analytics plays a role.

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How IT and business can work together

At a recent panel discussion, leaders from banking and insurance companies discussed how IT and business can collaborate on analytics.

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High-performance analytics lets you do things you never thought about before because the data volumes were just way too big. For instance, you can get timely insights to make decisions about fleeting opportunities, get precise answers for hard-to-solve problems and uncover new growth opportunities – all while using IT resources more effectively.
From the white paper Big Data Meets Big Data Analytics

 

SAS Visual Analytics

In-Memory Analytics

With in-memory analytics solutions from SAS – from SAS Event Stream Processing to SAS Visual Analytics – you can use analytics to get immediate insights from your big data.

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Who’s using it?

Think of a business that relies on quick, agile decisions to stay competitive, and most likely big data analytics is involved in making that business tick. Here’s how different types of organizations might use the technology:

Travel and hospitality

Keeping customers happy is key to the travel and hotel industry, but customer satisfaction can be hard to gauge – especially in a timely manner. Resorts and casinos, for example, have only a short window of opportunity to turn around a customer experience that’s going south fast. Big data analytics gives these businesses the ability to collect customer data, apply analytics and immediately identify potential problems before it’s too late.

Health care

Big data is a given in the health care industry. Patient records, health plans, insurance information and other types of information can be difficult to manage – but are full of key insights once analytics are applied. That’s why big data analytics technology is so important to heath care. By analyzing large amounts of information – both structured and unstructured – quickly, health care providers can provide lifesaving diagnoses or treatment options almost immediately.

Government

Certain government agencies face a big challenge: tighten the budget without compromising quality or productivity. This is particularly troublesome with law enforcement agencies, which are struggling to keep crime rates down with relatively scarce resources. And that’s why many agencies use big data analytics; the technology streamlines operations while giving the agency a more holistic view of criminal activity.

Retail

Customer service has evolved in the past several years, as savvier shoppers expect retailers to understand exactly what they need, when they need it. Big data analytics technology helps retailers meet those demands. Armed with endless amounts of data from customer loyalty programs, buying habits and other sources, retailers not only have an in-depth understanding of their customers, they can also predict trends, recommend new products – and boost profitability.

 

Lenovo uses analytics to rethink its design

As Lenovo was closing in on a final design for a new keyboard, the company's Corporate Analytics unit stumbled across something unexpected: a small but significant online community of gamers who passionately supported the current design. Analytics technology not only helped Lenovo discover the group, it also helped it unearth the sentiment and, as a result, empowered the company to keep its customers happy.

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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:

 

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.

Hadoop. This open source software framework can store large amounts of data and run applications on clusters of commodity hardware. It has become a key technology to 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 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. 

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 a best assessment on 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.

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