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BIG DATA - An Overview


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.
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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.

Rocheston Certified Big Data Analyst (CBDA)

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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.

Different Sectors using Big Data


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 health 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.
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