Big Data Analytics What it is and why it matters
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:
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.
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.
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.
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.
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.[Source]-https://www.sas.com/en_us/insights/analytics/big-data-analytics.html
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