BIG DATA ANALYTICS

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BIG  DATA  ANALYTICS

WHAT IT IS AND WHY IT MATTERS



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

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 analyse new sources of data , businesses are able to analyse 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 customer's needs .


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 :

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 analyse 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 analysed . 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 analysing 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 analyse 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 analyse large amounts of information and discover new topics and term relationships .


Written By - Ritesh Pandita  ©

What it is and why it matters

What it is and why it matters

What it is and why it matters

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