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Big Data Analysis – By Shreya SrivastavaBig data analytics involves examining of large amounts of data in order to find correlations, hidden patterns and any other insights that we can get from that data. As the technology is improving day by day and so does the analytics technology is improving which makes it possible to analyse large amounts of data and get the answers immediately unlike the more traditional intelligence solutions.The concept of Big data analysis is floating around for a long time now. However now most of the business organisation are understanding the fact that if they analyse all the data which is streaming in their business, they can get a significant result from it. But in 1950s, when no one knew about the Big data, that time also the businesses did some small analytics to uncover insights and trends. But now the new benefits of the analysis is speed and efficiency. As compared to the few years ago a business had to gather information, run analytics and unearthed information that could be used for future decisions, today, those same business can identify insights for immediate decisions. This ability to work faster and stay agile gives organizations a competitive edge they didn’t have before.

Big data analytics helps organization to analyse their data and find new opportunities for their businesses which leads to smarter business moves and more efficient operations and solutions which further leads to increase in profit and customer satisfaction. The companies uses big data analysis mainly for three purposes like cost reduction, making better and faster decision and new products & services.Technologies like Hadoop and cloud analytics can bring significantly reduce the cost in lines of storing large amounts of data and also can identify much more efficient ways of doing business. With the speed of Hadoop and in memory analytics which when combined with the ability to analyse new data helps in analysing the information immediately and make decision on a faster rate based on what they have learnt. Also, because of the analytics it is easier to create new and improved products to meet customer needs and satisfaction. Now let us check how different industries uses Big Data Analytics.

Travel and Hospitality: The main key in travel and hospitality industry is to keep the customer happy which is pretty difficult especially in a time constraint. Specially Casinos, Hotels and resorts have a short window for opportunity to make a customer satisfied. Thus Big data analytics helps then improve customer satisfaction.Healthcare: Big data is very useful in the healthcare industry as healthcare deals with a lot of data like patient records, health plans, insurance information and various other types of information that can be difficult to be stored and managed. But these data can be full of insights once they are analysed using the big data analytics thereby making them so important in the healthcare industry.

Also, by analysing huge amount of information both structured and unstructured and that too, quickly,  healthcare providers can provide lifesaving diagnosis or treatment options almost in a very short duration of time.Government: One of the biggest challenge that the government agencies face is to tighten the budget without compromising the productivity or quality. This is particularly a problem for the law and enforcement industry whose main struggle is to keep the crime rates down with relatively less resources and thus many agencies use big data analytics so as to solve their problems and give them a more holistic view of criminal activity.Retail: For the past several years, customer service has evolved as the customers now expect the retailers to understand what they need, why they need and when they need. Here big data analytics comes into picture where it can help the retailers meet the demand of the customer. Also, the industry have an enormous amount of data from customer loyalty, buying habits and other details.

Thus with the help of big data analysis one can predict the current trends, recommend new products, maintain wishlist, and also increase profits because of all these things.Having discussed the various types of industries that uses Big data analysis on a larger scale let’s see the different types of technologies that work together to help us get the most value for the data analysis. Data Management: The data which we collect is in a very haphazard form and this form cannot be analysed. So firstly, we need to clean the data in order for it to be high quality and well governed before we can send it to the analysis phase. Also, the data flows in and out of an organisation constantly and therefore we need to establish a repetitive process to build and maintain the basic standard of data which will be perfect for data analytics. And once the data is clean and reliable, it can be send to the analysis phase.Data Mining: Data mining is an important technology which helps us analyse huge amounts of data in order to discover patterns in the data and this analysis/ information can be used to further analyse so as to further simplify and analyse and help answer complex business questions. With the data mining technologies, we can filter the irrelevant data and select only the data that we need thereby accelerating the pace of making informed decision.

Hadoop: Hadoop is an open source software framework that helps to store huge amount of data and run applications on cluster of commodity hardware. So data storage and extraction becomes easier and faster with the help of hadoop. And as the volume and the variety of data is increasing day by day, hadoop technology have become the key technology for doing business as its distributed computing model can process the big data fast. Additionally, Hadoop is an open source framework software and thus is free and uses commodity hardware to store huge amount of data. In-memory analytics: Analysing data from system memory instead of the hard disk drive in order to derive immediate insights of your data so that one act on it immediately. This technology also helps to remove data preparation and analytical processing latencies which are required to test new scenarios and create newer models.

It not only makes the business organisations to stay agile and make better decisions but also it enables them to run iterative and interactive analytics scenarios. Predictive Analytics: Predictive analytics technology uses data, machine learning techniques and statistical algorithms to predict the possible future outcomes based on the 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.REFERENCES1 Data Science for Business- by Foster Provost and Tom Fawcett


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