By Arvind Sathi
Bringing a practitioner’s view to important information analytics, this paintings examines the drivers at the back of mammoth information, postulates a suite of use circumstances, identifies units of resolution parts, and recommends numerous implementation ways. This paintings additionally addresses and punctiliously solutions key questions about this rising subject, together with What is gigantic info and the way is it getting used? How can strategic plans for giant info analytics be generated? and How does colossal facts swap analytics architecture? the writer, who has greater than twenty years of expertise in details administration structure and supply, has drawn the cloth from a wide breadth of workshops and interviews with enterprise and data know-how leaders, offering readers with the most recent in evolutionary, innovative, and hybrid methodologies of relocating ahead to the courageous new global of huge info.
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Extra resources for Big Data Analytics: Disruptive Technologies for Changing the Game
A typical fraud rule looks for an unusual purchase initiated in a new international location. Unfortunately, for frequent travelers like me, irregular personal credit-card use can easily mimic these fraudulent transactions. However, I carry a smartphone all the time when I travel. Although my credit-card company may not know of my travel to distant geographies, my smartphone has full awareness of my location. Also, the chances of my losing both my credit card and my phone are significantly lower, and even if someone picked up both, it is highly unlikely they would travel with both credit card and smartphone to make fraudulent purchases.
As long as we have time slices with unstructured data representing independent variables and consumer sentiment as a dependent variable, the data can be analyzed to discover causal chains. This is the most powerful aspect of unstructured data analytics. 3 Big Data and Single View of Customer/Product In any enterprise, there are likely to be many views of customers and products. Most of the fragmentation comes from divergent views of customers and products. Customer and product MDM solutions are popular ways of bridging and bringing together a single unified view.
How many times did the product fail to perform? Where were the failures most likely? What led to the failure? What did the user do after the failure? Can we isolate the component, replace it, and repair the product online? These analysis capabilities can now be combined with product changes to create a sophisticated test-marketing framework. We can make changes to the product, try the modified product on a test market, observe the impact, and, after repeated adjustments, offer the altered product to the marketplace.