Big Data Characteristics

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INTRODUCTION

In every industry, in each portion of the world, senior leaders wonder whether their business are getting a total value from the massive amounts of information they already have inside their organizations. New technologies are gathering more data than ever before, so far many organizations are still looking for better means to gain value from their data and play in the marketplace. Their questions about how best to achieve value persist.
Information is the soul of every successful, profitable and clear business in the world. Something that is as true nowadays as it was before. What has changed in the last few years is the advent of “Big Data”, both as a means of managing the huge volumes of unstructured and semi-structured data
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These characteristics make big data a great challenge for organizations to learn useful knowledge from it.
One of the essential characteristics of Big Data is the vast volume of data represented by different and varied dimensionalities. This is because the ones who collect information select their own procedure for data recording, and the nature of diverse applications also results in diverse data illustrations.
Autonomous data sources with distributed and decentralized controls are a principal characteristic of Big Data applications. The enormous volumes of the data also make an application susceptible to spasms or malfunctions if the entire system has to be reliant on any centralized control entity. For principal Big Data-associated applications, like Flicker, Google, Walmart, and Facebook, a huge quantity of server farms are deployed everywhere in the world to guarantee uninterrupted services and rapid answers for local
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Point of scale (POS), CRM (Customer Relationship Management) or ERP (Enterprise Resource Planning) systems, and their underlying traditional Database Management Systems (DBMSs), were not architected to store long-term detailed data; much less the volume being generated by social, network and machine generated sources. Analytical environments, embodied by Enterprise Data Warehouses (EDW) or Data Marts, were architected to store a large number of historical transactions for analysis and reporting, but at a significant capital Expenditure (CAPEX). Offline tape archives provide a very specific long-term storage use-case at a lower CAPEX investment.
Interrogations about what approach to line up Big Data technologies to the accumulated complication of a remaining IM estate are infrequently addressed.
In order to make every decision good, organizations need to bring the results of knowledge discovery to the business process and track any impact in the various dashboard, reports and exception analysis being monitored. New knowledge over and done with analysis may as well have a bearing on business strategy, CRM strategy and financial strategy going forward. These flows are showed in the next figure

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