Big Data Analytics: A Practical Guide for Managers by Kim H. Pries

By Kim H. Pries

With this e-book, managers and determination makers are given the instruments to make extra proficient judgements approximately significant information paying for projects. Big info Analytics: a realistic advisor for Managers not just provides descriptions of universal instruments, but in addition surveys many of the items and owners that provide the massive info market.

Comparing and contrasting the different sorts of study more often than not performed with immense info, this available reference provides uncomplicated causes of the overall workings of huge info instruments. rather than spending time on how one can set up particular applications, it specializes in the explanations WHY readers might set up a given package.

The booklet presents authoritative suggestions on quite a number instruments, together with open resource and proprietary structures. It information the strengths and weaknesses of incorporating significant info research into decision-making and explains tips to leverage the strengths whereas mitigating the weaknesses.

  • Describes some great benefits of allotted computing in easy terms
  • Includes mammoth vendor/tool fabric, specially for open resource decisions
  • Covers favourite software program programs, together with Hadoop and Oracle Endeca
  • Examines GIS and computer studying applications
  • Considers privateness and surveillance matters

The booklet extra explores simple statistical suggestions that, whilst misapplied, could be the resource of error. many times, massive facts is handled as an oracle that discovers effects not anyone may have imagined. whereas vast information can serve this priceless functionality, all too frequently those effects are improper, but are nonetheless pronounced unquestioningly. The likelihood of getting misguided effects raises as a bigger variety of variables are in comparison until preventative measures are taken.

The method taken via the authors is to provide an explanation for those ideas so managers can ask greater questions in their analysts and owners as to the appropriateness of the equipment used to reach at a end. as the global of technology and drugs has been grappling with related matters within the book of reviews, the authors draw on their efforts and follow them to important data.

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Additional info for Big Data Analytics: A Practical Guide for Managers

Sample text

However, before exploring this field, it is important to understand the categories of data storage: • Primary: This is mostly made up of what in modern computers is referred to as random access memory (RAM). Primary storage is the memory that holds the information that the computer is working on at a given time. Information in primary storage usually resides there for the short term, then disappears when the device is turned off. There are two types of primary data storage aside from the RAM: • Processor registers: This is information stored directly within the processor.

Second, data centers generally offer a degree of protection and redundancy to data that is not possible when they are stored on a hard drive in the office. Third, data security in the cloud can be superb when it is in the right hands. With proper security, even the employees of the data center are physically unable to access any of the client’s data. Private cloud solutions with no presence on the Internet are, as we discussed, only accessible to the intended client using a secure connection and can be very safe solutions that are, for all intents and purposes, part of the internal information technology (IT) solution.

Each has its characteristics that are easy to remember. Once you understand these, you can ask better questions of external salespeople and your internal IT departments. • Big data technologies enable the integration of capabilities previously not included in most business analytics. These include GIS and predictive analytics. New kinds of analysis are evolving. • Data is not an oracle. It reflects the conditions under which it was created. There are biases and errors that creep into data. Even the best data cannot predict developments for which there is no precedent.

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