Friday, 16 January 2015

Business of Big Data What Executives and IT Leaders Need to Know

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Product Synopsis

Most of business people are aware of market analysis and its importance to the business. The process of market analysis has been carried out over decades by highly paid experts who enhanced the business leaders’ knowledge of the market as an approach to improve the decision taking. Therefore, many business executives today believe that the Big Data technology is just a step to boost the business analytics. This is quite true yet there are many key differences between both business analytics and Big Data.

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Big Data technologies are believed to bring much more performance to the analytical process and it is typically more powerful than analytics. This is merely because of the increased ability to measure, detect and manage precisely than ever before. The data driven business management will be typically dominated by intuitive decisions rather than rigor and risk taking. Therefore the big data technology represents a management revolution. Nevertheless, as with any other major change in business the organization may face some challenges that require executives’ engagement in order to maintain big data technologies and maintain the superior value out of it.
It is crucial for executives and IT leaders to understand Big Data, predictive analytics, and their relationship to business intelligence.

Target Audience:

Big Data and analytics companies
Data as a Service (DaaS) companies
Cloud-based service providers of all types
Data processing and management companies
Application Programmer Interface (API) companies
Public investment organizations including investment banks
Private investment including hedge funds and private equity

1 Introduction
1.1 Executive Summary
1.2 Big Data and Business Analytics
1.2.1 Volume
1.2.2 Velocity
1.2.3 Variety
1.3 Big Data Navigation

2 Implanting Big Data into the Enterprise: Solutions and Challenges 13
2.1 Data Constantly Flow
2.2 The Importance of Data Insights
2.3 Privacy Challenges
2.4 Human Error and Latency
2.5 Regulation Conflict

3 Building and Managing Data Science Teams

4 Case Study: The Guide to Implement a Big Data Project
4.1.1 Understanding Business Needs
4.1.2 Big Data Planning and Goal Setting
4.1.3 Benefits of Big Data to the Enterprise
4.1.4 Big Data Architect Role
4.1.5 Fraud Detection

5 Selecting the Right Big Data Technology
5.1 MapReduce
5.2 Hadoop
5.4 Hive and PIG

6 Big Data in Industry Verticals

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