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Mastering Business Intelligence with MicroStrategy

You're reading from Mastering Business Intelligence with MicroStrategy Master Business Intelligence with Microstrategy 10

Product type Paperback

Published in Jul 2016

Publisher Packt

ISBN-13 9781785884405

Length 396 pages

Edition 1st Edition

Tools

MicroStrategy

Concepts

Business Intelligence

Authors (4):

Dmitry Anoshin

Rana

Ma

Neil Mehta

View More author details

Table of Contents (18) Chapters

Mastering Business Intelligence with MicroStrategy

Credits

About the Authors

About the Reviewer

www.PacktPub.com

Preface

1. Getting Started with MicroStrategy

2. Setting Up an Analytics Semantic Layer and Public Objects FREE CHAPTER

3. Building Advanced Reports and Documents

4. Advanced Visualization Techniques

5. Customization of MicroStrategy

6. Predictive Analysis with MicroStrategy

7. Accelerating Your Business with Mobile Analytics

8. Data Discovery with MicroStrategy Desktop

9. MicroStrategy System Administration

10. Design and Implementation of the Security Model

11. Big Data Analytics with MicroStrategy

12. MicroStrategy Troubleshooting

Chapter 11. Big Data Analytics with MicroStrategy

Data with no analysis has no value, and similarly, for analysis we need data. Therefore, the two terms, data and analytics, go together and form big data, Business Intelligence analytics. There are several factors that drive Business Intelligence (BI), and a few of these are:

Speed: Every organization wants to eliminate delays in getting processed information such as reports and dashboards in order to make early and quick business decisions.
Intelligence data: This means use of lots of data and information for predictive and proactive analysis. This data can be of different data types and from multiple sources.
Effectiveness: This helps manage costs by increasing productivity for a business.

Big data is a solution to many of the BI needs. Hadoop has become synonymous with big data; it is an open source software framework for processing large amounts of data and for distributive storage across large clusters of computers. Therefore, we can...

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Authors (4)

Dmitry Anoshin

Dmitry Anoshin

Dmitry Anoshin is a data-centric technologist and a recognized expert in building and implementing big data and analytics solutions. He has a successful track record when it comes to implementing business and digital intelligence projects in numerous industries, including retail, finance, marketing, and e-commerce. Dmitry possesses in-depth knowledge of digital/business intelligence, ETL, data warehousing, and big data technologies. He has extensive experience in the data integration process and is proficient in using various data warehousing methodologies. Dmitry has constantly exceeded project expectations when he has worked in the financial, machine tool, and retail industries. He has completed a number of multinational full BI/DI solution life cycle implementation projects. With expertise in data modeling, Dmitry also has a background and business experience in multiple relation databases, OLAP systems, and NoSQL databases. He is also an active speaker at data conferences and helps people to adopt cloud analytics.

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Rana

Rana

Himani Rana is a senior data scientist and has been working as database developer and MicroStrategy specialist for multinational companies since 2007. She is currently pursuing her PhD in data mining and big data. She completed her master's in data mining and vehicular ad hoc networks. She has to her credit the following publication: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6557928. Himani possesses in-depth knowledge of Business Intelligence and big data technologies. She specializes in insurance and financial data analysis because of her Chartered Insurance Professional (CIP) designation. In addition, she was a committee member and reviewer of Information Systems, Technology and Management (http://www.springer.com/gp/book/9783642291654) and 2011 Third International Conference on Advanced Computing (ICoAC) (http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6156880).

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Ma

Ma

Ning Ma is an experienced data analyst working in an insurance company. He is experienced with statistical modeling, creating executive dashboards, fraud detection, text mining and teaching. He is also knowledgeable about implementing predictive analysis in MicroStrategy. He enjoys helping business users discovering knowledge from data, by visualizing data patterns and testing hypotheses. He is passionate about producing easy-to-use data products to support business decision making. Prior to his current position, he worked in China doing IT and investment jobs. He did his computer science and economics undergraduate studies in Beijing, economics and finance graduate studies in Canada, and did research on quantitative finance in Canada. In his free time, he loves reading, electronic arts, and all kinds of outdoor activities.

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Neil Mehta

Neil Mehta

Neil MehtaBSc (Hons) has 20 years of experience as a developer, analyst, and program manager and has spent the last 7 years specifically implementing business intelligence solutions to help companies leverage their corporate data. Trained in all aspects of analytics from data modeling to system architecture and reporting, Neil currently manages a large team of data architects, ETL developers, and report designers for a large insurance company.He has extensive experience with business analytics, administration, and dashboard design and has helped develop programs to establish super user communities and develop training plans. He has worked in multiple business segments, including financial, oil and gas, transportation, and retail industries.

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