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Big Data MBA: Driving Business Strategies with Data Science
Integrate big data into business to drive competitive advantage and sustainable successBig Data MBA brings insight and expertise to leveraging big data in business so you can harness the power of analytics and gain a true business advantage. Based on a practical framework with supporting methodology and hands-on exercises, this book helps identify where and how big data can help you transform your business. You'll learn how to exploit new sources of customer, product, and operational data, coupled with advanced analytics and data science, to optimize key processes, uncover monetization opportunities, and create new sources of competitive differentiation. The discussion includes guidelines for operationalizing analytics, optimal organizational structure, and using analytic insights throughout your organization's user experience to customers and front-end employees alike. You'll learn to "think like a data scientist" as you build upon the decisions your business is trying to make, the hypotheses you need to test, and the predictions you need to produce.Business stakeholders no longer need to relinquish control of data and analytics to IT. In fact, they must champion the organization's data collection and analysis efforts. This book is a primer on the business approach to analytics, providing the practical understanding you need to convert data into opportunity.Understand where and how to leverage big data Integrate analytics into everyday operations Structure your organization to drive analytic insights Optimize processes, uncover opportunities, and stand out from the rest Help business stakeholders to "think like a data scientist" Understand appropriate business application of different analytic techniques If you want data to transform your business, you need to know how to put it to use. Big Data MBA shows you how to implement big data and analytics to make better decisions.
- GenresBusinessTechnology
320 pages, Kindle Edition
First published January 19, 2016
36 people are currently reading
151 people want to read
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Displaying 1 - 6 of 6 reviews
November 15, 2021
I watched on YouTube an interview with the author, Bill Schmarzo, and found interesting what he said and his career: he is not the typical millennial data guru but a seasoned professional with many decades of experience in the BI and analytics world (way before it was "the sexiest job").
I think this book does a good job introducing key aspects of the most important data and analytics concepts. I did a master in Data Mining so I already knew them, but the author provides a practical framework and approach for almost everything you need to develop as a data lead so it's useful also for the ones with more experience.
The text becomes a little too repetitive (concepts and examples) and has a more high-level approach than I expected (many parts are written in the style of a textbook while using more of his personal experience and challenges could be more useful, I guess).
It's clear that he loves to teach: all the main algorithms are explained in a very easy to understand way and also the steps of every processes (in some moments it even reminds me of the "For Dummies" series, but with a more academic language).
I found the Analytics Hub and Spoke Analytics section interesting and also the last chapters in which he explores score development, data monetization, envisioning and chief data monetization officer role.
I think this book does a good job introducing key aspects of the most important data and analytics concepts. I did a master in Data Mining so I already knew them, but the author provides a practical framework and approach for almost everything you need to develop as a data lead so it's useful also for the ones with more experience.
The text becomes a little too repetitive (concepts and examples) and has a more high-level approach than I expected (many parts are written in the style of a textbook while using more of his personal experience and challenges could be more useful, I guess).
It's clear that he loves to teach: all the main algorithms are explained in a very easy to understand way and also the steps of every processes (in some moments it even reminds me of the "For Dummies" series, but with a more academic language).
I found the Analytics Hub and Spoke Analytics section interesting and also the last chapters in which he explores score development, data monetization, envisioning and chief data monetization officer role.
May 15, 2020
This book sets you rolling to make maximum of data and stages to improve and seeing it from a business perspective. Bill is a great leader as well as teacher,while he currently is CTO of Hitachis Digital initiatives.
December 15, 2018
All the frameworks, examples and contents in the book are helpful for MBA students with Data Analytics focus and organizations finding ways to monetize their data.
July 11, 2019
Lots of useful tips and techniques to apply to helping a business develop big data usage.
February 6, 2017
The book is aimed at the business community involved with the delivery of business benefit by the use of Data Science and Big Data. It covers a range of benefits and is not restricted to just increased revenue. Following an introductory section, the book contains 4 major parts and 15 chapters.
The content takes the reader through the stages of establishing a “Business Strategy for Big Data” rather than the more commonly seen approach of developing a “Big Data Strategy”, i.e. the use of Big Data without a business purpose to guide its delivery. The author is quite clear that the latter is not as advantageous as the former. In fact, it is also clear from the book that for Big Data to be a successful service to the business, the Business Strategy is key, and that the Business must take a more central role in the Big Data operation. This is emphasised in the book by the statement that, in the past, the Business handed over responsibility to IT for Data Warehousing, with the implication that, for Big Data, the same is happening.
There was recently an article on LinkedIn which presented the view that “Hadoop is Failing” whether this is correct or not is not relevant to this review but a lot of the facts being presented in the article seemed to be raised and answered by the contents of the book. Perhaps this highlights the need for businesses to take on board the concepts raised by the author.
This is the second book written by Bill Schmarzo with the first being aimed at an IT audience, but he felt that there were potentially bigger winners in the Business community and hence this second book. This focus on business can be clearly seen in the four main parts to the book, P1 - Business Potential for Big Data, P2 - Data Science, P3 - Data Science for Business Stakeholders and finally P4 - Building Cross Organisational Support.
Part 1 - Business Potential of Big Data, takes the reader through building the foundations of a business strategy that uses Big Data, rather than a Big Data strategy, for the organisation, therefore, paving the way for whether the dog wags the tail or vice versa. As part of this the Big Data Business Model Maturity Index is introduced and explained. This is a five-step index which places a business along the path of using Big Data to, at step one, monitor the business and at the top, step five, to undertake a metamorphosis of the business. The three intermediary steps take the business through insight, optimisation and monetisation (the creation of new sources of income).
Part 2 - Data Science, is a scene setting section to the data science concept as a precursor to moving the reader into the utilising those concepts from a business perspective. This is done by introducing several different analytical algorithms and when/where they might be appropriate to use. This is demonstrated by utilising a dummy company on which the algorithms can be deployed. The final chapter introduces the Data Lake and how this has allowed businesses to expand the data they can analyse, while being able to reduce the cost of storing the data in a usable form. This can be viewed in another way by comparing the storage capabilities and costs of the Data Warehouse against those of the Data Lake.
Part 3 - Data Science for Business Stakeholders, this section builds on Part 2 by enabling the business stakeholders to think like data scientists and identify what data and algorithms are fundamental to the business, and the decisions that the business must make. This is done by providing a framework that enables the business stakeholders to communicate and work with data scientists, by using a common understanding of the data and algorithms. In simple terms, it is not just about ‘more data’ but also understanding what the business could get from the ‘correct analysis’ of that data.
Part 4 - Building Cross Organisational Support, this final part of the book provides a foundation for transforming (metamorphosis) of the business, to better integrate Big Data, in its widest sense, into that business from both an organisation and cultural perspective. Including those of a human, procedural and roles/responsibilities nature, thus allowing that business to achieve the fifth step of Business Metamorphosis. The final chapter of this part, and of the book, is ‘Stories’, a section on Big Data deployment that, it is hoped, inspires the reader, perhaps allowing them to draft their own stories that fit their own organisations.
Review by Len Keighley FBCS CEng CITP
Originally published: http://www.bcs.org/content/conWebDoc/...
The content takes the reader through the stages of establishing a “Business Strategy for Big Data” rather than the more commonly seen approach of developing a “Big Data Strategy”, i.e. the use of Big Data without a business purpose to guide its delivery. The author is quite clear that the latter is not as advantageous as the former. In fact, it is also clear from the book that for Big Data to be a successful service to the business, the Business Strategy is key, and that the Business must take a more central role in the Big Data operation. This is emphasised in the book by the statement that, in the past, the Business handed over responsibility to IT for Data Warehousing, with the implication that, for Big Data, the same is happening.
There was recently an article on LinkedIn which presented the view that “Hadoop is Failing” whether this is correct or not is not relevant to this review but a lot of the facts being presented in the article seemed to be raised and answered by the contents of the book. Perhaps this highlights the need for businesses to take on board the concepts raised by the author.
This is the second book written by Bill Schmarzo with the first being aimed at an IT audience, but he felt that there were potentially bigger winners in the Business community and hence this second book. This focus on business can be clearly seen in the four main parts to the book, P1 - Business Potential for Big Data, P2 - Data Science, P3 - Data Science for Business Stakeholders and finally P4 - Building Cross Organisational Support.
Part 1 - Business Potential of Big Data, takes the reader through building the foundations of a business strategy that uses Big Data, rather than a Big Data strategy, for the organisation, therefore, paving the way for whether the dog wags the tail or vice versa. As part of this the Big Data Business Model Maturity Index is introduced and explained. This is a five-step index which places a business along the path of using Big Data to, at step one, monitor the business and at the top, step five, to undertake a metamorphosis of the business. The three intermediary steps take the business through insight, optimisation and monetisation (the creation of new sources of income).
Part 2 - Data Science, is a scene setting section to the data science concept as a precursor to moving the reader into the utilising those concepts from a business perspective. This is done by introducing several different analytical algorithms and when/where they might be appropriate to use. This is demonstrated by utilising a dummy company on which the algorithms can be deployed. The final chapter introduces the Data Lake and how this has allowed businesses to expand the data they can analyse, while being able to reduce the cost of storing the data in a usable form. This can be viewed in another way by comparing the storage capabilities and costs of the Data Warehouse against those of the Data Lake.
Part 3 - Data Science for Business Stakeholders, this section builds on Part 2 by enabling the business stakeholders to think like data scientists and identify what data and algorithms are fundamental to the business, and the decisions that the business must make. This is done by providing a framework that enables the business stakeholders to communicate and work with data scientists, by using a common understanding of the data and algorithms. In simple terms, it is not just about ‘more data’ but also understanding what the business could get from the ‘correct analysis’ of that data.
Part 4 - Building Cross Organisational Support, this final part of the book provides a foundation for transforming (metamorphosis) of the business, to better integrate Big Data, in its widest sense, into that business from both an organisation and cultural perspective. Including those of a human, procedural and roles/responsibilities nature, thus allowing that business to achieve the fifth step of Business Metamorphosis. The final chapter of this part, and of the book, is ‘Stories’, a section on Big Data deployment that, it is hoped, inspires the reader, perhaps allowing them to draft their own stories that fit their own organisations.
Review by Len Keighley FBCS CEng CITP
Originally published: http://www.bcs.org/content/conWebDoc/...
July 18, 2016
Fantastic book about Big Data. Truly hands-on guide on how to implement Big Data in the organization. Very business focused vs technology. Definitely going to utilize this knowledge, very valuable.
Displaying 1 - 6 of 6 reviews