The Enterprise Big Data Framework

Beschreibung

Transform enterprise big data into valuable assets with this comprehensive guide to data analysis, data engineering, algorithm design and data architecture.
Businesses who can make sense of the huge influx and complexity of data will be the big winners in the information economy. This comprehensive guide covers all the aspects of transforming enterprise data into value, from the initial set-up of a big data strategy, towards algorithms, architecture and data governance processes. Using a vendor-independent approach, The Enterprise Big Data Framework offers practical advice on how to develop data-driven decision making, detailed data analysis and data engineering techniques. With a focus on practical implementation, The Enterprise Big Data Framework introduces six critical capabilities that every organization can use to become data driven. With sections on strategy formulation, data governance, sustainability, architecture and algorithms, this guide provides a comprehensive overview of best practices organizations can leverage to win in the data economy. Throughout the different sections, the book also introduces a capability model that every organization can use to measure progress. Endorsed by leading accreditation and examination institute AMPG International, this book is required reading for the Enterprise Big Data Certifications, which aim to develop excellence in big data practices across the globe. Online resources include sample data for practice purposes.
Shows readers how to fully leverage, analyze and integrate big data to reduce costs, increase operating margins and add income
Offers a vendor-independent approach to transforming massive quantities of data into value
Covers detailed data analysis and data engineering techniques for anyone who wants a deep understanding of big data
Helps readers understand the core concepts and techniques that underpin the somewhat abstract concept of big data
Online resources: sample data for practice purposes

Autor*in

Jan-Willem Middelburg
Jan-Willem Middelburg is a Dutch entrepreneur and author with a passion for technology and innovation. He is the CEO and co-founder of Cybiant, a global technology that company that helps to create a more sustainable world through analytics, big data and automation. He is also President and Chief Examiner of the Enterprise Big Data Framework, an independent organization dedicated to upskilling individuals with expertise in Big Data. In partnership with APMG-International, the Enterprise Big Data Framework offers vendor-neutral certifications for individuals.

Themen in »The Enterprise Big Data Framework«

Big Data analysis Big Data architecture Big Data engineering Big Data algorithms Big Data functions Big Data security Big Data processes Data modelling Data warehouse machine learning

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"The Enterprise Big Data Framework is relevant for everybody within an organisation engaged in driving maximum benefits from data. There is something for everybody; from the board considering governance and ethical behaviour to individuals within the organisation knowing where they fit and the value they can get from better use of their organisation's data. If you are considering a transformation project, this is an excellent guide for your project team."
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"If you are looking for a good guide to empower your knowledge on big data and to find a framework to help you on your big data journey, then this book is for you. From learning what big data is to defining a big data strategy, Jan-Willem has built a book to empower the learner on the topic of big data."
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"This book is a master piece for those who are familiar and those who discover the world of data. It provides an "a la carte framework" starting with a (big) data strategy and the supporting aspects such as big data functions, architecture and algorithms. It covers in depth data platforms architectures, its management as well as data governance, data catalogue and all the required security considerations associated to the various data classifications. You will find details of data life cycle management, of various machine learning algorithms and an important chapter covering AI ethics when building and deploying sophisticated algorithms using data. The concepts covered in this book apply to on-premises and in the (public) cloud environments, making this book a must read."
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