Название: Defining Enterprise Data and Analytics Strategy: Pragmatic Guidance on Defining Strategy Based on Successful Digital Transformation Experience of Multiple Fortune 500 and Other Global CompaniesАвтор: Prakash Sah
Издательство: Springer
Серия: Management for Professionals
Год: 2022
Страниц: 186
Язык: английский
Формат: pdf (true), epub
Размер: 21.3 MB
This is the first of its kind book that describes key elements of enterprise data and analytics strategy, and prescribes a pragmatic approach to define the strategy for large enterprises. The book is based on successful digital transformation experience of multiple Fortune 500 and other large enterprises. It is estimated that more than 50% of data and analytics initiatives fail globally because of the inherent complexity of such initiatives. Some of the questions that enterprises struggle with are: How to define enterprise data and analytics strategy? What are the key elements that should be considered while doing so? Why one-size-fits-all approach does not work for all enterprises? How to align data and analytics initiative with the business strategy of the CEO? How to establish a futuristic technology and architecture foundation, given the exponential rate of innovation in data and analytics technologies? How to define the right data and analytics organization model? Why data and analytics organization and processes need to be different from other functions? How to manage organizational change to ensure success of data and analytics initiative? How to define a business value measurement framework and calculate ROI from data and analytics initiative? What are the key skills required in a data and analytics leader to wade through political and other challenges of a large enterprise? This book will help executives, chief digital/analytics officers, data and analytics professionals, and consultants, in answering the above questions. It will help them in addressing various dilemmas that they face every day and making their enterprises data-driven.
A data and analytics leader must have good understanding of relevant technologies. While the leader does not need hands-on experience in them, she/he should have good understanding of what these technologies are capable of and what are their limitations, i.e., a good understanding of art-of-the-possible and what-is-not-possible. The leader should keep abreast of the latest developments in data and analytics technologies and what these mean for the business of the enterprise. The leader must proactively seek opportunities to learn about various technologies.
The second must-to-have hard skill is Data Science. While different people use different definitions of Data Science, for me it is a broad term that encompasses all the activities involved in “data-to-insight” process. Data science includes data engineering (extracting, cleansing, harmonizing, and architecting data) on one hand, and data analytics/mining (data visualization, artificial intelligence, and machine learning) on the other hand. Data and analytics leader must have passion for data and must be thrilled by the way one can uncover hidden patterns in data to derive insights. Data science is more an art than science. While there are certain data science skills that can be taught to a person, there are others that cannot be taught, if the person does not have some inborn traits.
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