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.

This book describes key elements of enterprise data and analytics strategy and prescribes a pragmatic approach to define strategy for large enterprises. It 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 inherent complexities of such initiatives. The book discusses key challenges that enterprises struggle with, such asdefining enterprise data and analytics strategy, and key elements that should be considered while doing so; limitations of one-size-fits-all approach which does not work for all enterprises; aligning data and analytics initiative with business strategy of the CEO; establishing a futuristic technology and architecture foundation, given the exponential rate of innovation in data and analytics technologies; defining the right data and analytics organization model and structure; reasons why data and analytics organization and processes need to be different from other functions; managing organizational change to ensure success of data and analytics initiative; defining a business value measurement framework and calculating ROI from data and analytics initiative; and key skills required in a data and analytics leader to wade through political and other challenges of a large enterprise. Often, data and analytics leaders define a strategy that is focused primarily on technology and architecture. This leads to failure of a majority of data and analytics initiatives across enterprises. The book recommends defining a holistic strategy through five key elements (a) business capabilities, (b) technology and architecture, (c) team, processes, and governance, (d) organizational change management, and (e) value measurement framework. The book helps executives, chief digital/analytics officers, data and analytics professionals, consultants, and students in addressing various challenges and dilemmas that they face every day to make their enterprises more data driven.

Master a proven approach to create, implement, and sustain a data strategy.

"International Institute for Analytics"--Dust jacket.

Business Intelligence Strategy and Big Data Analytics is written for business leaders, managers, and analysts - people who are involved with advancing the use of BI at their companies or who need to better understand what BI is and how it can be used to improve profitability. It is written from a general management perspective, and it draws on observations at 12 companies whose annual revenues range between $500 million and $20 billion. Over the past 15 years, my company has formulated vendor-neutral business-focused BI strategies and program execution plans in collaboration with manufacturers, distributors, retailers, logistics companies, insurers, investment companies, credit unions, and utilities, among others. It is through these experiences that we have validated business-driven BI strategy formulation methods and identified common enterprise BI program execution challenges. In recent years, terms like "big data and "big data analytics have been introduced into the business and technical lexicon. Upon close examination, the newer terminology is about the same thing that BI has always been about: analyzing the vast amounts of data that companies generate and/or purchase in the course of business as a means of improving profitability and competitiveness. Accordingly, we will use the terms BI and business intelligence throughout the book, and we will discuss the newer concepts like big data as appropriate. More broadly, the goal of this book is to share methods and observations that will help companies achieve BI success and thereby increase revenues, reduce costs, or both. - Provides ideas for improving the business performance of one's company or business functions - Emphasizes proven, practical, step-by-step methods that readers can readily apply in their companies - Includes exercises and case studies with road-tested advice about formulating BI strategies and program plans

Without a data strategy, the people within an organization have no guidelines for making decisions that are absolutely crucial to the success of the IT organization and to the entire organization. The absence of a strategy gives a blank check to those who want to pursue their own agendas, including those who want to try new database management systems, new technologies (often unproven), and new tools. This type of environment provides no hope for success. Data Strategy should result in the development of systems with less risk, higher quality systems, and reusability of assets. This is key to keeping cost and maintenance down, thus running lean and mean. Data Strategy provides a CIO with a rationale to counter arguments for immature technology and data strategies that are inconsistent with existing strategies. This book uses case studies and best practices to give the reader the tools they need to create the best strategy for the organization.

This book shows how to use an integrated approach to bring business analytics and HR analytics together by presenting a thorough and realistic treatment of the reasons for and challenges of taking an integrated approach.