Companies across all industries are engaging in digital transformation to harness the power of advanced information technologies. Building on interviews and diverse case studies, this book provides an in-depth look at how data and algorithms are reshaping management practices, organizational structures, corporate culture, and work roles. Henri Schildt develops a broad framework for understanding digitalization not as a technological change but as a new normative mind-set, here called 'the data imperative'. It describes the new managerial ideals that compel companies to pursue digital omniscience and omnipotence-abilities to represent and understand the world through real-time data flow and to control customer experiences, physical equipment, and workers with software. The efforts to complement and replace human expertise with data and smart algorithms are associated with shifts in strategic priorities, adoption of powerful modular architectures, new organizational structures, and the introduction of artificial intelligence into diverse work roles. Surveying the developments in management and the workplace, this book offers an integrative and balanced account of the on-going changes that will continue to affect everyone from executives and professionals to front-line workers.

This practical book covers both strategies and tactics around managing a data governance initiative to help make the most of your data.

Big Data Imperatives, focuses on resolving the key questions on everyone’s mind: Which data matters? Do you have enough data volume to justify the usage? How you want to process this amount of data? How long do you really need to keep it active for your analysis, marketing, and BI applications? Big data is emerging from the realm of one-off projects to mainstream business adoption; however, the real value of big data is not in the overwhelming size of it, but more in its effective use. This book addresses the following big data characteristics: Very large, distributed aggregations of loosely structured data – often incomplete and inaccessible Petabytes/Exabytes of data Millions/billions of people providing/contributing to the context behind the data Flat schema's with few complex interrelationships Involves time-stamped events Made up of incomplete data Includes connections between data elements that must be probabilistically inferred Big Data Imperatives explains 'what big data can do'. It can batch process millions and billions of records both unstructured and structured much faster and cheaper. Big data analytics provide a platform to merge all analysis which enables data analysis to be more accurate, well-rounded, reliable and focused on a specific business capability. Big Data Imperatives describes the complementary nature of traditional data warehouses and big-data analytics platforms and how they feed each other. This book aims to bring the big data and analytics realms together with a greater focus on architectures that leverage the scale and power of big data and the ability to integrate and apply analytics principles to data which earlier was not accessible. This book can also be used as a handbook for practitioners; helping them on methodology,technical architecture, analytics techniques and best practices. At the same time, this book intends to hold the interest of those new to big data and analytics by giving them a deep insight into the realm of big data.

Data is powerful. It separates leaders from laggards and it drives business disruption, transformation, and reinvention. Today's most progressive companies are using the power of data to propel their industries into new areas of innovation, specialization, and optimization. The horsepower of new tools and technologies have provided more opportunities than ever to harness, integrate, and interact with massive amounts of disparate data for business insights and value – something that will only continue in the era of the Internet of Things. And, as a new breed of tech-savvy and digitally native knowledge workers rise to the ranks of data scientist and visual analyst, the needs and demands of the people working with data are changing, too. The world of data is changing fast. And, it's becoming more visual. Visual insights are becoming increasingly dominant in information management, and with the reinvigorated role of data visualization, this imperative is a driving force to creating a visual culture of data discovery. The traditional standards of data visualizations are making way for richer, more robust and more advanced visualizations and new ways of seeing and interacting with data. However, while data visualization is a critical tool to exploring and understanding bigger and more diverse and dynamic data, by understanding and embracing our human hardwiring for visual communication and storytelling and properly incorporating key design principles and evolving best practices, we take the next step forward to transform data visualizations from tools into unique visual information assets. - Discusses several years of in-depth industry research and presents vendor tools, approaches, and methodologies in discovery, visualization, and visual analytics - Provides practicable and use case-based experience from advisory work with Fortune 100 and 500 companies across multiple verticals - Presents the next-generation of visual discovery, data storytelling, and the Five Steps to Data Storytelling with Visualization - Explains the Convergence of Visual Analytics and Visual discovery, including how to use tools such as R in statistical and analytic modeling - Covers emerging technologies such as streaming visualization in the IOT (Internet of Things) and streaming animation

Written by a leading expert in the field, this guide focuses on the convergence of two major trends in information management--big data and information governance--by taking a strategic approach oriented around business cases and industry imperatives. With the advent of new technologies, enterprises are expanding and handling very large volumes of data; this book, nontechnical in nature and geared toward business audiences, encourages the practice of establishing appropriate governance over big data initiatives and addresses how to manage and govern big data, highlighting the relevant processes, procedures, and policies. It teaches readers to understand how big data fits within an overall information governance program; quantify the business value of big data; apply information governance concepts such as stewardship, metadata, and organization structures to big data; appreciate the wide-ranging business benefits for various industries and job functions; sell the value of big data governance to businesses; and establish step-by-step processes to implement big data governance.

This practical guide to artificial intelligence and its impact on industry dispels common myths and calls for cross-sector, collaborative leadership for the responsible design and embedding of AI in the daily work of businesses and oversight by boards. Artificial intelligence has arrived, and it's coming to a business near you. The disruptive impact of AI on the global economy—from health care to energy, financial services to agriculture, and defense to media—is enormous. Technology literacy is a must for traditional businesses, their boards, policy makers, and governance professionals. This is the first book to explain where AI comes from, why it has emerged as one of the most powerful forces in mergers and acquisitions and research and development, and what companies need to do to implement it successfully. It equips business leaders with a practical roadmap for competing and even thriving in the face of the coming AI revolution. The authors analyze competitive trends, provide industry and governance examples, and explain interactions between AI and other digital technologies, such as blockchain, cybersecurity, and the Internet of Things. At the same time, AI experts will learn how their research and products can increase the competitiveness of their businesses, and corporate boards will come away with a thorough knowledge of the AI governance, ethics, and risk questions to ask.

The pace of technological change is accelerating, hyper competition is growing, opportunities for business model disruption are exploding, and comprehensive cloud delivery is readily available. These factors challenge every aspect of business technology strategy. The Innovator’s Imperative: Rapid Technology Adoption for Digital Transformation prepares twenty-first century businesses leaders for competing and leading in this disruptive digital environment. Five years of research conducted by the authors suggests that leading companies have all but abandoned the requirements analysis and modeling best practices of the twentieth century. Accordingly, the authors put forth the innovator’s imperative that contends: All companies wanting to be competitive should adopt emerging and disruptive technologies as quickly as possible, and in many cases, immediately. Technology is driving business strategy, and companies are rethinking their technology strategy, especially the governance that determines how and why technology investments are made. Based on their research the authors have developed a five-step framework for digital transformation: Model and simulate Identify high-leverage opportunities Prioritize transformational targets Identify digital opportunities Find courageous leaders The book explains each of these steps to guide business leaders in architecting digital transformation projects according to their organization’s market positions, budgets, objectives, and corporate culture. Hyper-competitive, disruptive companies are jumping across technology adoption phases without regard to any phasing whatsoever. Companies focused on digital transformation often adopt emerging technologies immediately. They have become early adopters of technologies that can impact existing—and create whole new—business models and processes. This book examines this jump into new technologies, processes, and business models to prepare twenty-first century business leaders to make that leap.