With rapidly evolving emerging technologies, the business world is entering a phase of reform. Within times of change, transformative and disruptive environments as well as uncertain futures have created a difficult landscape for leaders within business. Exploring the landscape and providing valuable insight into the leadership among these challenging times, mean that business leaders can rely on Leading within Digital Worlds to provide them with a robust analysis of leadership practices and process to take them forward in the digital age. Pete Grindrod presents Leading within Digital Worlds in a way that will ensure this is an important text for business leaders in remaining at the forefront of the business world in these times of technological and digital evolution.The results of an in-depth study are presented in this title drawing on much of Pete's expertise in leading in the digital world.

Analytics is the application of mathematical and statistical concepts to large data sets so as to distil insights that offer the owner some options for action and competitive advantage or value. This makes it the most desirable and valuable part of big data science. Driven by the increased data capture from digital platforms, commercial fields are becoming data rich and analytics is growing in many sectors. This book presents analytics within a framework of mathematical theory and concepts building upon firm theory and foundations of probability theory, graphs and networks, random matrices, linear algebra, optimization, forecasting, discrete dynamical systems, and more. Following on from the theoretical considerations, applications are given to data from commercially relevant interests: supermarket baskets; loyalty cards; mobile phone call records; smart meters; 'omic' data; sales promotions; social media; and microblogging. Each chapter tackles a topic in analytics: social networks and digital marketing; forecasting; clustering and segmentation; inverse problems; Markov models of behavioural changes; multiple hypothesis testing and decision-making; and so on. Chapters start with background mathematical theory explained with a strong narrative and then give way to practical considerations and then to exemplar applications. Exercises (and solutions), external data resources, and suggestions for project work are given. The book includes an appendix giving a crash course in Bayesian reasoning, for both ease and completeness.