Leading in Analytics: The Seven Critical Tasks for Executives to Master in the Age of Big DataКНИГИ » ОС И БД
Название: Leading in Analytics: The Seven Critical Tasks for Executives to Master in the Age of Big Data Автор: Joseph A. Cazier Издательство: Wiley Год: 2024 Страниц: 320 Язык: английский Формат: epub (true) Размер: 11.3 MB
A step-by-step guide for business leaders who need to manage successful Big Data projects.
Leading in Analytics: The Critical Tasks for Executives to Master in the Age of Big Data takes you through the entire process of guiding an analytics initiative from inception to execution. You'll learn which aspects of the project to pay attention to, the right questions to ask, and how to keep the project team focused on its mission to produce relevant and valuable project. As an executive, you can't control every aspect of the process. But if you focus on high-impact factors that you can control, you can ensure an effective outcome. This book describes those factors and offers practical insight on how to get them right.
Drawn from best-practice research in the field of analytics, the Manageable Tasks described in this book are specific to the goal of implementing Big Data tools at an enterprise level. A dream team of analytics and business experts have contributed their knowledge to show you how to choose the right business problem to address, put together the right team, gather the right data, select the right tools, and execute your strategic plan to produce an actionable result. Become an analytics-savvy executive with this valuable book.
Although some predictive tasks were possible without computers, such as simple regression analysis and the application of proven formulas or laws (i.e., gravity), it was not until recent advances in computational power and increases in data availability that these tools could be widely adopted. The reason is based on how Supervised Machine Learning, the primary tool of prediction, works.
Most machine learning tools are static in the sense that a model is built on a snapshot of data and applied to future applications. Certainly there are some adjustments made over time to maintain and improve the models, but inherently they are built on mostly static data. Reinforcement learning is different. It is designed and built for continual learning and adaptation to new and dynamic circumstances. Reinforcement learning is like playing a game of hot and cold, where you get feedback for every action you take, with rewards (hot) and punishment (cold) based on the outcome. Reinforcement learning adds an important time dimension in that it acts, gets feedback from the outcome, then it reevaluates. In this way, it has the potential to be quite prescriptive in its effect. For these reasons, it is considered a new, third type of Machine Learning, in addition to supervised and unsupervised learning. It is best used where continual learning and automation are important. For example, this is one of the core technologies behind self-driving cars and winning ML video game applications, such as Alpha Go.
• Ensure the success of analytics initiatives, maximize ROI, and draw value from Big Data • Learn to define success and failure in analytics and Big Data projects • Set your organization up for analytics success by identifying problems that have Big Data solutions • Bring together the people, the tools, and the strategies that are right for the job
By learning to pay attention to critical tasks in every analytics project, non-technical executives and strategic planners can guide their organizations to measurable results.
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Leading in Analytics: The Seven Critical Tasks for Executives to Master in the Age of Big Data 
