To prove business value, data science efforts must avoid being seen as a series of independent science projects. To do so, data science itself, must become part of a predictable paradigm. Data science and the use of artificial intelligence must become a prescriptive and repeatable endeavor that fully scales. Moreover, data science is moving beyond the singular purview of the data scientist and becoming a hands-on tool of the professional businessperson. For any organization that wishes its personnel to leverage machine learning or deep learning is going to find itself being wholly dependent on its accessible data. How well an enterprise organizes and manages its data can be difference between data science becoming a honed enterprise capability or simply a series of one-off science projects. Smarter Data Science is a guide to realizing the imperatives that are required to build a robust and comprehensive information architecture that can be situated to maximize the use of artificial intelligence and to fully support the modern organization. By understanding the principles laid out in the book, practitioners will be empowered to lead their organizations in building and deploying viable AI capabilities that are reliable and that can be deployed at scale. Inherently, an AI model is used for its predictive output, this book lays out the path for successfully completing AI projects in a predictable manner. By following the framework Neal Fishman outlines in the form of a ladder, organizations can climb the ladder to move from collecting, organizing, and analyzing data to be better prepared to: Improve the time to value with pre-built AI for common use cases Optimize knowledge work and business processes Employ AI-assisted business intelligence and data visualization Automate planning, budgeting, and forecasting analytics | Smarter Data Science by Neal Fishman, Paperback | Indigo Chapters