Organizations can make data science a repeatable, predictable tool, which business professionals use to get more value from their data

Enterprise data and AI projects are often scattershot, underbaked, siloed, and not adaptable to predictable business changes. As a result, the vast majority fail. These expensive quagmires can be avoided, and this book explains precisely how.

Data science is emerging as a hands-on tool for not just data scientists, but business professionals as well. Managers, directors, IT leaders, and analysts must expand their use of data science capabilities for the organization to stay competitive. Smarter Data Science helps them achieve their enterprise-grade data projects and AI goals. It serves as a guide to building a robust and comprehensive information architecture program that enables sustainable and scalable AI deployments.

When an organization manages its data effectively, its data science program becomes a fully scalable function that’s both prescriptive and repeatable. With an understanding of data science principles, practitioners are also empowered to lead their organizations in establishing and deploying viable AI. They employ the tools of machine learning, deep learning, and AI to extract greater value from data for the benefit of the enterprise.

By following a ladder framework that promotes prescriptive capabilities, organizations can make data science accessible to a range of team members, democratizing data science throughout the organization. Companies that collect, organize, and analyze data can move forward to additional data science achievements:
• Improving time-to-value with infused AI models for common use cases
• Optimizing knowledge work and business processes
• Utilizing AI-based business intelligence and data visualization
• Establishing a data topology to support general or highly specialized needs
• Successfully completing AI projects in a predictable manner
• Coordinating the use of AI from any compute node. From inner edges to outer edges: cloud, fog, and mist computing

When they climb the ladder presented in this book, businesspeople and data scientists alike will be able to improve and foster repeatable capabilities. They will have the knowledge to maximize their AI and data assets for the benefit of their organizations.、

企业可以使数据科学成为一个可重复、可预测的工具，业务人员利用它来从数据中获得更多价值

企业数据和人工智能项目往往是分散的、不充分的、孤立的，而且不能适应可预测的业务变化。因此，绝大多数都失败了。这些昂贵的泥潭是可以避免的，而这本书恰恰解释了如何避免。

数据科学正在成为一种实践工具，不仅适用于数据科学家，也适用于商业专业人士。经理、主管、IT领导和分析师必须扩大他们对数据科学能力的使用，以保持组织的竞争力。Smarter Data Science帮助他们实现企业级的数据项目和人工智能目标。它是建立一个强大而全面的信息架构项目的指南，能够实现可持续和可扩展的人工智能部署。

当一个组织有效地管理它的数据时，它的数据科学计划就会成为一个完全可扩展的功能，既是规定性的又是可重复的。随着对数据科学原理的理解，从业人员也有能力领导其组织建立和部署可行的人工智能。他们采用机器学习、深度学习和人工智能的工具，从数据中提取更大的价值，为企业带来好处。

通过遵循一个促进规定能力的阶梯框架，组织可以使数据科学为一系列团队成员所接受，使数据科学在整个组织内民主化。收集、组织和分析数据的公司可以向更多的数据科学成就迈进。

- 通过为常见用例注入人工智能模型来提高价值实现时间

- 优化知识工作和业务流程

- 利用基于AI的商业智能和数据可视化

- 建立一个数据拓扑结构，以支持一般或高度专业化的需求

- 以可预测的方式成功地完成人工智能项目

- 协调来自任何计算节点的人工智能的使用。从内边缘到外边缘：云、雾和雾计算

当他们爬上本书介绍的阶梯时，商务人士和数据科学家都将能够改善和培养可重复的能力。他们将拥有知识来最大化他们的人工智能和数据资产，以利于他们的