Data Product Management in the AI Age: Design and Manage Your Data Strategy to Get Ahead » książka
Data Product Management in the AI Age: Design and Manage Your Data Strategy to Get Ahead
ISBN-13: 9798868813146 / Angielski / Miękka / 2025 / 220 str.
Data Product Management in the AI Age: Design and Manage Your Data Strategy to Get Ahead
ISBN-13: 9798868813146 / Angielski / Miękka / 2025 / 220 str.
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We have firmly entered the artificial intelligence (AI) age,. Data, the backbone of this technology, is more crucial than ever. If the algorithm is the brain, data is the content that feeds it. This book argues that to succeed in this era, organizations should adopt a holistic approach to data—one that uses product management principles to inform how data is sourced and designed, managed and maintained, optimized and leveraged. This book moves in two sections from the fundamentals to practical applications. In the first part, you’ll learn about the concepts that underlie product theory, the data architecture journey, and the necessary knowledge to manage products of data. The second part teaches you to put everything into practice, paying particular attention to the designing of solutions to ongoing maintenance and optimization. By the end of the book, you will have tools to begin transforming your organization’s data strategy and approach to get—and stay—ahead of the competition. What You Will Learn Know the evolution of data architecture and strategies Understand the fundamentals of data product management Know the differences between Data Product and Data as a Product Know what the Golden Data Platform is and how to use it Know what Data Product Management Canvas is and ow to use it Reorient your data strategy with product management principles Understand the concepts of products and data architecture evolution in relation to your leadership Evaluate how to solve data architecture without bias by technology Design and implement Data as a Product during a project and ongoing Who This Is Book Is For Data leaders or managers who design and implement data products; product managers interested in working with data; analytics engineers, data engineers, data scientists and/or machine learning engineers implementing data solutions; data-related professionals and developers aspiring to data leadership and product management
We have firmly entered the artificial intelligence (AI) age,. Data, the backbone of this technology, is more crucial than ever. If the algorithm is the brain, data is the content that feeds it. This book argues that to succeed in this era, organizations should adopt a holistic approach to data—one that uses product management principles to inform how data is sourced and designed, managed and maintained, optimized and leveraged. This book moves in two sections from the fundamentals to practical applications. In the first part, you’ll learn about the concepts that underlie product theory, the data architecture journey, and the necessary knowledge to manage products of data. The second part teaches you to put everything into practice, paying particular attention to the designing of solutions to ongoing maintenance and optimization. By the end of the book, you will have tools to begin transforming your organization’s data strategy and approach to get—and stay—ahead of the competition. What You Will Learn Know the evolution of data architecture and strategies Understand the fundamentals of data product management Know the differences between Data Product and Data as a Product Know what the Golden Data Platform is and how to use it Know what Data Product Management Canvas is and ow to use it Reorient your data strategy with product management principles Understand the concepts of products and data architecture evolution in relation to your leadership Evaluate how to solve data architecture without bias by technology Design and implement Data as a Product during a project and ongoing Who This Is Book Is For Data leaders or managers who design and implement data products; product managers interested in working with data; analytics engineers, data engineers, data scientists and/or machine learning engineers implementing data solutions; data-related professionals and developers aspiring to data leadership and product management
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