Driving Digital Transformation through Data and AI

A Practical Guide to Delivering Data Science and Machine Learning Products
1. Auflage 2020
ISBN: 978-1-78966-539-0
Verlag: Kogan Page

Buch, Englisch, 264 Seiten, Format (B × H): 156 mm x 227 mm, Gewicht: 404 g

Leading tech companies such as Netflix, Amazon and Uber use data science and machine learning at scale in their core business processes, whereas most traditional companies struggle to expand their machine learning projects beyond a small pilot scope. This book enables organizations to truly embrace the benefits of digital transformation by anchoring data and AI products at the core of their business.

It provides executives with the essential tools and concepts to establish a data and AI portfolio strategy as well as the organizational setup and agile processes that are required to deliver machine learning products at scale. Key consideration is given to advancing the data architecture and governance, balancing stakeholder needs and breaking organizational silos through new ways of working.

Each chapter includes templates, common pitfalls and global case studies covering industries such as insurance, fashion, consumer goods, finance, manufacturing and automotive. Covering a holistic perspective on strategy, technology, product and company culture, Driving Digital Transformation through Data and AI guides the organizational transformation required to get ahead in the age of AI.

Autoren/Hrsg.

Borek, Alexander

Prill, Nadine

Fachgebiete

• Wirtschaftswissenschaften Betriebswirtschaft Management Forschung & Entwicklung (F&E), Innovation
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Wirtschaftsinformatik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsinformatik, SAP, IT-Management
• Wirtschaftswissenschaften Betriebswirtschaft Management Strategisches Management

Inhaltsverzeichnis

Chapter - 01: Introduction to delivering data and AI products; Chapter - 02: Developing the data and AI product strategy and goals; Chapter - 03: Setting up the data and AI product delivery organization; Chapter - 04: Identifying and defining data and AI products; Chapter - 05: Delivering high quality data and AI products; Chapter - 06: Designing the data and AI platform and architecture; Chapter - 07: Driving transformative change with data and AI products; Chapter - 08: The future of data and AI products in your organization

Über Autor(innen)

Prill, Nadine
Dr Nadine Prill is Head of Product at Taktile, a Berlin based tech company that provides a platform for machine learning model deployment, governance and explainability. Previously, she worked as product manager at PricewaterhouseCoopers (PwC). Nadine holds a PhD from the University of Oxford.

Borek, Alexander
Dr Alexander Borek is an independent advisor for data, analytics and machine learning strategy. He was previously the Global Head of Data, Analytics & AI at Volkswagen Financial Services, scaling data products across international markets. In his previous roles, he led the data transformation at Volkswagen Group and worked as management consultant at Gartner and IBM.

Dr Alexander Borek is an independent advisor for data, analytics and machine learning strategy. He was previously the Global Head of Data, Analytics & AI at Volkswagen Financial Services, scaling data products across international markets. In his previous roles, he led the data transformation at Volkswagen Group and worked as management consultant at Gartner and IBM.

Dr Nadine Prill is Head of Product at Taktile, a Berlin based tech company that provides a platform for machine learning model deployment, governance and explainability. Previously, she worked as product manager at PricewaterhouseCoopers (PwC). She holds a PhD from the University of Oxford.