Framing data science strategy
Considering the inherent complexity in data science
Dealing with difficult challenges
Managing change in data science
Understanding the past, present, and future of data
Considering the ethical aspects of data science
Evolving from data-driven to machine-driven
Building successful data science teams
Approaching a data science organizational setup
Positioning the role of the Chief Data Officer (CDO)
Acquiring resources and competencies
Developing a data architecture
Focusing data governance on the right aspects
Managing models during development and production
Exploring the importance of open source
Realizing the infrastructure
Investing in data as a business
Using data for insights or commercial opportunities
Engaging differently with your customers
Introducing data-driven business models
Handling new delivery models
Ten reasons to develop a data science strategy
Ten mistakes to avoid when investing in data science.
