Introduction: Why do 80% of data science project fail? (1/11)

Artificial intelligence (AI) or data science in general requires a change in thinking from the first lighthouse projects, ideally even before. Ready-made, technically correct solutions are often not used because acceptance of the new technology is too little. This series is about the main mistakes why 80% of AI projects are failing. In less than one hour you learn how to avoid these obstacles.

This video is part of the playlist "Standard Process for Establishing Data Science (SPEDS)" by OWC:
(1/11) Introduction: Why do 80% of data science projects fail?
(2/11) Anatomy of an Organization: Who should be involved in data science projects?
(3/11) Obstacles: What obstacles you have to remove in the way to establishing data science in your organization?
(4/11) Let’s take SPEDS: What are the steps of the Standard Process for Establishing Data Science (SPEDS)?
(5/11) SPEDS - step 1: Data Science Ambassadors: What are the requirements and tasks for data science ambassadors?
(6/11) SPEDS - step 2: Upper Management: What does the upper management need to know about Data Science in order to make decisions?
(7/11) SPEDS - step 3: Departments and Use Cases: How to identify first data science use cases and how to start with the work?
(8/11) SPEDS - step 4: Individual Level: What do you have to consider for each stakeholder of your data science project?
(9/11) SPEDS - step 5: Lighthouse Projects: What are the ideal projects to get started?
(10/11): SPEDS - step 6: Penetration: How to spread the sparkle of data science in your organization?
(11/11) SPEDS - step 7: Data Science team building: When does it make sense to build your own data science team and when is the right time for it?