The Buzz 1: Data governance the organizational framework…to understand where your data originated, how it was handled, and the goals your AI platform and machine learning models set out to achieve.” [https://www.datarobot.com/wiki/data-governance/]

The Buzz 2: “Companies have tons and tons of data, but [success] isn’t about data collection, it’s about data management and insight.” [Prashanth Southekal at the Data for Business Performance Institute]

The Buzz 3: “You can have all of the fancy tools, but if [your] data quality is not good, you're nowhere.” [Veda Bawo at Raymond James]

Big data and artificial intelligence (AI) are accepted as key drivers for the digital transformation of organizations and today’s societies. To succeed with data-driven innovation, companies around the world need to develop an agile mindset and experiment with new technologies.

However, with key concerns about data access and data quality in analytics and AI initiatives, applying data governance is often perceived as rigid and non-value adding.

Is data governance an inhibitor suffocating technical innovation or a mandate that facilitates the success and further adoption of AI and machine learning (ML)?

We’ll ask Christine Legner, Maria Villar and Wolfgang Epting for their take on AI and Data Governance – Friends or Foes?