CDAO Germany schedule

• How to embed enterprise AI in a large legacy company
• What are the processes regarding implementation at scale?
• How does one navigate the regulatory challenges around AI?
  
  

Achieving Value at Scale by Implementing AI & Advanced Analytics Across Different Levels of Maturity  

• What is the base data maturity level required for AI powered advanced analytics?
• How can this contribute to value creation in the business?
• Achieve a single, unified view of your customer data at speed with Artificial Intelligence
• Can AI solve the problem of being scalable when dealing with increasing data volumes?
• What is the value of automation & ML-Ops as part of this process?

Panelists include: 

Cornelia Schaurecker, Global Group Director AI & Big Data, Vodafone 

Michael Haszprunar, Chief Data Scientist – My BMW App, BMW 

• Increase resilience and precisions by motivating your data scientists to apply A/B testing
• Building processes that are capable of supporting software deployment and model lifecycle management
• Is it useful to stay cloud agnostic, or are you better off concentrating on one cloud provider and its services?
• Enhance flexibility for data scientists by choosing the right ML-Ops tools for them
• How to gain investment for distinct ML-Ops personnel and maximise the potential of AI

• What can be done to increase synergy with technology to overcome data silos?
• In a digital, and often hybrid/virtual working environment, what can you do to drive collaboration and innovation?
• How to navigate economic challenges with leadership strategy for digital transformation that drives growth

• Is a data driven culture achievable without executive leadership that doesn’t own and drive the usage of data?
• How much training do data champions need to break down silos and promote a culture of collaboration?
• Is a data driven culture even a concern until trusted data is sharable and accessible?
• How much is a data literacy a critical skill for every role?

• How important is the ability to transcend traditional organisation barriers for a data executive?
• Do you need technical expertise to be a strong data leader?
• What are the major technological trends that require more devoted focus?
• Can someone without technical precision see through the hype and deliver a united vision for the whole organisation?

• Utilising AI to facilitate the energy transition 
• What role will quantum computing play in future transformations
• How drones have been harnessed to implement high voltage cables
  
  

• What metrics can be used to measure data attributes?
• Creating a standardised product to be consumed by everyone
• Is data a product in itself or does it need to be tied to a predictive analytics model or automation

Pedro Duarte, Head of Client Data Insights, BNY Mellon

• Overcome data inertia by implementing initiatives with clearly measurable monetary value
• Bring together the story of data with the language of business to maximise investment
• Is a top down or bottom-up approach better for creating a data-driven culture?
• Remove the perception of data as a technical business component to build agility

• What are the different ways data monetisation can be leveraged for a business?
• How to avoid missing and start seizing opportunities for data monetisation
• Navigating the regulatory challenges of data monetisation
• Creating a mind-shift with data as a quantifiable and tangible asset
  
  

• When dealing with vast amounts of data, generate a framework for prioritising what data to bring under governance
• How to build appropriate controls for data governance to mitigate risk
• Establish key compliance benchmarks to continuously monitor and improve master data
• Ensuring data architecture includes standardisation and metadata management