As a Global CDAO, how do you create alignment between Tech and Business Leaders in your organization to develop a digital transformation strategy that includes analytics modernization, cloud migration, data

democratization or data fabric/data mesh to enable innovation and transformation in your organization. Join your peers to discuss, why despite the strategic and competitive advantages of a data-centric

approach, it may take a lot of effort to get others to see that it is the right thing to do for the corporation and understand the perceived gap between business, leadership, and IT.

• Getting validation in starting a corporate digital transformation initiative

• Working in marketing, sales, finance, and other business roles who need to understand the goals and value IT brings

• Framing the benefits of digital transformation without having to read excessive technical jargon

• Convincing doubters that digital transformation is the right thing to do

• Convincing others in their corporation of ITs value

As a global CDAO, how you manage and distribute your data both at the local and global levels directly impacts business value. This boardroom discussion will examine lessons learned and best practices in the deployment of global data management to meet the new business, technical and regulatory requirements for modern data management in a multinational and global context. 

• Identify best practices to manage your data in the context of specific business processes

• Build sophisticated global data management to step up to the scale, speed, diversity, and complexity

• Establish a thriving global organization by leveraging global data for innovative use cases

• Implement integrated and repurposed data, ranging from advanced analytics to real-time business management

• Integrate security and data protection into your best practices

As business environments become more complex and volatile and organizations try to stay afloat in a sea of data, many are turning to Decision Intelligence (DI) to act as the needed missing link between data and better decision making. DI uses AI-powered information to make more efficient and accurate decisions at scale. It effectively strips away complexity and accelerates translation from data to decisions so that organizations can derive more value from their investments in data and machine learning models, and stay ahead of business opportunities by arriving at decisions faster.

• How DI differs from AI and fuses BI, data science, management, and decision modeling

• Use cases for decision intelligence – optimizing human augmented strategic decisions and automated, AI-enabled tactical decisions

• Designing decision models and processes in the context of business outcomes, performance, and stakeholder behaviors

• Eliminating bias while accommodating the value of human intuition, knowledge, and judgment