CDO Matters Podcast

Robert Hawker discusses practical data quality in a strategic and value-driven approach, highlighting its importance for data leaders in overcoming challenges. The conversation covers aligning data strategy with MDM initiatives, justifying investments in data quality, and leveraging AI to streamline rule codification.

Exploring next gen semantic layers and their value in driving improved search, analytics, and generative AI. Discussions on the evolution of semantic layers, challenges of integrating operational and analytical data stores, benefits in data architecture and supply chain transparency. Insights on product passports, metadata alignment, and global data ecosystems for mutual benefit.

In this episode, Malcolm and Jason discuss the importance of CDO's being AI ready and taking a holistic approach to change resilience. Topics include leveraging data for decision making, the value of design thinking, and commercial focus. They also explore AI readiness, large language models for innovation, product-oriented thinking, and maximizing data value in business. Strategies for building a change-resistant organization are also highlighted.

Explore the evolving trends in data management with a focus on data fabric, data products, MDM, and AI. Learn about the challenges and benefits of data frameworks, the significance of harmonizing data for regulatory purposes, and the transition to more distributed models. Delve into the distinctions between aggregates and MDM, AI adoption trends in Europe, and the potential for automation through generative AI. Discover the concept of defining 'good' data quality, the evolution and potential of data fabrics, and the convergence of analytical and operational stores. Dive into blockchain integration for enhanced governance and security in data management at scale.

Former nuclear physicist turned data scientist, Santona Tuli, shares actionable insights for data leaders implementing AI and data science. Topics include measuring unseen phenomena, challenges in data observability, impact of variables on business performance, data-driven decision making, leveraging knowledge graphs, challenges with data, and the future of AI.

The podcast discusses top data predictions for 2024, including investments in AI data, software, and consulting, the potential decline of Chief Data Officers in the private sector, and the increasing demand for CDOs in the public sector. It also explores the concept of data product ownership and its value in data management, highlights the importance of fundamentals and driving value with AI, and explores the potential implications of AI's advancement in mathematics and encryption.

Malcolm welcomes Erik Zwiefel, CDAO of Microsoft Americas, on this live episode. Topics include AI readiness for CDOs, data governance and responsible AI, the future of technology and LLMs, leveraging tools for data quality, and understanding data lake house architecture.

This podcast explores the top data trends of 2023, including the growth of AI, the demise of the data mesh, and advancements in language-based deep learning models. It also discusses the importance of mindset in data quality, the future impact of ETFs, blockchain, and data valuation, and the significance of valuing and measuring data impact.

The podcast discusses the importance of creating a data culture within organizations and the challenges faced by data and analytics leaders. It explores the role of organizational culture and HR in fostering a data-driven culture. The relationship between data culture and intuition in decision-making is also explored. Additionally, the podcast highlights the need for data leaders to quantify business outcomes and model the impacts of decisions using AI and decision science.

Mark Stouse, Founder and CEO of Proof Analytics, discusses the frustration organizations face in realizing benefits from data management investments. He highlights the need for CDOs to prioritize specific business needs and outcomes. The podcast covers challenges of operationalizing analytics, the value of automation and targeted AI, establishing causality in predictive analytics, and the current state of data science functions.