Play it on your favorite podcast platform
Episode notes
Over the next several years, the data landscape will undergo significant changes. As AI becomes more prevalent, businesses will become more reliant on applied analytics and machine learning.
These advancements bring up questions about strategies for maintaining ethical, trusted data, free from bias. And the application of this data to further the goals of purpose-driven organizations.
Listen as CDOs explore the critical role of data strategies in today’s rapidly evolving business landscape, discussing topics from fueling innovation and transforming the business with data insights to preparing for and utilizing generative AI. This conversation will also touch on the importance of diversity, especially when it comes to data analysis and better decision-making.
Featured experts
- Claire Thompson, Group Chief Data & Analytics Officer, Legal & General
- Gary Burnette, Chief Data Officer, Kyndryl
What you will hear
"Having those strong foundations in place are the things that enable you to move more quickly, especially in the AI space journey and AI space. Those that have really strong data foundations and good data management principles will be able to move very quickly. With the introduction of Gen AI, there's more of a view about how you bring risk in a lot earlier into the process as well from a governance perspective to review the use cases and make sure that you're happy that ethically that fits with your brand, your organization, and the customers that you're serving"
– Claire
“I think there is a real opportunity with using some of the unsupervised machine learning to identify the unknowns that you have within there. So you're not seeking a specific answer, you're using the data to identify the unknowns. That for me is the power of some of what we're able to now do in that space. You can flip it around and think of it in a more positive way; you can use it for identifying things you've not considered before.”
– Claire
"…You can't presume what combinations of data, or even data elements within that data, are going to actually produce the result you want. And so bringing a diverse set of people, whether that diversity is age, geography, cultural, gender, ethnicity, or whatever it may be, right? Bringing those different points of view often help clarify what the model needs to look like, how it needs to be constructed, and equally important, or perhaps even more important, what the inference feels like when you're looking at the results on the other end."
– Gary
