Summary

I greatly enjoyed hearing about Cortnie’s career, and her trajectory to becoming a leader in the field.

In developing successful data science capabilities, I agree with her advice on needing to align data and analytics to the broader business strategy.

Cortnie made it clear that successful data leaders need to be politically savvy in a business context, and that CDOs need to be “really good politicians” – and have strong negotiation and influencing skills . The people and politics elements can make or break a data science initiative in my experience. Cortnie and I agree that senior leaders also need to have data charisma – the ability to influence and negotiate with data – beyond strong data literacy skills, which we also discussed.

We discussed the challenges organizations face developing responsible and ethical AI and the reasons that AI ethics boards can fail. Cortnie pointed out that it’s difficult to find people with the appropriate skills and diversity of opinion and background to sit on these boards. That difficulty can set a board up for failure.

Cortnie outlined her 12 guiding principles (The 12 Tenets) for how organizations can develop ethical, fair, and trusted AI solutions. One of the key themes that emerged was that people need agency in the data-driven decisions that affect them, with the ability to review and provide feedback on the data used in the decision. It’s the responsibility of everyone – not just the tech developers – to help build responsible and socially aware AI solutions. I encourage you to think about how you might apply the 12 Tenets to your own work.

She also stressed the importance of listening to staff who raise objections about processes or projects, with staff at all levels being empowered to speak up and raise concerns, such as ethical issues.

One of the most important topics Cortnie raised was how to work out what is important to key stakeholders so you can ensure you develop solutions that meet their requirements. This can be non-trivial and challenging. Her advice was to try and work out what they actually want and need from the following:

• What they tell you
• What they tell others
• What they won’t admit – such as their career motivations and political machinations

She also suggested identifying who you need to get on board as key sponsors, and figuring out how to attach and align your work to their priorities.

Cortnie also offered some valuable advice for designing business metrics that measure progress against strategic goals. In my experience, this is context specific, as designing good business metrics in the public sector, for instance, can be more challenging than in the private sector. One reason is that sometimes the metric you’re optimizing is a second-order effect – for which you may not have suitable data – such as providing actionable and timely intelligence to another government agency.

For data scientists, Cortnie suggested not making the mistake of thinking that career progression only means moving up into leadership roles – and thus becoming less “hands-on” with the technology.

She looks for the people she hires to have attributes such as a flexible and adaptable mindset, enjoying experimentation and exploration, and being a team player.