Sarah Gadd, Head of Data and AI Solutions at Credit Suisse, shares how she’s demystifying data science, advancing its data strategy in partnership with its Group Chief Data Officer and accelerating the company’s path to the cloud
What would you say have been your greatest achievements at Credit Suisse over the past 12-24 months?
Part of what I’ve been doing for the last 12-24 months is helping people understand the fact that AI or machine learning, which is pretty much what we use right now, is not some scary robots in the room. We’re not talking about Robocop. What we’re talking about here are things can that actually help people and inform people with better decisions.
I’ve been holding a lot of knowledge sessions internally and externally, with data scientists, students, business leaders and project managers, on how they should think about AI ethics, focusing on making this a more natural, tangible topic.
We’re a large wealth management company, with employees spread globally, across many different offices and divisions. We have been breaking the divide down in the space of data analytics and data science, so people can understand what colleagues are doing.
So, whether I’m creating a visualization or doing analytics or building machine learning in one part of a division, it’s getting that out there and adding transparency, so the broader bank can understand and potentially cross-leverage the work that has already been done, or the lessons learned.
Then, on the platform side of things, we’ve been building a ‘data access framework’, tying data governance to data analytics. So, leveraging data governance for a data shopping cart experience, where we can shop for quality data assets from across the firm and streamline secure data access for analytics.
How do your roles and responsibilities overlap and contrast with those of Credit Suisse Group Chief Data Officer Adrian Pearce PhD?
I sit as part of what you would think of as the technology organization, part of what we call group CIO which is a subset of the overarching umbrella of group COO. Group CDO is also under our COO function and I work closely with Adrian Pearce, the Group Chief Data Officer for Credit Suisse.
We have a data management framework at the firm, where we focus on five different capabilities, from architecture to security, to governance, to quality, and then usage and insights. I co-run usage and insights with a colleague from CDO and sit [with] part of [the] CDO management team to help drive the data strategy.
Together, we spent a good part of the last couple of years creating a revenue-generating tool that identifies opportunities for the wealth management side, using external data combined with internal data.
What are the key things you’ll be looking to achieve in the coming 12 months?
One of my top priorities is always around people. We need to make sure that we nurture internal talent, as well as bring in and grow external talent, especially in data spaces. That’s not data science specifically. It’s also things like data engineering; understanding how to really construct a data pipeline.
Making sure that we’re providing good career paths and trajectories, promoting and encouraging a diverse landscape, is a focus for us. How we bring people in from the start of their career journeys and help them grow in the direction they want to go and also to provide the internal mobility to support that growth.
On the delivery side, ‘path to cloud’ is a big thing at Credit Suisse. So, how do we support that from a data architecture and tooling perspective?
I run the big data platform for Credit Suisse globally, looking at how we identify the hybrid model that works. Not all workloads are going to go on cloud. So, how do you keep your data in one place, be that in cloud or on-prem, and then execute loads you want to execute in a suitable environment for that load.