This is a five-part blog series from an interview that I recently had with Grace Lee, Chief Data and Analytics Officer and Dr. Yannick Lallement, Vice President, AI & ML Solutions at Scotiabank.
Scotiabank is a Canadian multinational banking and financial services company headquartered in Toronto, Ontario. One of Canada's Big Five banks, it is the third largest Canadian bank by deposits and market capitalization. With over 90,000 employees globally, and assets of approximately $1.3 trillion Scotiabank has invested heavily in AI, Analytics and Data and aligned an integrated function that is well supported by all business lines. Although their journey has zig zagged in impact along its way, the organization now has a strong foothold in bringing consistent value and impact to the business. We can all learn a great deal from these words of wisdom in what it takes to advance AI successfully in a large enterprise.
This five-part blog series answers these five questions:
Blog One: How is the advanced analytics function structured and what have been some of the most significant operational challenges in your journey?
Blog Two: What does it take to set up an AI/ML Solutioning Competency Center?
Blog Three: How are some of the operational challenges like Digital Literacy impacting your journey?
Blog Four: What are some of the operational lessons learned?
Blog Five: What does the future hold for Scotiabank’s Advanced Analytics and AI function?
Advanced Analytics and AI at Scotiabank
How is your organization structured in terms of analytics, data and AI?
“If you've been following our recent history, you would know that we've had a lot of fits and starts. We've had some aborted attempts to bring analytics and data to the Bank in a meaningful way. And through this journey, we have learned from our mistakes to enable us to move from siloed analytics, data, and AI professionals into a unified centre of excellence where we have integrated teams across the various business lines and functions. Prior, we had data in a primarily governance function in our risk management function where they were primarily focused on data quality but did not do much data enablement or delivery.
We currently have over 500 analytics, data, and AI professionals, and about half are skilled in AI. We have quite a diverse team in terms of skills, ranging from business analysts, user-centric designers, data scientists, data engineers, NLP specialists, ModelOps engineers, as well as resources skilled in data and AI ethics. Our people are primarily in North America (75%) and the balance of our talent is located in different global regions, in Mexico, South America (Peru, Chile, Colombia), the Caribbean, etc.
We are proud that our team consists of people that can ensure that our AI modelling and ML solutions are designed and deployed effectively from inception to consumption” (Verbatim: Grace Lee).
What were some of the most significant lessons learned in your organizational restructuring journey?
“Simply having AI, analytics, and data as capabilities does not mean that we are driving value, and if we don't drive value, we don't have a place in the Bank. So, one of the things we said we must do differently is, rather than put the function in technology, in operations, or in marketing where these teams often live, we will have data and analytics report directly to the business lines. We had to ensure that the value was from the business users using the solutions and driving tangible value” (Verbatim: Grace Lee).
What were some of the technical lessons learned?
“We learned that by bringing data and analytics tightly together, aligned with technology, and by having priorities and shared goals set by the business, it's less about the sophistication of the model and it's more about the meaningfulness of the outcome.
We have learned that we must work together closely in this ecosystem that we've built. This allows us to activate the virtuous cycle between data, analytics, and technology because technology is necessary to make data; data is necessary to make models; and models must be reintegrated into technology in order to get in front of a customer and employee by being embedded into the operating process. If we don't ensure process integration, we are not working in harmony.
For example, if we built an AI model where data pipelines are built one-off and not sustainable, when something changes in the technology, the models will stop properly functioning and supporting the business – this scenario is antithetical to the way that we think about delivering value. When we talk about bringing data and analytics together, it's not just data governance, it's data delivery. Our concept of a reusable authoritative data set underpinning models to ensure operational sustainability is factored in from the onset and is core to our strategy.
This allows us to provide an abstraction layer that allows the end-user data to remain consistent and persist - so if something changes in the systems upstream, we are still able to deliver that same high quality data to all of our models. This means our reports and our processes are, in a way, relatively insulated from technology change. In other words, as you know in AI, often 80% of the problem is in the data sourcing; with well-managed and accessible data, we expect it to be closer to 20%.” (Verbatim: Dr. Yannick Lallement)
Note: See Blog Two: What does it take to set up an AI/ML Solutioning Competency Center?
