Vanguard is an American investment management company founded on May 1, 1975, and based in Malvern, Pennsylvania. As of May 31, 2024, according to the firm’s website, Vanguard manages $9.3 trillion dollars in assets for 50M+ clients worldwide, while operating 423 funds through 17 global offices, making the firm one of the largest global money managers. Vanguard has been an innovator in the money management business since its founding. John Bogle, Vanguard founder, is widely credited with the creation of the first index fund for individual investors. Vanguard has been a pioneer in low-cost investing for individual consumers for nearly five decades.
I recently spoke with Ryan Swann, Chief Data Analytics Officer for Vanguard, whom I have gotten to know in recent years through his participation in Chief Data Officer roundtable discussions that I regularly organize and conduct, as well as through participation in data and AI industry leadership events. We were both among the featured keynote presenters at the Deloitte CDAO Program held at Deloitte Academy earlier this year.
Swann assumed the data leadership role at Vanguard in early 2021, and before that held data leadership roles in private industry and served as Chief Data Officer & Director of Data Analytics for the U.S. Office of Government Wide Policy (OGP). He was co-founder of the first White House Data Cabinet. Swann had previously served in data leadership roles in the U.S. Department of Treasury and was an aviation logistics officer in the U.S. Marine Corps before that. He holds an MBA from The Wharton School and MPA from Harvard University.
As Chief Data Analytics Officer for Vanguard, Swann’s mandate includes driving a data culture where he is responsible for transforming data into a strategic asset for Vanguard that delivers business value. Swann explains, “At Vanguard, data, analytics and AI are more than strategic assets. Whether Vanguard is using data and analytics to create alpha so that investors can retire securely, or we are using data and analytics to help our advised clients understand what to do when markets go up or down, data is at the center of decision making.”
Swann elaborates on the Chief Data and Analytics Office (CDAO) mandate at Vanguard, noting, “First, we’re responsible for the entire data life cycle. That means how we manage, store, and use data to fulfill our mission to provide investors with the best chance for investment success.” He continues, “Second, we use analytics to improve the client experience, scale advice, optimize investment and business operations, and reduce risk.” Swann concludes, “Lastly, we are responsible for data defense to ensure we are using data and deploying analytics in a responsible way. Every day we focus on how we incorporate responsible practices—think privacy by design—into how we use data to ultimately help our clients.”
Optimizing Data, Analytics, and AI Responsibilities and Results
I ask Swann about the organizational structure of Vanguard and where data and AI sit within the firm. Swann explains that Vanguard has established a Center for Analytics and Insights (CAI), which sits within the CDAO function. He comments, “The CAI is responsible for developing advanced analytics, which includes capabilities like artificial intelligence, machine learning, data science, and behavioral science, to drive business outcomes.”
Swann elaborates, “We’ve moved from a siloed structured to a unique federated structure of our data & analytics teams (over 800 employees, or “crew”) under the CDAO to scale our data capabilities,” noting, “This transformation means that data and analytics organizations report into the CDAO but the teams remain aligned with the business through OKRs, budget, and co-location.” Swann continues, “This approach allows us to create deeply rewarding career pathways and enhanced personal development plans for data professionals so they can learn best practices and develop new skills.”
Career development of data, analytics, and AI professionals is critical to the CDAO mandate within Vanguard. Swann comments, “As we thought about how we scale a data culture across the organization, we developed a unique strategy that we believe would both maximize talent development and scale our ability to be a data-forward organization.” He explains, “Through co-location and close alignment of business objectives, data and analytics professionals gain valuable business insights that are vital for business outcomes and then use those insights to drive to inform their work.”
Swann elaborates on the benefits of close business and technology alignment, noting, “Our data, AI and technology professionals are co-located with the business professionals and share the same OKRs. We partnered closely to raise the business acumen and the data/AI acumen on both sides.” He continues, “This approach helps break down silos to establish a unified data ecosystem that enables seamless data integration, sharing, and collaboration across the organization.” Swann concludes, “Often you can’t tell who in the meeting is from technology or who is from the business.”
Using Data, Analytics, and AI to Help Clients Achieve their Financial Goals
Improving and innovating to continually improve the client experience is central at Vanguard. To enhance the client experience, Swann describes how Vanguard uses data, analytics, and behavioral science, to help “nudge” clients to achieve their goals. Swann explains, “We use proactive messages that are aligned to the goals that the client has already set.” He elaborates, “Sometimes, emotion or life gets in the way. We want to remind our clients that they have a goal – like, for example, they want to save for retirement or prepare for their child’s education expenses.”
Swann notes that Vanguard employs a “next best action” experience to share proven strategies that are important for clients, and offers some real-world examples of how Vanguard is helping clients achieve their financial goals:
- Vanguard uses “nudges” to increase clients’ savings in their 401(k) when appropriate. Swann explains, “In 2023, we tested our approach by offering personalized nudges to clients encouraging them to take healthy investment actions such as maximizing 401K matches from employers.”
- On the advice side, Vanguard provides capabilities, such as Multi-Goal Solver, which leverages machine learning to help clients understand if they are on track for multiple goals at once – e.g. retirement, health care savings, kid’s college education.
- In 2024, the Vanguard team continues to provide value for its clients across these “interventions”. Swann explains, “New interventions have been launched including, an SMS notification to not miss the IRA contribution deadline, a new 529 account savings experience, and, capitalizing on our new dollar-based trading for ETFs, an Automatic Investment Plan experience for ETFs.”
Delivering Business Value using Data, Analytics, and AI
Delivering business value from data, analytics, and AI investments is core to the mission of the CDAO function at Vanguard. Swann explains, “There is a strong connection between the business strategy and the data strategy of Vanguard.” Swann continues, “The close collaboration between the CDAO and business partners provides valuable and continuous insight, and together we identify opportunities to optimize processes, build efficiencies, and reduce friction across key operational areas.” He adds, “At Vanguard we drive a data, analytics, and AI driven culture by aligning our people, process, and technology around a mission driven objective.”
I ask Swann about what constitutes an effective data strategy for the company. Swann comments, “An effective data strategy is one that drives maximum value and considers both an offensive (value-driven) and defensive (risk-driven) approach.” He explains, “Proactively bringing together the offense of what we do, for example, creating value through GenAI; and defensive approaches, like privacy by design, helps us unlock more value for our investors.” Swann adds, “We also view data as a product, because we create data and avail it responsibly to various parts of the organization to, for example, create a better client experience, improve outcomes, or reduce fraud.”
Swann continues, “At Vanguard, we take a quantitative and qualitative approach to measuring data impact. Quantitatively, we use a model to assess data return on investment using a net present value (NPV) approach by testing the measurable impact of our new model or capability against a control group. The NPV approach helps us articulate the incremental value we bring to the organization. We also pair that quant-heavy approach with a qualitative view on anecdotal impact, productivity metrics, and partner feedback.”
Driving Innovation through Experimentation and Learning
Vanguard has a history of embracing experimentation to drive innovation and learning across its businesses. Swann comments, “Vanguard embraces experimentation by identifying and evaluating new and exciting opportunities every day to optimize how it can help clients across the globe.” He explains, “For example, we know how effective our sales executives and advisors are, so how do we help those individuals be more productive? We’ll do an A/B test or an experiment to see if we can increase their capability or automate manual tasks, so they can be more productive.”
Reflecting on his role and mandate as the Chief Data and Analytics Officer for Vanguard, Swann concludes, “Across the organization, the CDAO partners to unlock synergies and capabilities that we haven’t before, which get us to the next level of maturity”. He continues, “This helps us quickly move capabilities and wins to other parts of the business, which reduce development costs to give back to our investors.”
As an innovator and industry leader in the half century since its founding, Vanguard continues to embrace the future with energy and excitement. Swann summarizes, “Technology continues to power Vanguard’s client experiences, investment products, and advice. Our approach to innovation is rooted in our long-term perspective.” He adds, “At Vanguard, we have a history of leveraging data, analytics, and AI to create value for our clients.” Swann concludes, “Teamwork is what makes this dream work.”
This post was originally published on here
