Evan Harp sat down with J.P. Morgan Asset Management’s Danius Giedraitis to discuss data, AI, and the future of asset management.
Evan Harp: Tell us a bit about yourself and your work with data.
Danius Giedraitis: I lead a global business intelligence and analytics team within J.P. Morgan Asset Management. We work a lot with trying to use data to drive distribution strategies. So, it's something I'm passionate about, it's something I've seen the growth of firsthand.
I've been in the industry for almost 15 years. The big “aha moment” for me came at the culmination of a lot of time and energy with our stakeholders in distribution leadership and marketing leadership. One of the things that really felt like data was finally at the table was when we worked with some of our distribution heads to actually create and drive a new coverage strategy. We're talking about a strategy that requires millions of dollars of investment. It requires hiring human capital. It requires a lot of analysis for the market opportunity.
Historically, data was a foreign language to a lot of leadership. They relied on intuition and anecdotal evidence. Now data has clearly become the centerpiece for distribution strategy. It's embedded in the decision-making framework and continues to integrate and entrench itself into the fabric of our organization, and hopefully many other organizations as well.
Why data matters
Harp: A lot of evidence supports the power of data. McKinsey & Co. has noted that organizations that leverage customer behavioral insights and data outperform peers by 85% in sales growth and more than 25% in gross margin. Despite that, financial services is often seen as trailing other industries when it comes to deploying data. Do you agree finance is behind, and if so, why do you think that is?
Giedraitis: I think there's no shortage of data being generated. For me, the opportunity feels like harnessing it and applying it in the most powerful, intelligent ways, and I would say there are probably worse industries. At the same time, I do think that there's probably still quite a bit of space for finance to more dynamically apply data insights.
And so, I do choose to agree somewhat with that statement, but with the understanding that there are probably some ways that finance actually is quite far along relative to other industries. But I think there's a lot more opportunity to be a little bit more thoughtful with where and how we're using the data that we're generating.
Harp: Where do financial institutions trip themselves up when it comes to data, and what can they do differently?
Giedraitis: I think some data teams feel like every number or attribute or field that they can report on, they should. So many organizations think that more data equals better outcomes, and I think it is important to consider, with respect to all metrics, that a few metrics matter more to drive the PnL, to drive the customer experience, to drive the customer engagement.
The thing organizations can do differently is to be as thoughtful and intentional with understanding what those metrics actually are. I think a lot of well-meaning effort is put into generating all sorts of new data, at the expense of not collecting and deploying insights from the key metrics and data points that drive the signals that you’re interested in.
It is also critical to define what your systems of record are like. Because, often, what we see is people copy data to this server and this database and that database, and so you have this spaghetti bowl of data that's copied all over. Define your systems of record. When you have those defined and you have processes to maintain the quality of it, then think about how you start to stitch that data together. Because when you start to stitch the data together, I think that’s when you start to provide more relevant insights, more relevant reporting, and more relevant metrics for the stakeholders.
All of that, I think, goes a long way in terms of getting people comfortable with using data to make decisions.
How to change an organization’s relationship with data
Harp: That makes a lot of sense, but how do organizations actually navigate implementing those changes?
Giedraitis: Organizational change is in some ways just as hard, if not harder, than the actual technology investments, the data investment, etc. However, what I have found is to drive that broader change, understanding, education — it's really hard to centralize it from a small data team.
What works well is finding the power users, the data-literate individuals across different functions and teams to become extensions of what you're trying to achieve as a data organization. It's very difficult to drive that organizational buy-in without really clearly creating a strategy of education, of building the trust. It starts with leaning on the more data-literate in an organization and building from there.
Once you have the organization properly leveraging data, it can help your market strategy and client experience. Ultimately, you're trying to deliver to a client, a customer, or stakeholder, whoever.
Now, 10, 15, 20 years ago, there were maybe one or two channels of engagement. That has completely exploded in the last decade. And so I think one of the things that's really important to think about and that we have found so critical to understand is, what are our clients interested in? How do you connect their engagement profile across fragmented systems and channels? Has this prospect engaged with one of our webcasts? What other webcasts have they attended? Who are they outside of their professional life? What have our salespeople talked to them about? Are they deeply embedded in our ecosystem, or have they maybe just purchased a product or two? These kinds of insights can help teams deliver the right offers in the right way and drive actual impact.
AI continues to be top of mind
Harp: Let’s pivot to AI. Though controversial in some corners, many organizations are embracing the potential efficiencies AI and machine learning can provide. How should organizations be approaching this new technology?
Giedraitis: I think what we're trying to do, and what a lot of organizations are trying to do, is when you build these machine learning algorithms that deliver some suggestion or some recommendation to a stakeholder, their behavior then can also inform the next iteration or the next version of that capability. A lot of social media organizations are doing this. You think about TikTok, and their algorithms. You think about Instagram, and their algorithms. Getting people more embedded into using data and thinking about data as part of their daily practice is helpful, and it also builds the capabilities and makes them more robust. How people provide feedback and how their behaviors inform future versions of those capabilities is important to consider.
To me, it all bubbles up together, right? And so getting people comfortable, getting people engaged, getting feedback up front and over time evolves the capabilities themselves. It all works together. If you're missing certain pieces of that, the system starts to sort of break down and you're not moving forward.
What I'm seeing is, within our industry, teams need the right data delivered the right way to drive impact, and that AI and machine learning enhances decision-making but still requires human expertise. They can do more with the same, is how we've been framing it, and I think taking out the “no joy” parts of a job away is a big deal. AIso, I think there's a lot of risk and compliance assistance that maybe AI can provide.
Of course, when you're talking about transforming the client experience, there's still some human in the loop that will need to persist for some period of time.
When it comes to modernizing platforms, that's a hard decision to make because it takes a lot of energy, a lot of investment, and it's sometimes the foundational transformation that's needed to enable the AI capabilities. So, it's not necessarily seen as exciting, and the outcomes aren't felt as acutely by senior leaders right away. But I think, with time, we're going to see more and more from the inside moving out, because that to me feels like the larger opportunities and larger green spaces down the road.
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