With more than 5,000 branches across 48 states and 80 million customers, each with its own unique requirements to satisfy its customers’ financial needs, a clear data strategy is key for JPMorgan Chase. According to Mark Birkhead, firm-wide chief data officer at JPMorgan Chase, data analytics is the oxygen that breathes life into the firm to deliver growth and improve the customer experience.

Providing first-class business in a first-class way for clients and customers applies to every part of the firm, including its heavy investments in data analytics, machine learning, and AI. Using these advanced technologies, JPMorgan Chase can gain a deeper understanding of the breadth and specificity of the needs of the customers and communities it serves.

“It means using our data to drive positive outcomes for our customers and our clients and our business partners. And it means using this to actually help our customers and clients manage their daily lives in a better, simpler way,” says Birkhead.

At their best, a strong data strategy along with AI and machine learning adoption can free employees from tedious tasks to focus on high-value work. Reaching this extended intelligence — humans and machines working better together — means having the right deployment strategy. It’s key to understand both the potential and the limitations of these tools to make sure your enterprise is investing wisely in the areas where technologies like AI and machine learning can offer the greatest value.

“At the end of the day, what we’re trying to do is build an analytic factory that can deliver AI/ML at scale,” says Birkhead. “And that type of a factory requires a really sound strategy, efficient platforms and compute, solid governance and controls, and incredible talent.”

Adopting this vision at scale is a long-term investment that requires strong conviction, adherence to governance and controls, and operationalizing data. One of the most challenging aspects of this, Birkhead says, is defining your data priorities.

“Everyone talks about data every minute of every day. However, data has been oftentimes, I think, thought of as exhaust from some product, from some process, from some application, from a feature, from an app, and enough time has not been spent actually ensuring that that data is considered an asset, that that data is of high quality, that it’s fully understood by humans and machines.”

This episode of Business Lab is produced in association with JPMorgan Chase.

Laurel Ruma: From MIT Technology Review, I’m Laurel Ruma and this is Business Lab, the show that helps business leaders make sense of new technologies coming out of the lab and into the marketplace.

Our topic is data and analytics. Building a global data strategy requires a strong understanding of governance, regulations, and customer experience for both internal and external customers. As technologies like AI emerge, the opportunity expands for real-time learnings and making better decisions.

Two words for you: data strategy.

My guest is Mark Birkhead, who is the firmwide chief data officer at JPMorgan Chase.

This podcast is produced in association with JPMorgan Chase.

Welcome, Mark.

Mark Birkhead: Thank you for having me, Laurel. It’s great to be here.

Laurel: Let’s start here. You were recently appointed to firmwide chief data officer for JPMorgan Chase. Previously you were the chief data and analytics officer at Chase and JPMorgan Wealth Management. Can you give us some insight into how your new role factors into the firm’s data strategy?

Mark: Absolutely. My new role as the firmwide chief data officer will be focused primarily on driving this strategy and solutions, that maximize the impact that data can have on our clients and customers across the globe and doing it in a highly governed and controlled ways. Data plays a huge part in our firmwide strategy. It’s been described by several of our senior leaders as the oxygen that powers the firm. And I truly believe that. Data analytics has propelled so many of our businesses, including our consumer bank and business bank, our commercial bank, our wealth management businesses, and our payments business globally. And its impact continues to grow in more meaningful ways every single day and month.

Strong data analytics capabilities really do provide the foundational underpinnings for our core business activities, but it’s actually fueling the growth of our businesses in meaningful ways. This addition is driving productivity, delivering insights that help our customers grow their businesses, and enabling our bankers and advisors to deliver elevated customer experiences.

Laurel: Thank you Mark for giving that context. As a global firm, you talk about delivering first-class business in a first-class way for clients and customers. Could you tell us how data and analytics, AI and machine learning are used to improve outcomes for your customers?

Mark: Absolutely. When we talk about first-class business in a first-class way, it really applies to every part of our firm and we’re investing heavily in data analytics, machine learning, and AI. But this is not new to us. We’ve been utilizing AI and ML for many, many years in many different ways. The Chase Analytics team actually will celebrate a sixth anniversary next March with the same mission and objective. Again, this is not new to us, but when we think about applying first-class business in a first-class way to the new set of AI capabilities, the new set of LLMs [large language models], the new set of generative AI, it means to us really honoring our customers’ expectations when it comes to privacy. It means using our data to drive positive outcomes for our customers and our clients and our business partners. And it means using this to actually help our customers and clients manage their daily lives in a better, simpler way.

I’m going to actually spend most of my time talking about my former role as the chief analytics officer for Chase and JPMorgan Wealth Management, but really our AI efforts across the globe are very similar to what has been happening at Chase and JPMorgan Wealth Management. It’s really been focused on improving the financial health for our customers and our clients. Today, JPMorgan Chase serves over 80 million customers in the US and we use advanced analytics to deliver best-in-class experiences and to respond to the needs of our customers. And our customers have all kinds of situations at any given moment in time. And at one moment we’re planning for college and other times we’re dealing with some difficult times in a family situation. And being able to have the right tools for our bankers, for advisors, for our call center agents to utilize is really important to us.

And I mentioned the breadth of data analytics as being the oxygen of the firm, and that really is reflected in the Chase business. And one of the hardest parts of the CDAO [chief data and analytics officer] job is to determine what investments to make and where to focus our attention when it comes to solving data problems and also determining where we have to lead with AI/ML and when we actually don’t. For us, there’s a couple of things that we always have to lead in given the nature of our business. We have a branch network of 5,000 branches. It covers all over 48 states. And we’ve got to lead with geospatial analytics and that heavily utilizes AI and ML to determine the optimal placement of our branches, of our network, of our community centers, and for the staffing within those branches. We also have over 60 million digitally active customers.

We have to lead in product analytics, experimentation, understand the customer experience in a journey, and how they interact with our products across multiple channels. It might start in a branch, end up in a mobile app, and end up in a call center, but all that has to be stitched together. We also have to lead when it comes to preventing fraud, and it has really become a difficult task given what’s going on across the world. But protecting our customers, from these types of acts, is incredibly important to us.

And we also need to make sure that within our branches, within our customers, they get the best experience possible, which means really using data analytics, machine learning, AI to understand our customers and communities in deeper ways. And in fact, for our 5,000 branches, there’s not a lot of similarities. And we actually have to prepare playbooks for these branches to make sure our employees are trained on these types of situations, the needs of their customers and clients so they can actually produce the best possible service. The only way to deliver all that at our scale is through leveraging data analytics, machine learning, and AI.

Laurel: Touching on that, at its best artificial intelligence, machine learning, and a robust data strategy can automate those tedious tasks to free people up to focus on high-value work. How do you think about that as an ongoing effort?

Mark: We think about this a lot and innovation has cycles, and that includes my field as well. But those cycle times are really changing and becoming more compressed, and that’s drawn a lot of attention and scrutiny particularly to the field of AI. At the end of the day, with the emergence of LLMs and generative AI, there’s just more opportunities to enhance the work of our employees day-to-day. Sandy Pentland, who helped form your MIT Media Lab, really described a few years back to our employees, this interaction is extended intelligence, humans and machines working better together. And this is actually one of our highest priorities at JPMorgan Chase, leveraging machines to help our employees do their jobs better for our customers and for our clients. And today we’re exploring experimenting with LLMs in a number of capacities. But it’s really important to understand what these tools can do well and what they can’t, and then making sure that we’re actually organizing ourselves against them and making the right investments in people and resources in those areas where these actual tools can help us to the greatest extent.

It’s also important that we focus on the governance and controls around this. And all that comes into play when it comes to figuring out what we do with these tools and how we apply them. I was meeting with our global marketers a couple of weeks ago, and every time I do this and talk about our plans for generative AI across the firm or at Chase, talk about the impact it can have on JPMorgan Chase, I get two types of questions. One is, “What does this mean for me and my employees?” And I think the answer is, with any type of technology, it’s not exactly going to take your job, but people who do use this technology will. And that’s the same thing with AI. And the only caveat to all of this is I think when it comes to this type of technology and capability, particularly with generative AI, those that understand what this does well and what it doesn’t, will actually have a leg up and be better positioned to actually succeed.

The second question I always get is, “If we’re always using the same tool for every company, the same model, aren’t we all going to sound the same?” And that’s where I think the relationship of the business and our models and data scientists has to evolve. Every time we build a model or an AI solution, we always engage with the business. But I think given what’s going on now with LLMs and generative AI, it’s really important to mature that model. The thinking around design and analytics needs to change to ensure that we incorporate the brand voice, the marketers’ voice into these solutions to make sure that the content that we deliver using these tools reflects the brands that the customers have come to know is really important. And this entire operating model has to evolve. And I think it presents really exciting opportunities to go deeper with customers in meaningful ways, but it requires the model to change.

Laurel: Speaking of having a leg up, successfully deploying AI and machine learning has become a competitive differentiator for large enterprises. What are the challenges of deploying AI and machine learning at scale? And then a second big question is, as regulations for AI and machine learning evolve, how does the firm manage government regulations?

Mark: That’s a great question. And first, I would say across JPMorgan Chase, we do view this as an investment. And every time I talk to a senior leader about the work we do, I never speak of expenses. It is always investment. And I do firmly believe that. At the end of the day, what we’re trying to do is build an analytic factory that can deliver AI/ML at scale. And that type of a factory requires a really sound strategy, efficient platforms and compute, solid governance and controls, and incredible talent. And for an organization of any scale, this is a long-term investment, and it’s not for the faint of heart. You really have to have conviction to do this and to do this well. Deploying this at scale can be really, really challenging. And it’s important to ensure that as we’re thinking about AI/ML, it’s done with controls and governance in place.

We’re a bank. We have a responsibility to protect our customers and clients. We have a lot of financial data and we have an obligation to the countries that we serve in terms of ensuring that the financial health of this firm remains in place. And at JPMorgan Chase, we’re always thinking about that first and foremost, and about what we actually invest in and what we don’t, the types of things we want to do and the things that we won’t do. But at the end of the day, we have to ensure that we understand what’s going on with these technologies and tools and the explainability to our regulators and to ourselves is really, really high. And that really is the bar for us. Do we truly understand what’s behind the logic, what’s behind the decision-ing, and are we comfortable with that? And if we don’t have that comfort, then we don’t move forward.

We never release a solution until we know it’s sound, it’s good, and we understand what’s going on. In terms of government relations, we have a large focus on this, and we have a large footprint across the globe. And at JPMorgan Chase, we really are focused on engaging with policymakers to understand their concerns as well as to share our concerns. And I think largely we’re united in the fact that we think this technology can be harnessed for good. We want it to work for good. We want to make sure it stays in the hands of good actors, and it doesn’t get used for harm for our clients or our customers or anything else. And it’s a place where I think business and policymakers need to come together and really have one solid voice in terms of the path forward because I think we’re highly, highly aligned.

Laurel: You did touch on this a bit, but enterprises are relying on data to do so many things like improving decision-making and optimizing operations as well as driving business growth. But what does it mean to operationalize data and what opportunities could enterprises find through this process?

Mark: I mentioned earlier that one of the hardest parts of the CDAO job is actually understanding and trying to determine what the priorities should be, what types of activities to go after, what types of data problems, big or small or otherwise. I would say with that, equally as difficult, is trying to operationalize this. And I think one of the biggest things that have been overlooked for so long is that data itself, it’s always been critical. It’s in our models. We all know about it. Everyone talks about data every minute of every day. However, data has been oftentimes, I think, thought of as exhaust from some product, from some process, from some application, from a feature, from an app, and enough time has not been spent actually ensuring that that data is considered an asset, that that data is of high quality, that it’s fully understood by humans and machines.

And I think it’s just now becoming even more clear that as you get into a world of generative AI, where you have machines trying to do more and more, it’s really critical that it understands the data. And if our humans have a difficult time making it through our data estate, what do you think a machine is going to do? And we have a big focus on our data strategy and ensuring that data strategy means that humans and machines can equally understand our data. And because of that, operationalizing our data has become a big focus, not only of JPMorgan Chase, but certainly in the Chase business itself.

We’ve been on this multi-year journey to actually improve the health of our data, make sure our users have the right types of tools and technologies, and to do it in a safe and highly governed way. And a lot of focus on data modernization, which means transforming the way we publish and consume data. The ontologies behind that are really important. Cloud migration, making sure that our users are in the public cloud, that they have the right compute with the right types of tools and capabilities. And then real-time streaming, enabling streaming, and real-time decision-ing is a really critical factor for us and requires the data ecosystem to shift in significant ways. And making that investment in the data allows us to unlock the power of real-time and streaming.

Laurel: And speaking of data modernization, many organizations have turned to cloud-based architectures, tools, and processes in that data modernization and digital transformation journey. What has JPMorgan Chase’s road to cloud migration for data and analytics looked like, and what best practices would you recommend to large enterprises undergoing cloud transformations?

Mark: We’ve been on this journey for quite some time across JPMorgan Chase and globally. And we have a really solid relationship with our technology partners, with our cloud providers, and we really have ensured that as we move up to the cloud, we do it safely and thoughtfully with a sound strategy and governance and controls. And that’s been the first and foremost piece I would say with regard to a business like Chase and JPMorgan Wealth Management, which into itself is incredibly large and we’ve talked about this publicly many, many times. It is something that requires conviction and a sound data strategy, but at the end of the day, we are not just moving to the public cloud. We’re going to do that with modernized data, but we’re also going to improve governance and controls while improving the user experience.

And to do all of that, it’s a massive undertaking. And to ensure that our data is discoverable and easily usable where our analysts require us to make informed decisions when it comes to these investments, as well as these different types of choices and staging of the work product. And as we think about this, and my advice to others would be to do the same. If you look at the user experience when it comes to your data scientists and your modelers and how they spend their time, what their challenges are, what your analytic priorities are, all those have to be brought together before you actually start building out a data strategy. Otherwise, you’ll be building things that you may not need. And this is already hard enough, why not make it easier by understanding what you’re trying to build, what user population is looking for and then building to that specifically and then staging out in the right appropriate ways?

And that’s been our journey. And we have these milestones. We have goals and everything else. We have OKRs [objectives and key results], we have product teams, we have data engineers. Everyone is aligned and doing this, and we’re focused on doing this in the right way. We’re also focused on ensuring that we can do this in many cloud platforms, not just one. And that requires modern pipelines. It requires us to organize our data differently and inventory it in a certain way and describe it in ways that are easily understandable. This is really difficult work, but it’s well worth the investment. Even if you have to go slow and make little bits of progress year over year, this will absolutely pay off.

Laurel: Speaking of that payoff, working across the company is crucial to meet goals. What is your talent and skills strategy to mobilize cross-functional teams to ensure a data literate workforce that uses both domain and technical knowledge like data science?

Mark: Absolutely. And I’m really proud of our focus on talent, not only across JPMorgan Chase, but at Chase specifically. It has been really difficult to find great talent in this space. And once you have them, you want them to stay, you want them to grow, you want them to feel supported, and you want them to feel challenged, and you want them to be able to experiment and to work and design solutions that are elegant, that meet the needs of customers and that are advanced. And as we think about all of this, there’s a number of buckets that we’re really focused on.

First, in terms of attracting talent, we do have a very robust campus program. We have a very robust internship program, and we have a very robust rotational program that actually spans the firm. And in Chase, this rotational program has existed for many, many, many years. And it really gives data scientists and aspiring data scientists a chance to spend a couple of years with us, move across the bank and the firm, and to really understand what it’s like to work in various different types of settings and before they land in a job or land in a function or a field.

And that’s one piece. It’s really understanding the community, the new talent coming in at campuses, out of graduate programs, out of Ph.D. programs, and making sure that we have the right types of programs that meet their needs. And that’s one piece. We’re also really focused on our existing talent and our existing talent is absolutely incredible. And they come to us because they want to continue to grow. They want to continue to learn. And we’re heavily investing in training to make sure that learning development opportunities are available to our existing employees and design for the different types of data users and the different types of career goals that they have. And that’s a great thing about our field today. There are so many avenues with which you can go.

And it’s really exciting to actually be able to pick in adventure, pick a career with a firm like ours, at JPMorgan Chase. And then as I mentioned before, we are really focused on our communities and giving back. And in addition to our campus programs, we also try and invest in talent that may not actually come to work for us ever. And we do have hackathons. We bring in hundreds of college students twice a year for our campuses. We pay for everything. And they go through a twenty-four-hour hackathon where they work with other teams, meet other students, work with JPMorgan Chase volunteers, and really try to solve problems for a local nonprofit. And those hackathons really are investment in the next generation of analytic talent, but it also gives them an opportunity to work with real data, with real problems, and to learn a little bit and to help build the community.

And then lastly, we have programs around Data for Good, and our employees absolutely love this. We partner with over 30 nonprofits over the past two years to help them solve their needs. And nonprofits are amazing at serving their communities and finding needs. They’re not always great at bringing tech stacks or digital solutions or using data or analytics to help their nonprofits. And we have great partnership with them. All of this encompasses our talent strategy. It’s focused on engaging students early on in the process, experienced hires, developing our core talent, and giving them opportunities to do things beyond their core job, like giving back to their communities.

Laurel: Mark, thank you so..