mark: That's a good question. First, I want to say that across JPMorgan Chase, we view this as an investment. And whenever I talk to senior leaders about what we do, I never talk about costs. It's always an investment. And I firmly believe that. Ultimately, what we're trying to do is build an analytics factory that can deliver AI/ML at scale. And that type of factory requires really sound strategy, efficient platforms and computing, solid governance and controls, and incredible talent. And for any size organization, this is a long-term investment and not for the faint of heart. You have to be really confident to do this and do it well. Deploying this at scale can be really difficult. And when you think about AI/ML, it's important to make sure you have controls and governance in place.
We are 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 we serve in terms of maintaining the financial health of these companies. And at JPMorgan Chase, we're always keeping that top of mind, thinking about what we actually invest and what we don't do and the types of things we want to do and what we won't do. But at the end of the day, we need to understand what's going on with these technologies and tools and make sure that we have a very, very high likelihood of accountability to regulators and ourselves. This is our standard. Do we truly understand what's behind the logic, what's behind the decisions, and do we feel comfortable with that? And without that comfort, we cannot move forward.
We don't release a solution until we make sure it's sound and good and we understand what's going on. In terms of government relations, we are very focused on that and have a large presence globally. And at JPMorgan Chase, we're focused on engaging with policymakers to understand their concerns and share those concerns. And I think we're all united in that we think this technology can be used forever. We want it to do good things. We want to make sure it stays in the hands of great actors and that it's not used to harm our clients or clients or anything else. And this is where I think businesses and policymakers need to come together and have one unwavering voice on the way forward. Because I think we are highly aligned.
honor: You touched on this a little bit, but businesses rely on data to do many things, including improving decision-making, optimizing operations, and driving business growth. But what does it mean to operationalize data, and what opportunities can companies find through this process?
mark: I mentioned earlier that one of the most difficult parts of the CDAO's job is actually trying to understand and determine what the priorities are, what types of activities need to be undertaken, what types of data problems are big or small, etc. I would say that trying to operationalize this is just as difficult. And one of the biggest things that has been overlooked for a long time is that the data itself has always mattered. It's in our model. We all know about it. Everyone talks about data every minute of every day. However, data has often been thought of as a waste in some product, some process, some application, feature, or app, and not enough time has been spent to ensure that that data is, in fact, considered an asset. , the data is of high quality and fully understood by humans and machines.
And as we enter the world of generative AI, where machines are trying to do more and more things, I think it's becoming more and more clear now that it's really important for machines to understand data. If humans have trouble processing data assets, what do you think machines will do? And we're focused on our data strategy, which ensures that humans and machines can understand data equally. Because of that, data operationalization has become a big focus not only for JPMorgan Chase, but also for the Chase business itself.
We've been on a multi-year journey to really improve the health of our data, make sure our users have the right types of tools and skills, and do it in a secure and tightly managed way. And there's a lot of focus on data modernization. This means changing the way we publish and consume data. The ontology behind it is really important. Cloud migration ensures that your users are in the public cloud and using the right types of tools and features to do the right computing. And real-time streaming, streaming enablement and real-time decision-making are really important things to us and the data ecosystem needs to transform in a significant way. And investing in data allows you to harness the power of real-time and streaming.
honor: When it comes to data modernization, many organizations have turned to cloud-based architectures, tools, and processes in their data modernization and digital transformation journey. What does JPMorgan Chase's cloud migration process for data and analytics look like, and what best practices would you recommend to large enterprises undergoing a cloud transformation?