Large enterprises rarely publicize their internal meetings, but financial services giant JPMorgan Chase & Co. is taking the unusual step of telling the world about its first global conference of software engineers and data scientists that convenes today at the company’s Global Technology Center in Dallas.
The event, called Devup, was created and designed by some of the 40,000 software engineers who work for the company. About 500 people from 30 countries were chosen to attend the three-day event, which tackles some of the most complex problems the firm faces in areas like application modernization, cloud migration and uses of artificial intelligence.
“Engineers love to solve complex problems and when you think about the size and scope of the problems we have to solve — leaning in with AI and analytics, accelerating the transition to public cloud and applying machine learning — it makes the job more exciting,” said Lori Beer, JPMorgan Chase’s global chief information officer.
JPMorgan Chase expects the conference to become an annual affair that not only showcases the advanced technology being built at the company but also aids in attracting talent in a white-hot job market. More than 20% of the company’s employees, some 55,000 people, now work primarily in technology roles.
“This is an indication of the shifting role that technology and data science play in our business,” Beer said. It’s also “part of our overall talent strategy to drive innovation and support engineers in their career journey. We want to make sure people outside the bank understand the culture we’re creating and the new opportunities for software engineers and data scientists.”
A key goal will be to accelerate the use of the roughly 500 petabytes of data the company possesses. JPMorgan Chase last year went public with its plans to adopt a data mesh, an emerging governance model that distributes data ownership to business functions across the firm and treats data as a product. Cultural resistance has been called one of the biggest obstacles to the successful adoption of data mesh concepts.
Distributed data ownership raises questions about “how we organize, prepare and think about our data as core products,” Beer said. “We have to label it for context and have business owners accountable for that data. That’s important as we scale machine learning and modernization.”
Another top priority is to “build a platform and ecosystem for the data scientists,” she said. “One of the biggest challenges is how to get them their data quickly. We’re looking to reduce the time it takes by 70%.”