Linda F. Powell has over 30 years of experience in the finance industry, including commercial banking, banking supervision, and supporting economic research. She spent most of her career with the Federal Reserve System, covering regulatory reporting, bank examination, and economic research in addition to data management. She was the Chief Data Officer at the Treasury Department’s Office of Financial Research and the Consumer Financial Protection Bureau. In 2018, she joined Citibank as the Global Head of Data Governance & Data Reporting, and in 2021, she joined BNY as the Deputy Chief Data Officer. Her experience covers data operations, data governance, data platform design and implementation, data architecture, and analytics/data science. Linda’s data management initiatives were instrumental during the 2008/2009 financial crisis, and she led the interagency initiative for the Legal Entity Identifier (LEI/GLEIF). She has published several papers on the topics of data standards, metadata, and data strategy. She has a B.A. in Economics from Rutgers University and an M.S. in Quantitative Finance from George Washington University.
Recently, in an exclusive interview with CIO Magazine, Linda shared insights on her career journey, the future of data management, leadership principles, personal hobbies and interests, future plans, words of wisdom, and much more. The following excerpts are taken from the interview.
Hi Linda. Can you tell us about your early career experiences and how you got started in the field of data management?
I started my career in internal audit and banking. This taught me the importance of controls and how to analyse operational and technical systems. Having started my career before data management was a profession, I grew along with the profession, having one foot in business and the other in evolving technology. I was interested in the business insights from data and got proficient in the tooling to enhance the insights. Throughout my career, I have always followed what I found interesting and enjoyable rather than chasing titles or money. Working with the Research functions at the Federal Reserve consistently provided interesting projects and brilliant partners.
What do you love the most about your current role?
Seeing the impact of the data management program on commercial value is fun. The last few years have been particularly fun with the advent of Gen AI. Improving data quality while making data more accessible and understandable enables high-quality AI. I loved that we looked at data governance as an enabling tool to go further and faster rather than as a must-do restriction. In all of my roles, what I’ve loved the most is partnering with business teams to help bring business value to accomplish the impossible and make operations more efficient and controlled.
How do you see the field of data management evolving in the next 5 years?
Data management has gone through several significant evolutions during my lifetime. During the Big Data era, people talked about the 3 Vs. Volume, Variety, and Velocity. Big data focused on Volume and Variety. Gen AI builds on that but amplifies Velocity. Analysis is done so fast with AI. As a result, I think the field of data management will need to focus on how to enable the fast analysis of data. This increases the need for high-quality data and especially metadata to enable reasonable and accurate analysis.
How do you think data professionals can stay current with the latest trends and technologies in the field?
Read, listen, play. Personally, I’ve enjoyed using AI in both my personal and professional life to expedite and simplify tasks and analyse documents. I’m also a big advocate of attending conferences, especially those that are outside of your comfort zone. You can learn a lot by being introduced to topics you don’t already understand.
What skills or qualities do you think are essential for success in data management and governance?
I often say that data management professionals need three skills: domain knowledge, technical skills, and curiosity. The most important thing is curiosity. I can teach someone the domain knowledge or the technical skills, but I can’t teach curiosity. Curiosity helps you identify the cause of anomalies, problems in data quality, and unreasonable statistical insights. Learning the math that underlies statistical analysis during my Master’s in Quantitative Finance taught me that statistics aren’t always right. I think that curiosity with added skepticism will be essential in using gen AI.
What personal or professional philosophies have contributed to your success, and how have you applied these principles in your career?
I have several golden rules that I try to follow, but I’ll admit that I sometimes falter. The first is to be authentic and to be kind. Always be careful not to undermine your colleagues – it doesn’t make you better. In terms of leadership: 1) it is important to remember that everything you say as a senior leader carries more weight than when you were a staffer (we replay everything our boss says to us a dozen times), 2) never complain about your management to staff (it makes everyone uncomfortable and creates stress), 3) don’t assume that you are always right / consider the alternative opinion (because sometimes you won’t be right).
Is there a particular person you are grateful for who helped get you to where you are?
Yes. I was incredibly fortunate to have an amazing leader as my first boss. He taught me more than how to be a good banker. He taught me how to be kind and tough at the same time. He taught me to appreciate the diversity of teams and opinions and how to adapt my style to bring out the best in the people around me. He put me into challenging positions at an early age and helped me find my way through them. When faced with a personnel challenge, I’ve often asked myself, “What would Al do in this situation?” He helped shape me into a leader rather than a manager.
What are some of your passions outside of work? What do you like to do in your time off?
I’m pretty active. Since the pandemic, I’ve traded in my soccer cleats for golf shoes, and I’m probably not doing any more triathlons, but when the weather is nice, you’ll find me running along the Hudson or biking through Central Park. In recent years, I’ve enjoyed competitive storytelling through NPR’s Moth events. I also serve on the Board of a nonprofit that runs 17 homeless shelters around NYC with programs to help families out of homelessness.
What is your biggest goal? Where do you see yourself in 5 years from now?
My biggest goal has always been to improve things, whether that is international data standards, people’s lives, or the way a company operates. I love to bring it to life. Like many people, I’m intrigued by Gen AI. I hope to be the person who makes Gen AI more reliable and valuable, improving not just business outcomes but trust in the technology itself.
What advice would you give to aspiring data leaders looking to break into the financial services industry?
Understand the math and models underpinning AI and apply that knowledge to risk management and the control environment. Data risk management will become a more critical piece of data management. As AI evolves, risk management needs to evolve to address the risks we don’t yet know or understand.