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Louis DiModugno, Managing Partner, Data Curiosity

Describe your career to date

After 12 years of active duty as an USAF, Aircraft Maintenance and Logistics Officer during the first Gulf War, I joined the USAF Reserves and continued with an additional 15 years of service, ultimately retiring as a Colonel. Accomplishments include transformation of largest USAF Engine repair facility, supply chain improvement for nuclear weapons refurbishment, and corrosion identification and repair processes for RAF, F-3 Tornado fleet.  

Upon shifting to the Reserves, I joined the first cadre of General Electric’s Six Sigma BlackBelts and Master BlackBelts supporting financial services. With a process focus, data access, refreshed statistical tools, and an emphasis on change management, I initiated Lean and Six Sigma programs at CitiCapital, SAS and The Hartford Insurance Group. I then took my process improvement and strategy expertise to consulting and worked independently and as a Director with PwC. Known as “the Bowtie Data Guy,” I was afforded the opportunity to be the inaugural Chief Data and Analytics Officer at AXA US, in their Life and Annuity organization prior to being spun off as The Equitable.  

At AXA, I was responsible for model development in Life Insurance applications and designing agent-based, virtual population for annuity artificial intelligence (AI) model simulations. I then became the inaugural Chief Data and Technology Officer at HSB, a division of MunichRe, for five years. There, I implemented the first MunichRe cloud environment and Master Data Management initiative. I then started data curiosity as a data strategy and technical evaluation venture focused on preparing organizations for generative AI (genAI) opportunities. Recently selected as the Chief Data Officer for Verisk Analytics. 

What challenges do you see for data in the year ahead that will have an impact on you and on the industry as a whole?  

Large language models (LLMs) will continue to attract an increased amount of attention as capabilities are refined and use cases become more associated with value addition for organizations. Preparing unstructured data in graph and vector databases for consumption in LLMs through retrieval augmented generation (RAG) will be time consuming and a steep learning curve for most organizations.  

Understanding data privacy and security issues associated with LLM rollout will also take focused and dedicated elements of the organization. Using RAG on proprietary data sources will increase the adoption of LLMs and trust in the output. 

How do you see data literacy developing across a) your network and b) the data industry generally? 

Data literacy is important because it enables individuals to understand, interpret, analyze, and effectively communicate with data, leading to better decision-making and improved outcomes. Organizations will need to invest in education efforts to ensure executives, scientists and users are all talking the same language and know that they can rely on data sources for truthful, accurate input into modeling efforts. 

How do you see the industry preparing for AI adoption and change management? 

The industry is preparing for AI adoption and change management by recognizing the need for a people-centric approach. The emphasis on keeping the human in the loop will help to establish trust of the AI tools. This involves supporting employees through the adoption of AI, restructuring job roles, and fostering an organizational culture that embraces AI as a tool for enhancing work rather than a human replacement and threatening job security. 

Louis DiModugno
has been included in:
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