When the 2024 National Football League (NFL) college draft kicked off recently, we were glued to the action. (Nick’s beloved New England Patriots got their quarterback of the future 🤞, while Bruce’s alma mater, LSU, had not one but two top ten first-round draft picks, including the Heisman Trophy winner, Jayden Daniels going at #2 💪)
Similar to how pro football teams create championship-worthy talent mash-ups, insurance companies must often decide whether to invest in young, raw startup talent capable of building tech infrastructure from the ground up, or fall back on the experience and immediate impact offered by a veteran or existing technology solution. This build or buy choice presents a strategic dilemma for insurers on a budget which closely parallels NFL teams’ draft/trade/free agency strategy while working under a strict salary cap. Let’s explore how the seemingly disparate worlds – building a winning football team and building a winning insurance technology infrastructure – share surprising similarities.
The Enticing Rookie: Potential vs. Polish
Building a technology solution in-house is akin to drafting a rookie. You have the raw talent and potential for greatness, but it comes with development needs. The initial investment is in time and resources dedicated to building the solution from scratch. There’s a thrill of customization – tailoring the technology to perfectly fit your needs. However, just like a rookie athlete, the solution might be riddled with bugs and require extensive training for your team to use it effectively.
The Proven Veteran: Immediate Impact vs. Long-Term Fit
Buying existing technology, like trading for a veteran player (or signing one through free agency), offers immediate gratification. You acquire a proven solution that’s already been battle-tested and has a track record of success. Integration is often faster, allowing you to see results quickly. This is similar to a veteran player who can step in and contribute right away. And, while this may seem the more expensive option at the time of purchase, the total cost of ownership (TCO) over the lifetime of the solution is far and away the best, and most cost effective, strategy.
Scouting the Landscape: Needs Assessment
Before making the “build vs. buy” decision, carefully assess your team’s needs, just like an NFL team meticulously scouts potential draftees and trade targets. Is there a readily available solution that meets your core needs? Is customization essential, or can you adapt your workflow to fit an existing technology? Analyzing the competitive landscape is crucial.
The Long Game: Development and Support
An investment in technology – whether you are building a tech solution in house or buying from an external solution provider – requires a long-term commitment. If you choose to build, you’ll need a dedicated development team for ongoing maintenance, updates, and bug fixes. On the other hand, buying existing technology comes with ongoing costs for licensing, maintenance from the vendor, and potential future upgrades.
The Wildcard: Culture and Team Fit
Just as a player’s personality and fit within the team dynamic are crucial considerations in the NFL, company culture plays a role in the “build vs. buy” decision. Can your team build a new, homegrown solution that will function as effectively as one from an established external solution provider? Evaluating team capabilities is essential. Does your company have the in-house expertise to develop and maintain a custom solution, or is relying on external expertise a better option?
The Winning Play: There’s No One-Size-Fits-All Answer
Like building a winning NFL team, the best approach often involves a strategic mix of both approaches. You might draft promising rookies for core functionalities while acquiring proven veterans to address immediate needs. Similarly, consider building crucial, custom features in-house while leveraging existing, best-in-class technologies for complementary functionalities.
The “build vs. buy” decision is a complex one, but by understanding the parallels with the NFL’s draft/trade/free agency strategy, you can equip yourself to make informed choices that drive innovation and propel your business forward. Remember, the winning play lies in thorough analysis, a keen understanding of your needs, and the willingness to leverage both established veterans and the exciting potential of the rookies.
In future articles, we will present and explore a framework that will simplify the analysis to decide when to buy and when to build using an economic methodology called the Total Cost of Ownership (TCO)
About Bruce Broussard
Bruce F. Broussard, Jr. has more than 30 years of P&C and Life & Annuities insurance experience focusing on insurance data solutions. Bruce has served in various roles, including solution & data architect, project & program manager, and insurance application manager. Prior to founding Percipience, Bruce led the Data & Analytics practice at MVP Advisory Group and served as SVP & General Manager of the Data Solutions Business Unit for Insurity. He led the product strategy, development, client implementations, and sales for Insurity’s DataHouse product (formerly IEV). DataHouse became the leading P&C data solution under Bruce’s leadership. Previously Bruce was an Associate Partner for IBM’s Insurance Consulting Practice, where he led IBM’s global insurance data practice. Bruce also served as VP, Application Development for Pan-American Life Insurance Company, responsible for all US and International systems. Bruce holds a Bachelor of Science degree in Computer Science from Louisiana State University and completed the IBM Executive Program at Wharton School at the University of Pennsylvania and received his Machine Learning certification from the Massachusetts Institute of Technology (MIT). Bruce holds FMLI and ACS designations.
Bruce F. Broussard, Jr. has more than 30 years of P&C and Life & Annuities insurance experience focusing on insurance data solutions. Bruce has served in various roles, including solution & data architect, project & program manager, and insurance application manager.
Prior to founding Percipience, Bruce led the Data & Analytics practice at MVP Advisory Group and served as SVP & General Manager of the Data Solutions Business Unit for Insurity. He led the product strategy, development, client implementations, and sales for Insurity’s DataHouse product (formerly IEV). DataHouse became the leading P&C data solution under Bruce’s leadership. Previously Bruce was an Associate Partner for IBM’s Insurance Consulting Practice, where he led IBM’s global insurance data practice. Bruce also served as VP, Application Development for Pan-American Life Insurance Company, responsible for all US and International systems.
Bruce holds a Bachelor of Science degree in Computer Science from Louisiana State University and completed the IBM Executive Program at Wharton School at the University of Pennsylvania and received his Machine Learning certification from the Massachusetts Institute of Technology (MIT). Bruce holds FMLI and ACS designations.
