Editor's Note: This post is the third in a four-part series titled "Unleashing the Power of Data: A Comprehensive Guide to Building Your Private Markets Data Strategy" that explores the importance of having a data strategy framework for private markets and why it is a topic of growing interest, published by Lionpoint’s Front & Middle Office team.
Previous posts outlined the importance of formulating a target operating model, choosing a system, and assessing data frameworks. In this post, we will dive into the elements of executing a data strategy program, highlight the benefits of implementing a data strategy, identify common complexities of an implementation, and detail best-practice implementation elements and methodologies.
This whitepaper delves into crucial considerations for investment companies as they develop their AI strategy. It addresses widespread misunderstandings and core implementation challenges related to AI, followed by a comprehensive analysis of overcoming these hurdles, showcased through an illustrative scenario involving a private equity firm.
To craft a successful AI strategy, builders need clear, actionable guidelines. This document contrasts open and closed AI models, examining their performance and cost implications, providing investment companies with the knowledge to make informed decisions.
Dive into this whitepaper to grasp Lionpoint’s perspective on AI in the private sector. Watch for future discussions on applying AI in private markets.
Contact Dr. Till Blesik, Bernard Hoefsmit, Miguel Castaneda, or Chris Edgar for insights into how Lionpoint’s AI frameworks can enhance your investment strategies and optimize your results.
To read the white paper please click on the link here: AI Strategy for Builders: Debunking the supermodel architecture myth to provide practical solutions for investment companies
