White Paper: Evaluating Data Science Infrastructure During M&A Deals

In the world of mergers and acquisitions, the game is changing. As businesses increasingly recognize the value of data and AI, evaluating a company's data infrastructure has become a pivotal aspect of any successful M&A strategy. Colleen Qiu, the Head of Data at Lindenlab, shares her extensive expertise in this must-read white paper.

Discover how Colleen's experience in AI, data science, and analytics at Fortune 500 firms and tech startups played a crucial role in M&A processes. Learn how data-driven insights shaped the post-merge growth strategy of companies like PayPal and Chegg, and get a glimpse into the data-centric world of the Metromile-Lemonade acquisition.

Colleen Qiu takes you on a journey through the five key areas that define the value of a company's data infrastructure during M&A:

1. DS/ML Platform And Technical Stack: Learn about the ML stack and integration that shape the core of AI solutions. Discover why platform consolidation is a game-changer during M&A.

2. Models And Algorithms In Production: It's not just about the number of models but their real-world impact on business.

3. Roadmap For Bringing Models To Production: Understand the importance of deploying models and the role of MLOps in streamlining this process. Different lead times for different models? Colleen explains.

4. Quality of Big Data: Discover why the quality and quantity of data are paramount in building AI solutions.

5. Talent & Team Culture: A peek into the significance of a strong team culture and talent evaluation during M&A, and how teams can be integrated for maximum value.

Whether you're a Buyer looking to assess data infrastructure or a Seller preparing to showcase your assets, Colleen's insights provide invaluable guidance. Don't miss the opportunity to gain a competitive edge in the world of mergers and acquisitions by unlocking the power of data.

To access the complete white paper and unlock the secrets to successful M&A in the data age, click here

➡️ Evaluating Data Science Infrastructure During M&A Deals

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