Data science talent is infamously difficult for data and analytics (D&A) leaders to find. Organizations know their data is a latent asset that can create business value. But they have no easy way to get or develop the talent they need to take advantage of it, since the D&A labor market is so competitive. By seeding their data science team with internal hires, Eastman laid the foundation for a productive team before making expensive and commonly counterproductive talent investments.
Download this case study to learn more about how Eastman delivered value from the beginning without heavyweight external data talent.