Summary: You can buy the best hardware in the world, and hire the best mathematicians. You can write brilliant machine learning algorithms. However: if you do not have a way to produce information that is relevant to your organization and successfully communicate it to them, your entire data science department is the functional equivalent of a paperweight that costs more than raw plutonium. So let’s take a minute to talk about organizational structure, information flows, hiring, training, and data’s social signal-to-noise-ratio. Kimberly Stedman Data Scientist Motiga Kimberly Stedman does big data in the games industry. She was originally a field anthropologist, and has lived in five developing countries. Kim has a Master’s in Social and Organizational Systems Analysis. Kim specializes in the design and management of the social systems that surround data technologies. In other words: Awesome! We’ve got a better algorithm running on faster hardware! … Now what? Kim gave a 5-minute Ignite talk on this topic: How to Build an Effective Data Science Department. Kim also blogs as K2. She wrote Brosie the Riveter, a comedic article on gender issues in gaming and STEM.
