Executive Briefing: The black boxInterpretability, reproducibility, and data management

The growing complexity of data science leads to black box solutions that few people in an organization understand. Mark Madsen explains why reproducibilitythe ability to get the same results given the same informationis a key element to build trust and grow data science use. And one of the foundational elements of reproducibility (and successful ML projects) is data management.

The growing complexity of data science leads to black box solutions that few people in an organization understand. You often hear about the difficulty of interpretability—explaining how an analytic model works—and that you need it to deploy models. But people use many black boxes without understanding them…if they’re reliable. It’s when the black box becomes unreliable that people lose trust. Mistrust is more likely to be created by the lack of reliability, and the lack of reliability is often the result of misunderstanding essential elements of analytics infrastructure and practice. The concept of reproducibility—the ability to get the same results given the same information—extends your view to include the environment and the data used to build and execute models. Mark Madsen examines reproducibility and the areas that underlie production analytics and explores the most frequently ignored and yet most essential capability, data management. The industry needs to consider its practices so that systems are more transparent and reliable, improving trust and increasing the likelihood that your analytic solutions will succeed.