As business leaders strive to get the most out of their analytics investments, democratized data science often appears to offer the perfect solution. Using analytics software with no-code and low-code tools can put data science techniques into virtually anyone’s hands. In the best scenarios, this leads to better decision making and greater self-reliance and self-service in data analysis — particularly as demand for data scientists far outstrips their supply. Add to that reduced talent costs (with fewer high-cost data scientists) and more scalable customization to tailor analysis to a particular business need and context.
However, amid all the discussion around whether and how to democratize data science and analytics, a crucial point has been overlooked. The conversation needs to define when to democratize data and analytics, even to the point of redefining what democratization should mean.
Fully democratized data science and analytics presents many risks….
This article was written by Joel Shapiro and originally published on hbr.org