As we’ve established earlier in this post series, Data Science is a process, with quite a lot of repetitive elements. Many Data Science projects involve a familiar set of tasks to identify, clean and prepare data, before finding the best model for the scenario at hand. And despite the mystique around the whole profession, many Data Scientists spend a lot of time complaining about all this repetitive work. But any repetitive process is ripe for automation, and Data Science is no exception. Enter the field of “AutoML”.
The 2010s were a big decade for Chief Data Officers: from a standing start at the beginning of the decade, CDO has risen to become an indispensable C-suite role, with almost two thirds of Fortune 500 organizations hiring one.
But the role of CDO, especially outside of the US, is still poorly defined, and CDOs are frequently not set up for success within their organizations. Is the job a poisoned chalice?