Citizen Data Science and the Democratization of Analytics - InformationWeek

The ongoing shortage of data scientists has been well documented. Even as the business world grows increasingly digitized and reliant on big data modelling and analytics to drive value and profit, those possessing the requisite education and expertise in mathematics/statistics, data prep, programming, and distributed computing to meet data science challenges are rare birds. The ability to make sense of the enormous troves of transaction, customer, and equipment data across digitized industries has become a premium skillset, and the recent explosion in machine learning (ML) and artificial intelligence (AI) capabilities has compounded the problem. Now that we can access the compute power and data volumes necessary to operationalize tasks such as pattern recognition, anomaly detection/diagnosis, customer analytics, pricing and predictive planning, we want ML systems that can learn to automatically prepare and perform data science functions with minimal programming. Thus, the irony: Machine learning is often deployed as a kind of digital surrogate for the data scientist, but one that requires the skills of a data scientist to be brought into existence.