So what if there is a talent gap? Companies that are early adopters of automated data science tools can evade the shortage of developer talent plaguing their competition.
New tools have been developed that simplify data prep, deploy pre-trained AI models, and automate machine learning processes that can short-circuit the need for engineering talent. Then there is also the added benefit of democratizing the data space for non-coders who will ultimately benefit from the insights into the data.
Doug Black filed this report in Enterprise Tech:
The Deloitte analysts report that, anecdotally, early adopters of data science automation tools are realizing time and cost savings, citing an article in ZDNet in which Virgin Australia, using Boston-based DataRobot’s automated machine learning platform, has “cut down the time it takes to build predictive models by up to 90 percent, while boosting accuracy by up to 15 percent.”
Loyalty Lab, a Netherlands company that develops customer loyalty strategies, adopted an automated PredicSis.ai machine learning tool, available in the AWS Marketplace, and combined it with Amazon Redshift and Simple Storage Service technologies to develop ML capabilities with no AI specialists on staff, according to AWS.
Industry analyst firm IDC conducted a study on usage of Salesforce’s low code development Lightning Platform, finding that organizations had a 57 percent faster IT development lifecycle and, over five years of use, a 545 percent ROI.
