McKinsey May 2, 2021
Developing artificial intelligence and analytics applications typically involves different processes, technology, and talent than those for traditional software solutions. Executives who possess a solid understanding of the basics can ensure they’re making the right investments in their tech stacks and teams to build reliable solutions at scale. We’ve created an interactive guide to help.
Defining environments
Lab
Due to its experimental nature, analytics development work—including data exploration, experimentation with predictive models, and development of prototypes through rapid iterations— must be performed in a “lab” environment that’s separate from other systems so that it doesn’t hinder normal business operations. Lab technologies must be flexible and scalable to handle the changing demands of the analytical approach (eg, new data, new...
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