Читать книгу Decision Intelligence For Dummies - Pamela Baker - Страница 41

Perhaps no buzzword is more touted in the data analytics industry than actionable outcome. To be fair, some outputs are actionable and some of those actionable items can even be fully automated — no humans needed outside of those who built the machines that are now doing all that work.

However, more often than not, actionable outcomes are insights that might enable an action. That’s quite a different concept than analytics that can deliver actual decisions, or a rated range of them, complete with expected impacts.

Decision intelligence aims to change outputs from insights to decisions, at any scale and by using varying blends of human and machine tactics. This is what Google’s Cassie Kozyrkov means when she so often describes the difference between traditional data science with machine learning, and decision intelligence as “the difference between those who make microwave ovens and the cooks who use them.” It’s the recipe and the outcome that matter, she says, because the chef has no need to build a microwave or even understand how it works.

The focus is shifting, in other words, from data explorations and building more technology to delivering a specific payload.