Читать книгу Blockchain Data Analytics For Dummies - Michael G. Solomon - Страница 25

In the preceding section, you learned about using analytics models to make predictions of future outcomes. There can be tremendous value in prediction, but you can use analytics also to set the outcome and tell you how to get there. Think about it. It's one thing to predict next week’s sales, but wouldn’t it be cool to set your next week’s sales goals and let your analytics models tell you how to get there? With good analytics models, it's possible.

Predictive analytics basically gives you an equation: y = mx + b (yes, that’s a simple one and the same as the point-slope form of a line). Your model provides values for m and b. Your data provides a value for x and you solve for y. Simple algebra.

Prescriptive analytics is a little different. Prescriptive analytics ask the question: “If I choose a value of y, what value of x will get me there?” In other words, you choose a value of y (maybe your goal for next week’s sales), and then solve for x. After you know x (perhaps x represents the number of prospect calls you need to make), you know what it will take to reach y (your sales goal). At its core, it's still simple algebra.

Even though the algebra is simple, putting prescriptive analytics into practice can be tricky. In algebra, equality is reflexive, which means you can read left-to-right or right-to-left. Technically, models should work the same way, but they don’t always work that simply. Prescriptive analytics can provide some guidance on reaching goals, but you always have to take that guidance with a grain of salt. Try your model’s recommendations, and then evaluate the results. Fine-tune your changes, and then try it again. The best use of prescriptive analytics is as a good suggestion, not a surefire approach to reaching goals.