Читать книгу Decision Intelligence For Dummies - Pamela Baker - Страница 39

Traditional data mining tends to box in your thinking and put borders around the questions you ask by simply focusing the work on the data in question.

For example, if you’re mining marketing and sales data for insights, your thinking predictably shifts to a more or less standardized list of questions. That’s natural because it’s how humans organize information as well as their thinking about information.

In other words, people label data to organize it, but those same labels also influence how they think about the information so labeled. It’s weird how that works, isn’t it?

Nonetheless, labels are helpful to a large degree, and you can hardly function in using data without them. They’re so helpful that Google’s head of decision intelligence said that if data scientists and statisticians were left to their own devices, they would have named machine learning “the labeling of things” because these professionals prefer names that label what the thing actually does.

If you feel that this discussion is going in circles now, you’re right. And that’s the best illustration I can think to offer you on how traditional data mining limits everyone’s thinking and querying.

Care to go around again? No? Well, people do continue to repeat many of their efforts in analyzing data anyway. Most often folks do that by refreshing the data and repeating the query — over and over again.

Models, algorithms, and queries are shaped accordingly. Machine learning, also known as AI in common use, is trained on this or similar data where it learns the most often asked questions and the common outputs. Querying is often automated, which results in a list of preselected questions. Even drill-downs in data represented by interactive visualizations are preset.

Those parameters form the box analytics software users find themselves in, the bordering that places restraints on querying, and the reasoning behind the repetitions in actions.