HBR Guide to Data Analytics Basics for Managers (HBR Guide Series) by Harvard Business Review

Author:Harvard Business Review [None]
Language: eng
Format: epub
Publisher: Harvard Business Press
Published: 2017-06-28T21:00:00+00:00

Find the Appropriate Data

Once you verify that your problem is suitable for machine learning, the next step is to evaluate whether you have the right data to solve it. The data might come from you or from an external provider. In the latter case, ask enough questions to get a good feel for the data’s scope and whether it is likely to be a good fit for your problem.

Ask Questions and Look for Mistakes

Once you’ve determined that your problem is a classic machine learning problem and you have the data to fit it, check your intuition. Machine learning methods, however proprietary and seemingly magical, are statistics. And statistics can be explained in intuitive terms. Instead of trusting that the brilliant proposed method will seamlessly work, ask lots of questions.

Get yourself comfortable with how the method works. Does the intuition of the method roughly make sense? Does it fit conceptually into the framework of the particular setting or problem you are dealing with? What makes this method especially well-suited to your problem? If you are encoding a set of steps, perhaps sequential models or decision trees are a good choice. If you need to separate two classes of outcome, perhaps a binary support vector machine would be best aligned with your needs.

With understanding come more realistic expectations. Once you ask enough questions and receive enough answers to have an intuitive understanding of how the methodology works, you will see that it is far from magical. Every human makes mistakes, and every algorithm is error prone too. For all but the simplest of problems, there will be times when things go wrong. The machine learning prediction engine will get things right on average but will reliably make mistakes. And these errors will happen most often in ways that you cannot anticipate.

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