It is no secret that machine learning is all the rage. And no wonder. It offers the potential to address areas that, so far anyway, have eluded traditional technologies.
At the same time, machine learning algorithms are no better than the data used to train and feed them. Indeed, the quality standards for data used by machine learning algorithms far exceeds most other quality requirements. See “If Your Data is Bad, Your Machine Learning Tools are Useless,” (Redman, hbr.org, April 1, 2018). All this means some fairly steep organizational changes will be required as well.
These concerns motivated this “Data Quality and Machine Learning Readiness Test.” It is designed to help you understand the most important issues, baseline where you are, and sort out which issues you must address in the short term. The test also aims to help you get started on a program that will stand you in good stead for years to come. It may take ten years and hundreds of millions of dollars to become world-class. So, over time, you should ask yourself what is the desired end state for your company. Remember, the goal is to obtain business benefit, not score well on this test.