Machine Learning: Are You Ready? A 7-Part Checklist Posted on : Sep 24 - 2017

Machine learning is all the rage. But while the topic is top of mind in boardrooms and the media alike, it is not always clear how machine learning is best applied. Or what it takes to implement. While the tools themselves are getting easier to use, machine learning projects are not just add data to algorithm and stir.

Ultimately, machine learning is a synergistic exercise between man and machine. Machine learning in practice requires human application of the scientific method and human communication skills. Successful organizations have the analytic infrastructure, expertise and close collaboration between analytics and business subject matter experts to translate these synergies into ROI.

Is your organization ready? Here are seven signs your organization is ready to forge forward with machine learning:

#1  Articulated a Problem That Needs Solving

Like any other technology, machine learning works best when there is a clear problem statement and outcomes defined. Routine or repetitive decision points that are high-volume, require rapid response and/or where potential actions are defined but dependent on variable inputs are good candidates for machine learning.

It works especially well for applications where applicable associations or rules might be intuited but are not easily described by logical rules, when accuracy is more important than interpretation or interpretability, or when the data is problematic for traditional analytic techniques.

Because machine learning is time and data intensive, a critical evaluation of whether existing analytic models/approaches or alternate solutions may apply is also in order. This ensures potential value is commensurate with input effort. View More