A successful technical implementation of a Data Science solution must be followed up with a mechanism that measure its success and the appropriateness of the insights it has developed. Including whether it has real world application.

Depending on the type of solution and business case, measurement of success will range from more quantitative to qualitative forms, with it being important to consider the feeding back of results to evolve the solution itself and keeping it relevant to the needs of the business.

In this short video, we will explore the ways in which this can be done and argue why it is vital.

If you liked this video, make sure to check out the rest of ‘The Stages of your Analytics Journey’ video series. In this series we talk through each stage of the journey that an organization can undergo to effectively apply machine learning (ML) to their data to find insights and inform decisions. From the beginning steps of creating an ML model, all the way through to having a successful productionized solution that the business understands and uses repeatedly, and every stage in between. Parts 1, 2, and 3 are linked below.