Many engineering teams are focused on redundant input metrics as an indication of the success of “quality assurance” on their technology implementation projects. Are we so busy measuring the number of test cases, pass rates, code coverage and bugs discovered that we have forgotten what real quality means in the eyes of our users?
Unfeatured
This is the default setting. This displays articles in the normal listing(s), and not in Featured areas.
The exponential growth of digital products and services means more data being gathered than ever before: customers, usage, patterns, and problems. With this growth, we have needed to store and manage it all, but more importantly, to figure out how to learn and benefit from it. This has driven the increasing popularity and desire to incorporate machine learning (ML), artificial intelligence (AI) and data science into most new generation systems and products, these big data projects are often ambitious, and often failures.