The volume of data that organizations handle is growing faster than their engineering capabilities. On top of being resource-consuming and expensive, hiring more engineers doesn’t always solve the problem. In fact, it might make things worse — the more people in a team, the greater the risk of misunderstandings.
Optimizing existing processes is a proven way out for organizations with complex and large data pipelines. A great example of this approach is Volvo, which reaps machine learning benefits at an enterprise scale by reducing friction in the ML value stream by automating proven processes that have been fully tested manually.
