Many organizations are immature when it comes to data use. The answer lies in delivering a greater level of insight from data, straight to the point of need. Enter: machine learning. In this webinar, William will look at categories of organizational response to the challenge across strategy, architecture, modeling, processes, and ethics. Machine learning maturity levels tend to move in harmony across these categories. As a general principle of maturity models, you can’t skip levels in any category, nor can you advance in one category well beyond the others. Vis-à-vis ML, attaining and retaining momentum up the model is paramount for success. You will ascend the model through concerted efforts delivering business wins utilizing progressive elements of the model, and thereby increasing your machine learning maturity. The model will evolve. No plateaus are comfortable for long. With ML maturity markers, sequencing, and tactics, this webinar provides a plan for how to build analytic Data Architecture maturity in your organization.