This is a companion discussion topic for the original entry at:
At McKinsey, we help many large public sector and private sector entities transform themselves into data-driven organizations. We have the privilege to help clients predict everything from hospital readmission likelihood to detecting mule accounts used by money launderers. But, often, the difference between step-change improvements in client performance and interesting analytical insights lies in ensuring meaningful change in everyday practices by “the front-line”. We have found that R is an effective tool for making change stick. It lets us unleash a design-thinking approach to build machine learning applications that are co-created with users. It supports us in building visualizations and stories that unravel the mysteries of black-box techniques. It also helps create technologies that turn interaction with advanced analytics output from an adversarial experience to the best part of a user’s day. During this session Aaron Horowitz, Analytics Expert, will walk through some lessons learned and offer tips in making adoption of analytics built in R easier for any organization.
Aaron Horowitz - Analytics Team Leader
I help clients deliver against their mission using digital and advanced analytics. For the past 5 years, I have focused on the public sector, leading technical teams and teaching/enabling client data scientists across criminal justice, public finance, social services and more. I build, and lead teams that build, machine learning models, deliver advanced digital tools and support front-line cultural change to enable more efficient and effective government services. I also lead internal efforts to enhance the advanced analytics technology stack with a suite of internal packages, and train consultants to become “analytics translators” — business professionals that support the data science process.