Policies, programs and other interventions fail to achieve results for two reasons:
They are built on guesswork, instead of the best available evidence.
People are not given the opportunity to have a meaningful say in the design of policies that affect them, and they are reluctant to accept others' imposed perspectives.
Incorporate the latest evidence, data science and best practices
Demonstrate that stakeholders have been listened to and are a meaningful part of decision-making
Identify which policy levers are most effective in producing desired outcomes
Implement effective policies that minimize disruption and cost
Measure desired outcomes
Implement a cycle of continuous improvement
Step 1:Gather Evidence
Experiential evidence derived from deliberative stakeholder narrative
Evidence derived from subject matter experts
Evidence derived from existing research literature
Evidence derived from available big and/or open data sets
Step 2:Identify Key Outcome Pathways
Step 3:Manage Interventions Strategically
It was during his time with the Auditor General of Canada that Chris became interested in the policies and frameworks associated with measuring organizational performance and accountability. In 2006, Chris opened his own consulting firm specializing in program evaluation. As he gained insight into some of the challenges associated with program evaluation theory and practice, he observed what he sees as serious gaps in how interventions are designed and measured. Chris is now concerned with developing better means to understand how interventions work, how to improve design processes, and how to support evidence-informed decision-making. His vision is a world in which decision-making is systematically supported with evidence, including research, expert knowledge, stakeholder narrative, and open/big data. To realize this vision, he is centering his focus on the use of cutting-edge concepts and technologies, including those associated with data science and machine learning, to gather, analyze and test the evidence needed to bring policy-making into the modern, data-driven era.
Matt is an effective international and inter-disciplinary collaborator. He has over 10 years of experience working with teams around the world and communicating complex ideas to a wide range of audiences. He has 5 years of experience leading machine learning projects at Amazon. His technical expertise includes machine learning research and development employing a wide variety of technologies, including TensorFlow, Sklearn, Spark, Python and AWS. Other strengths include natural language processing, biosignal/time-series analysis, complex network analysis (Graph Theory), predictive stochastic models, reinforcement learning, programming (Python, Java, C#, C/C++, MATLAB), and enterprise software architecture.