Chris Evett is a data scientist with 14 years experience of producing innovative processes for a wide range of sectors most notably futures analysis, strategy, defence policy and research and development. In April 2014, Chris formed ‘Simplexity Analysis’ – a company specialising in predictive analytics. Through this company he works on projects that combine data science, programming and analysis to help understand and model complex problems. Current projects include modelling the future strategic direction of Africa (out to 2045) and a data driven assessment of the future of healthcare.
Before founding Simplexity Analysis, Chris worked for the UK government as a lead editor for the ‘Global Strategic Trends Programme’ for the UK Ministry of Defence. In this role he analysed a wide range of future trends on a diverse set of issues from societal issues (such as how values and beliefs relate to terrorism, scenario development to predict mass atrocities and trends in defence spending) to longer term strategic issues on climate change and the rising geopolitical influence of China. He developed methodologies for better understanding future trends; specialising in advanced mapping and data-driven policy recommendations and led project collaborations both across the UK government (National security secretariat, Joint Intelligence Committee, Government Office of Science, Foreign and Commonwealth Office and Department for International Development) and internationally. In addition, he drafted contributions for the UK Strategic Defence and Security Review and the National Security Strategy, producing evidence-based policy recommendations and strategic options for policy and strategy makers.
Prior to working for the UK Ministry of Defence, Chris was a Knowledge agent for the Defence Science and Technology Laboratory (DSTL) where he produced knowledge-based systems and solutions for a diverse range of consultancy projects, including the design of a system for assessing and understanding national research and development systems using open data.
Chris is passionate about using data science to improve both the accountability and rigor of decision making, especially for understanding the future. He has a broad experience of analysis techniques which include; scenario development, knowledge mapping, red-teaming, trend and driver analysis (STEMPLE), concept testing and developmental editing