This project aims to build a Bayesian dashboard for data engagement, analysis and insight. More and more businesses use visual analytics dashboards to represent their data for better decisions. However, typical analytics dashboards don’t actually improve decisions much because their data doesn’t enhance the cognitive firepower of users. Before leaders can analyse data, they need to understand the function of the data in their own reasoning processes. I propose a new sort of dashboard—a Bayesian dashboard.
Bayesian rationality dictates that leaders need to generate likely hypotheses about their business before they even consider visualising data . If leaders specify a robust set of likely hypotheses and set prior probabilities, then a Bayesian dashboard would get populated with data visualisations in the service of these hypotheses. A Bayesian dashboard ought to represent data as evidence to activate human reasoning. Visualised data becomes evidence for or against multiple hypotheses. Users interact with the dashboard by making bets on which hypothesis they think is most likely to be true, similar to making bets on a horse race. Dashboard predictions could be linked between users.
While there are Bayesian dashboards already in existence, they are designed to feed Bayesian networks (i.e. machine learning algorithms) or to create Bayesian decision support systems, such as personalised drug treatments . I believe this project would be the first attempt to make a Bayesian dashboard for human reasoning purposes. I propose that such a dashboard would generate greater user engagement with data, data comprehension, analysis and insight. There would be a feed-forward loop of user data that leaders could use to generate new hypotheses for further consideration in the dashboard.
A Bayesian dashboard will promote and assist data analysis for increased situational awareness and new insights leading to better resilience.
References:  Montibeller, G., & Winterfeldt, D. (2015). Cognitive and Motivational Biases in Decision and Risk Analysis. Risk Analysis, 35(7), 1230-1251. doi:10.1111/risa.12360  Reddy, Vikas, Farr, Anna Charisse, Wu, Paul P., Mengersen, Kerrie, & Yarlagadda, Prasad K.D.V. (2014) An intuitive dashboard for Bayesian network inference. In Journal of Physics: Conference Series, Institute of Physics Publishing Ltd., 012023. Retrieved from: http://eprints.qut.edu.au/63346/  Mould, D., D'Haens, G. and Upton, R. (2016), Clinical Decision Support Tools: The Evolution of a Revolution. Clinical Pharmacology & Therapeutics. doi: 10.1002/cpt.334