Interview with David Hand
What is Dark Data?
What inspired David to write this book?
What did he hope to accomplish in its writing?
Today, we all make decisions using data. Dark Data shows us all how to reduce the risk of making bad ones.
In this episode we focus on how you can grow in your effectiveness as scientists and leaders. With that in mind, we like to dive into this concept of Dark Data and how, where, when, why this impacts decision making in the pharmaceutical industry.
- How dark data is an issue in the area of healthcare, particularly pharmaceutical R&D, clinical trials, manufacturing, marketing, and health technology assessments?
- What’s the taxonomy of dark data?
- Where might statisticians/data scientists have their own “blind spots” related to dark data?
- From the standpoint of “effectiveness”, what is Davids advice to statisticians when it comes to this matter of Dark Data?
Reference: Dark Data
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David Hand is Emeritus Professor of Mathematics and a Senior Research Investigator at Imperial College, London, where he previously chaired the Statistics Section. He is a Fellow of the British Academy and a former President of the Royal Statistical Society. He has served on many boards and advisory committees, including the Board of the UK Statistics Authority, the European Statistical Advisory Committee, the AstraZeneca Expert Statistics Panel, the GSK Biometrics Advisory Board, and many others. For eight years he was Chief Scientific Advisor at Winton Capital Management.
He has received many awards for his research, including the Guy Medal of the Royal Statistical Society, the Box Medal from the European Network for Business and Industrial Statistics, the Credit Collections and Risk award for Contributions to the Credit Industry, and the International Research Medal of the IFCS. His 31 books include Principles of Data Mining, Artificial Intelligence Frontiers in Statistics, The Improbability Principle, Statistics – A Very Short Introduction, and The Wellbeing of Nations. His latest book, Dark Data, deals with the challenges for statistics, machine learning, and AI arising from incomplete and distorted data.
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