The Success Formula

The Networks scientist Albert-Laszlo Barabasi’s latest book The Formula: The Science Behind Why People Succeed or Fail is a cracker. His six laws of success summarise more than a decade of research into the science of success, in particular how the social network in which we live and operate, with its many kinds of relationships … More The Success Formula

Legislation as Code

Apparently, everything that has the word Blockchain attached to it is now news-worthy. Or at least that’s how I think this relatively small R&D collaboration between AUSTRAC and Swinburne University of Technology got a mention a couple of days ago in ZDNet, a pretty respectable technology site that has been around forever. Coding the … More Legislation as Code

The Data Quality Trap

There is a lot of work in the data management community that looks at data quality as a first-class problem, capable of being solved largely as an independent problem that can then benefit many downstream systems. There is certainly a lot going for this line of thinking. My own view, drummed into me by a former NASA scientist who I … More The Data Quality Trap

Interview Questions for a Data Science Leadership Role

It’s not always easy for a technically competent data scientist to make the transition to a data science leadership role. Here are some interview questions I use to assess whether a candidate has successfully made that transition. Can you tell us a little bit about yourself and why you applied for this role? What is … More Interview Questions for a Data Science Leadership Role

Robert Kuok’s Trading Algorithm from 1963

I am reading Robert Kuok’s autobiography and these two paragraphs from the chapter Vintage 1963 jumped out at me. “Success in futures depends on your feel for the market, your instincts and rhythm. I would talk to different brokers. Each company had bright, young English traders. One or two would be a little cunning, but, … More Robert Kuok’s Trading Algorithm from 1963

A Framework to Detect Waste and Fraud in Health Insurance

A challenge with fraud-detection problems in many cases is the lack of any meaningful collection of labelled data for supervised-learning approaches to work. Two things practitioners do to tackle the problem are statistical profiling, usually via domain-specific business rules, and statistical outlier detection, sometimes augmented with non-trivial models of what constitute “normal” behaviour. There is … More A Framework to Detect Waste and Fraud in Health Insurance