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Anything But Average

Perhaps this seems inevitable when his observations seem supernatural, but he knew that his brilliant explanations had to be based on data; Sir Arthur Conan Doyle, the writer of Sherlock Holmes, ensured that Holmes knew that to start making conclusions before you had the data would be a monumental blunder.

Confucius is a hero of data because he sought the principles and values to ensure we started asking the right questions; so that we could choose the right things to measure.

Florence Nightingale not only collected data but applied statistical methods to help understand its true meaning and developed ways to present it in a graphical form that highlighted its meaning.

The use of BS in the title represents their desperate concern about the way data is being collected and used, supposedly to reach effective solutions but, too often, to represent confirmational bias.

Welcome to the Austraffic series "Heroes of Data" ...

We are relaunching the Austraffic ITE-ANZ World Wide Learning Opportunities Award to send young Traffic and Transport professionals overseas. COVID paused the program for two years but applications are now open.

The systems are getting better, but the nature of the surveys and what they are really measuring should still be foremost in our minds. A detailed understanding of what is happening on the ground is essential for finding local improvements. Some more detailed surveys and good old-fashioned observations are integral components to add to automatically collected data.

We are racing ahead with high powered computer technology without the right foundation. We can make bigger mistakes at a faster rate: Garbage in – Garbage out. The impact of COVID has been huge, but it is also being used as an excuse to cover some long term, systemic problems.

The mandatory Census of Population and Housing has been defined as the most significant statistical event in Australia. But it is not sacrosanct. What is essential is good data.

If the most advanced assessment processes are based on poor data and/or wrong assumptions the conclusions can be enormously misleading.

Having a huge amount of data can be quite different from having enough of the right data. How often do we test if a survey has gender or other biases?

With the boom in available data and the computing power to analysis it, we need to introduce data collection, management and even governance more firmly into the education system. However, we need to go further; we need an education system that is not based on the very problem we have with data in defining our transport needs: the use and abuse of averages!