Mr Jevons is not much remembered in Australia, despite his having been the Chief Assayer at the Sydney Mint from 1854-1859. That was during his youth when a cyclical downturn in the steel market resulted in the failure of the UK family business. However, despite those humble beginnings and the personal handicap of a deeply retiring shyness, Mr Jevons went on to become the first and foremost Logician and Economist in the United Kingdom.

I mention this piece of history because the life of W.S. Jevons contains many a salutatory lesson about the lasting value of un-glamorous but persistent effort.

In his time, W.S. Jevons had no equal for the practice of Data Science. He was the inventor of numerous mathematical innovations for the statistical analysis of economic time-series and a pioneer of the then radical “Graphical Method” for the investigation of relationships through the display of visual charts.

He charted everything from steel cycles to sunspots and was the very first person to conduct any systematic employment and labor income survey – unfunded at the tender age of 21 – in the then wild Colony of Sydney.

With such a remarkable individual, concerned with the nitty gritty of real-life economic data, it is perhaps no surprise that he is justly highly regarded in his home country of the United Kingdom and all but forgotten in Australia.

Australia has never been much concerned with any topic requiring deep insight or a questioning turn of mind. It is a true desert landscape for the intellect.

Nonetheless, the young William Stanley Jevons thrived scientifically in the young Colony of Sydney because there was rich data all around! New forms of industry, the rapid growth of a colony and abundant new species to catalog.

In the 160 years since W.S. Jevons first encountered Australia very little has changed in the intellectual landscape. The place is a still a desert.

However, what rich data you can find as a Nomad in the Great Desert!

For those entering the field of Data Science and Analytics, Australia is a truly splendid playground. Not only is there rich data, but the levels of knowledge about what to do with it remain abysmally low. That spells opportunity.

A harsh judgment you think?

Perhaps, but I do not think so. I have been in the business of Data analysis of one form or another for thirty years. The levels of comprehension about the messages contained in data or the opportunity has barely budged.

Case in point… There was a recent survey of Venture Capital commitments to FinTech start-ups. It ranked twenty areas according to committed capital. The area of Data and Research focused FinTech was ranked #19 out of 20.


Do you know? Every one of the 18 categories ahead of that in capital committed had a deep dependence on the ready availability of wide, deep and rich data. This is for everything from Peer to Peer Lending to online Financial advice!

Remarkable! Insane even… perhaps a useful window of opportunity.

All the more remarkable when you consider that it is precisely within the core area of Data and Research that the twin trends of Cloud Computing and the application of Machine Learning are most applicable and transformative.

You see, Data is and always shall be deeply unglamorous. Working in the data mines is dirty and dangerous. Far better to sell the world on the sexy stuff.

Every Young Turk who enters the game wants to profoundly change the world with the splendid insight of a new model, but nobody wants to clean the data!

Does this situation worry me? Not really… I say bring it on.

The situation is splendid. One of the best moments in investment history.

You see, I have always admired W.S. Jevons for his vision and his wisdom in making two things crystal clear about the economic & management sciences:

1) You absolutely need good data

2) The sense-making is limited by available models and processing power

Very few of the contemporaries of W.S. Jevons understood this in his time. His work was roundly criticized for “polluting” the clear thinking of the Academic scribblers with “those infernal algebraic relations”.

Perhaps even fewer of the economics fraternity understand that today. They now have equations of far greater sophistication, but no developed insight to test these in any deep sense for their supposed fit to the real world.

Certainly, most folks without close contact to data analytics would readily confuse data with facts and assume it is all correct in the last detail.

In truth, these social norms to neglect the value of data are a good thing.

More power to those firms who grasp the opportunity of the moment and invest in Data and Research capabilities in quantitative finance. They will be the most profitable, the most productive and the ones with greatest global reach.

At the end of the day, the only thing that matters in financial markets is being more right than the next firm, more consistently, and at lower cost.

Very few will, because it is a long, ardous and un-glamorous path to success. The vast majority of incumbents prefer marketing and personality-driven business.

However, the future belongs to those who forsee it.

If you are a young Data Scientist never lose sight of that.

The world is now yours to own.

Curiosity finds Opportunity.