“Time is money.” As a child growing up in the 90s, I lived through an era where this statement was the conventional wisdom. It inferred that just like money is a limited resource and should be valued, so too, time is a limited resource and should be valued.
However, Jim Rohn, an author and motivational speaker, is quoted as having said that, “Time is more valuable than money. You can get more money, but you cannot get more time.” So, it seems that there are levels of limitation when discussing these limited resources. Does that then mean that time is more valuable than money simply because it is more limited?
Before we discuss data and oil, I would like to suggest a much more profound reason for time’s dominance of money that takes us away from supply and demand theory, where price or value is defined by scarcity. The one major difference for me between time and money is the potential that each resource intrinsically has. Money can buy you lots of ‘things’ but that’s where it ends. Time, on the other hand, is overwhelmingly pregnant with opportunity. It can be ‘spent’ on almost anything – including interpersonal relationships, self-development, healthy habits, and myriads of others (even on ‘making’ more money). So, the power in ‘time’ is less about its limits than about its potential.
And now, we come to another anecdote that has become popular in recent times – “Data is the new oil.” A cover of the Economist magazine from 2017 comes to mind which shows an image of many oil platforms in the sea. Each platform is branded with a different technology logo. The metaphor is clear – oil used to be the centre of global commerce and now data and technology are (I am smiling at the irony of this statement because as I write this, the price of oil has dropped dramatically because of the COVID19 pandemic and low demand. This is supply and demand theory at its most poignant).
But again, just like with time and money, I think the comparison of oil to data is misguided. Data is infinitely more valuable than oil. And once again, I think the reason for that is because it, too, is pregnant with potential.
In my daily work at Emerge, where we use machine learning to find valuable patterns in data for our clients, I am so often amazed at what comes out. The most unexpected patterns can and often do emerge. For example, when modelling credit default, most credit providers insist on using the applicant’s ‘debt-to-income ratio’ as an input to the credit scorecard which determines whether the applicant will default on the loan or not. And very seldom do we find that variable to be predictive in the credit scorecards that we build. The ‘day of the week that the applicant applies’ for the loan, however, is almost always predictive. We don’t know why that is. We can retrospectively guess at a reason like, “Monday applicants are impulsive” or “Friday applicants are focused on leisure”. But the truth is that we don’t really know – the patterns in the data are often too complex for our human brains to comprehend. If we could understand these patterns, we wouldn’t need help from machine learning in the first place.
Who would have thought that the ‘day of the week that the applicant applies’ would be important? There was no way to know in advance. So, we often approach the data collection and collation processes without any prejudice. We use all the data available and allow the machine to find its own patterns. Who knows what glorious revelations we will find?
So, in conclusion, my message is that all data is valuable. Collect it. Store it. Analyse it. Value it. Because Data is not the new oil – it is much more valuable.
This article is one of a suite of articles created through a collaboration between Crowe and Emerge. The series discusses the significant potential that insights extracted from data can have, as well as ideas to get you started on your innovation journey and key considerations to help achieve maximum impact. Contact us for more information.
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