We have allowed ourselves to become slaves to computers, and thereby to numbers and precision. In most contexts, precise numbers are spurious or misleading. Our obsession with them often leads to blinkered thought processes.

Over the last decade, Big Data has become big business. A vast amount of time and energy is channeled into a narrow obsession with a specific class of questions and concerns – ones that can be quantified using a computer, the more numbers the better.

Here’s an alternative approach to analysis, for businesses and individuals. Start by putting the computer to one side. Start with qualitative issues and ideas, and – where they matter – approximate numbers only, in order to get a feel for the key issues and their relative importance. Only then consider whether it’s possible and worthwhile trying to get more accurate numbers, which may or may not entail greater precision. And only then consider whether computers could help and whether it’s worth the investment in time and energy, and if so how much.

Climate analysis

Analysis of climate change and carbon targets provides a good example of our enslavement to computers. Over the last year, many institutions have become obsessed with measuring their carbon footprint. In some ways, this is good news. It’s great that awareness of the climate crisis has increased so much in twelve months. The desire to analyse is encouraging, and a natural starting point for target setting is to assess one’s (individual or corporate) current carbon footprint.

Unfortunately, this good intent often leads to a fixation with the wrong question. Instead of “What matters most?” the question that is addressed is “What can my computer work out?”.

And the latter question shapes the analysis that is undertaken. Using Paris terminology, Scope 1 and Scope 2 emissions are easy to define and determine – at least in comparison with Scope 3 – so most of the effort is concentrated on them. Yet for most institutions, with notable exceptions such as power generators and cement kilns, Scope 3 emissions are likely to be more significant. They are also harder to demarcate (how far upstream or downstream should the analysis extend?) and the corresponding data, being external to the institution, is harder to obtain.

Instead of focussing effort on working out approximate values for Scope 3 emissions, the common tendency is to ignore them, or at least make them low priority. Oil companies for example continue to focus primarily on Scope 1 and 2, even though 90% of oil-related carbon emissions occur downstream, where the oil is consumed. And Scope 3 is relatively easy to estimate for an oil company; most other companies make little or no attempt to measure their Scope 3.

Admittedly, there are other factors at play, one such being that companies don’t want to have targets for things they regard as outside their control. But, without being the only ones, data availability and ‘computability’ are certainly major drivers of how companies spend their time and energy.

Following the alternative approach mentioned above, a company or individual can fairly quickly establish what matters most as regards their greenhouse gas emissions. For an oil company, it’s their Scope 3. For a builder, it’s likely to be the upstream manufacture of the materials (Scope 3 again). For a financial institution, it’s the emissions of the companies to which it lends money, a category conveniently labelled as “Scope 3 Category 15” in the Paris agreement. For a management consultancy, say, it’s likely to be its business travel (“Scope 3 Category 6”); for a government office, it’s probably employee commuting (“Scope 3 Category 7”). For a boarding establishment (e.g. a public school, nursing home or prison), it’s probably the food served on site, which in Paris features … nowhere.

Once the question “What matters most?” has been addressed, the right priorities for action can be put in place. The oil company could (should) focus on targets for reducing hydrocarbon output. The British builder can decide to use British stone rather than stone imported from Asia. The consultancy can look at cutting back on business travel. The government office can bring in an enlightened approach to teleworking. The boarding school or care home can concentrate on the food it serves.

For this to work, we need amongst other things a cultural awakening that a computer is a tool for thought, not the embodiment of it. Though numbers matter, it matters more to determine a rough answer to a major question than a precise answer to a minor one. But computers dominate our thinking and computers thrive on numerical precision. In consequence, the entire industry is obsessed with Big Data and some of the biggest questions are parked if the numerical data is unavailable, unreliable, or hard to interpret.