Why all budding data scientists should be practising “the art of looking sideways”
In 2016, you can’t imagine Steve Howard was sleeping particularly well.
At the time, Howard was the head of sustainability at the global homeware giant Ikea, as well as being part of the leadership team tasked with almost doubling sales by 2020. Howard also believed that the West had probably “peaked in terms of the stuff” it could possibly own – something he was stating publicly. The need to double sales against the backdrop of this belief in “peak stuff” seemed to be in conflict – with the only solution appearing to be a massive innovation in Ikea’s business model.
Data certainly seems to support Peak Stuff Theory
Indeed, 2017 Barclaycard data subsequently highlighted a 1% year-on-year drop in department store spending, an 11% year-on-year decrease in vehicle sales and a year-on-year 2.5% decrease in household appliance sales. This wasn’t due to a market-wide economic downturn: in contrast, spending in important aspects of the service sector such as pubs and theatres & cinemas increased by 20% and 13% respectively over the same period.
Western spending habits were and are changing. Most businesses are aware of these trends – they don’t need talented Data Scientists to spot them. What is much more complex to identify are the reasons behind the trends and how these are impacting specific sectors and businesses.
This is where a great Data Scientist becomes invaluable – explaining the why behind the what. It’s the art of looking sideways.
The art of looking sideways
The art of looking sideways is about data scientists becoming creative. It’s about fusing an interest in human motivation with the brilliance of mathematics and computer science to reveal the stories behind the data. As soon as data scientists begin to include human motivations in their data models, the models become infinitely more compelling.
In the age of data, such ‘humanistic’ data science – as it’s becoming known – is becoming an important differentiator for leading corporations. But it’s a rare skill that hinges on data scientists practised in the art of looking sideways.
Masters of the art see beyond the obvious. They see patterns. Then they draw on their accumulated knowledge of context and, of course, human motivations. In doing so, they’re able to investigate the unlikely and uncover the implausible. They then share their findings with stakeholders in a simple way that makes sense and can be actioned.
It’s a skill great data scientists possess… and a skill any budding data scientist would do well to nurture.
Which brings us back to the Ikea conundrum.
Explaining the Ikea conundrum
The data suggests spending on goods is falling. Meanwhile, spending on services continues to climb. Why?
Have we indeed reached “peak ‘stuff’”?
Maybe – but it’s unlikely. Commentators such as Vance Packard argued that we in fact reached peak stuff as early as 1960, following an increase in US GNP of more than 400% in around 20 years. Since then, spending on material goods has continued to climb. So what else might be going on?
The trend forecaster James Wallman has been monitoring the pattern closely. According to Wallman, at least part of the pattern can be explained by the human desire for status.
Status was once established through expensive cars, suits and designer labels. In the age of social media, status can instead be asserted through shouting about experiences.
Cool bars, trips to the theatre, music festivals; none are goods – and yet all fulfil our inherent desires to assert our self-identity and build status in our communities.
The power of the art
The art of looking sideways is not the exclusive reserve of data scientists. It’s evident in the accomplishments of all great innovators – and none more so than Henry Ford.
Ford did more than anyone to make the automobile synonymous with modern life. And he did so without guidance from focus groups or customer feedback. “If I’d asked people what they wanted,” Ford is credited with saying, “they’d have said a faster horse.”
While the origin of the statement remains controversial, it’s clear that Ford spent a lot of time thinking through human needs and motivations. Ford employed the art of looking sideways – and became wildly successful as a result. If he had asked people this question and taken their answers quite literally, he would have spent years trying to breed faster horses. Instead, by employing the art of looking sideways, he identified the underlying need – faster travel on land. He was then able to identify a way of fulfilling this need – the affordable motorcar.
Revealing the true stories behind data patterns can be a game-changer. Concluding that changing consumer spending habits are the result of market saturation is little more than a brick wall. Work out what’s really going on, though, and the pattern becomes an opportunity.
Instead of closing clothing outlets, you can instead launch Thread – where free personal stylists subtly relieve status anxiety. Instead of attempting to sell razors, your proposition becomes the Dollar Shave Club – where people can rebel against corporations as part of an irreverent community.
In a world where off-the-shelf algorithms are becoming increasingly commonplace, tomorrow’s data scientists should already be practising the art of looking sideways.
Their unique insights will prove to be invaluable to society as a whole.