Data science used to be shrouded in mystery, a scientific process usually carried out by the IT department or esoteric tech-based agencies. But in recent years the field has matured to a remarkable degree, and progress is being made on multiple fronts, from ModelOps and reproducibility to ethics and accountability.
Not that long ago, data scientists – or data analysts as they were known – were seen as number-crunching geeks sitting in a dark office corner. Perhaps you worked next to them in the marketing team but weren’t confident or interested enough to engage with them.
Fast forward to the present day and all of that has changed. Data is the new oil and data scientists are the people extracting its value. Successive global developments, from the digitisation of everything to the daily statistics we’ve become used to during the pandemic, have dragged data science into mainstream conversation.
Not only are people from all walks of life talking about data science, many are actively using it. Visualisation and analysis tools have democratised data science in the same way that software and apps have simplified many other aspects of professional life, from web design to accounting.
That means some crucial new implications for marketers. Organisations are now much more likely to hunt for people with specialist skills, not just those with generalist marketing attributes and experience.
In its latest release of job vacancy data, LinkedIn notes a 52% surge in 2020 of technical marketing roles such as ‘growth hacker’ and ‘growth specialist’; what it calls “low-cost and innovative alternatives to traditional marketing”. It’s no exaggeration to say that marketers who are unwilling or unable to invest time in adding data skills as a string to their bow could be left behind by better-qualified candidates.
And yet it’s vital to recognise that a snazzy visualisation app doesn’t make you Larry Page, in the same way that Squarespace and GoDaddy aren’t the overnight making of website Picassos. There will always be a need for data science experts.
First, developments in AI and machine learning are almost too rapid for humans to keep up. Consider quantum computing. The race to develop the quickest technology is getting serious. Hot on the heels of Google’s announcement that it can complete calculations far quicker than supercomputers comes the claim from Chinese scientists that their machines can do the sums 100 trillion times faster.
Data scientists are key to the development and deployment of these technologies that will cross industries and touch all aspects of daily life. They are the strongest links in the chain between R&D and actual use in a range of situations so wide we probably haven’t yet discovered them all. Data scientists are working hand in glove with the new breed of ModelOps professionals to close the so-called ‘reproducibility gap’ and turn conceptual code into consumable products and services.
Then consider the sheer volume of data that consumer interactions create. More people are shopping online than ever before across a spectrum of connected devices. Every time they browse, click or buy they are leaving a data trail that equates to mind-boggling amounts of information. This can be a minefield for marketers to pick their way through; data scientists have the acuity to transform it into a field of gold.
Take advertising, where the industry has long had a penchant for ‘black box’ technology that attempts to automate attribution across channels for brand campaigns. Without a ‘white box’ approach, where data scientists take a step back and look at the bigger picture to analyse what’s really happening, true sales attribution is nigh-on impossible.
It’s not all about the numbers, of course. As data unlocks power for those who know how to use (or abuse) it, the debate about ethics will rage on. How much control should consumers have over their data compared to the brands they buy from? How do we ensure data doesn’t contribute to discrimination, for example in recruitment or university admissions? Led by data experts, organisations are putting more thought into the potential pitfalls to the public and their own reputations of poorly developed AI systems.
These are just some of the areas where data scientists can lead the way with their technical ability and insight, working alongside other professionals for the successful application of new technologies. Not least in marketing, where data science offers brands the best chance yet to find and communicate with more consumers who would happily buy their wares.
Data science has finally matured. Visualisation and analysis tools are seeing to that, giving marketers – and other professions – permission to explore it. But data science experts are still the people to make all that information whistle and hum, ultimately boosting the bottom line.
Bill Portlock is managing director of Metrix Data Science