And why some people consider them the rock stars of the 21st century?
Data analysts in marketing contexts are given the job of sifting and sorting through reams data to uncover valuable insight into audiences. But the majority of analysts are not mathematicians.
As is typical in the agency world, many employees were likely Arts majors in university, with a few accounting and marketing courses under our belts – albeit with less of a STEM-focused background.
A ‘data scientist’, however, is likely to have some technology background, an understanding of statistics, and should, according to some, be able to describe a few basic algorithms at a dinner party.
The reason for this is “big data”, which for brands could mean competitive advantage. Here are important Internet marketing trends in 2017, according to Dave Chaffey at Smart Insights. As you can see, in our line of work, the omnipotence of ‘big data’ is inescapable.
Many brands and agencies would love to have a data scientist in their ranks, but what is a data scientist and what distinguishes them from a data analyst?
According to Marketing Land, unstructured, (or semi-structured) “big data” requires a programmer to analyze, understand and cleanse – leveraging statistics and programming to discern what attributes are valuable – before it can be plugged into a tool like Excel for an analyst to examine.
Equally, in order to extract insights and make impactful business decisions, one must have a keen business sense, and know what to look for. This is where Data Scientists come in. Data Science requires a background in statistics, programming, and computer science.
So how do we sit the freaks with the geeks? After all, traditional marketing or advertising agencies are replete with liberal arts, communications and English majors.
In an ideal scenario, both agencies and brands’ marketing departments would have data scientists on staff. Agencies to better serve their clients, and marketing departments to collaborate with external experts and draw connections between client and agency data.
But whether or not traditional agencies are able to attract this level of talent is another story.
Data science isn’t really something you learn in school – most employed in data science have backgrounds in machine learning, statistics and computer science, and have (seemingly) fallen into the role. In fact, with the emergence of widely available online learning resources (think edX, Coursera and Udacity) one could theoretically learn everything they need to become a data scientist online, at a fraction of the cost of a typical education.
As traditional marketing and advertising shifts towards digital, agencies would be wise to hire some technical people with STEM backgrounds, and have staff with some basic understanding of computer science, coding, databases and analytics, who can use data to make better decisions.
Further, encouraging existing employees to train on tools enabled by data science (like Brandwatch for social media monitoring, and Google Trends, Google Analytics, and ComScore for audience analysis and campaign planning) would be useful.
A prevalence of these skills in the workforce has led to the emergence of new hybrid job types, like “data journalist”, requiring an amalgamation of skillsets from past and present. Another hot job of the future, the “data journalist” (according to an Economist job listing) is a blend of journalist and coder who will write stories based on numerical research, using statistics and coding skills to work with raw sources of data, and design skills to create interactive graphics.
In an increasingly digital world, and as lines blur between technology and media, it’s clear that as editors, journalists, content marketers and arts majors who work for agencies, that if we are to keep up in marketing, and deliver ground-breaking experiences for our clients, that we can all benefit from a foray into uncharted waters: Namely, the oft-geeky-seeming (but apparently rockstar-worthy) worlds of computer science, technology and programming.