It was five years ago, exactly, in October 2012, when Harvard Business Review (HBR) declared “data scientist” to be the sexiest job of the century. HBR told the stories of Jonathan Goldman and D.J. Patil from LinkedIn, and Jeff Hammerbacher from Facebook, among others. They were the ones who coined the original term “data scientist” back in 2008 while they were leading data and analytics at their respective companies. The appearance of data scientists on the business scene reflects the fact that enterprises are now dealing with information that comes in varieties and volumes never seen before – what we usually call “Big Data.”
Also in 2012, the research company Gartner suggested that there will be 4.4 million “big data jobs” in the coming years, and that only a third of them will be successfully filled. That projection should not have been surprising. Everything is moving toward data at the speed of light: big data, mobile data, performance data, content data, product data, and even data about how we measure our data.
Today, the situation remains the same. The top spot in Glassdoor’s 2017 rankings of the best jobs has gone to – guess who? – the data scientist, its second year at number one. Glassdoor determines the rankings via three key factors – number of job openings, earning potential, and career opportunities rating. With businesses placing an ever-growing importance on data, this is again unlikely to be a surprise to many of us.
But what is a data scientist? The straight answer would be that it is a high-ranking professional with the training and curiosity to make discoveries in the world of big data. Today, thousands of data scientists work at both startups and large enterprises. What data scientists do is make discoveries while analyzing data. It’s their preferred method of navigating the world around them. They can bring structure to large quantities of data and make analysis possible. They identify data sources, combine them with other potentially incomplete data sources, and clean the resulting set, helping decision-makers shift from ad hoc analysis to an ongoing conversation with data. Data scientists are doers, not strategists.
Now, can you spot the link between data science and marketing? Yes, that’s immediately visible. It’s no secret that today’s marketers are faced with huge amounts of customer data in volumes that can potentially be quite intimidating for people – quite frankly, many of us – who are not used to handling data and analytics. Thus, we now have a greater opportunity to get to know our customers infinitely better than ever before. From social media, SEO, subscriptions, and browsing habits, to offline in-store behavior and loyalty programs, there has never been a better time for marketers to develop cross-platform customer insights and build a genuine “single customer view.” Even in B2B today, data is king: Most of the customer journey is digital, and B2B marketers gain an enormous advantage when they create personas and map content to the digital buyer journey. Also, with the explosion of software for marketing analytics, marketers can create valuable data analysis from social media channels and make strategic decisions from those insights. And yet, despite having the opportunity and technically the data available, marketers are still failing to develop an accurate profile of their customers. Something continues to hold them back.
The problem is that many in the marketing community have never felt particularly comfortable with data analysis. Marketers rarely get into marketing to become data scientists; they expect to plan campaigns, be creative, and help bring brands to life. In addition, they want their marketing tools and technologies to make the tasks of unifying, cleansing, and matching data more automated. Instead of investing time in such technologies, however, 27 percent of marketers are choosing to hand data analysis over to the IT department, while 6 percent are still forking out for external data analysis services.
Nevertheless, with the rise of customer data profiling and web analytics, even more traditional marketers have started to change their minds. In fact, based on a recent report from BlueVenn, the marketing community considers data analysis to be the most important skill a person could learn in the next years. The report says that 72 percent of marketers consider data analysis vital, above skills like social media (65 percent), web development (31 percent), design (23 percent), or SEO (13 percent). Marketers have recognized that without skills in data analysis, the effectiveness of content marketing and social media is limited. And yet in 2016 alone, there have been 1,780 articles written on the “data skills gap,” resulting in more than a quarter (27 percent) of marketers believing that their teams lack the skills needed to succeed in the analytics age.
Now, this has two main implications, in my opinion:
- Any marketing professional can give themselves a competitive advantage by strengthening their data analysis capabilities. From ensuring the success of your content marketing programs and gaining C-level buy-in, to securing customer success and killing unsuccessful pilot programs, data skills will be a huge asset.
- The second implication is for brands and agencies: The ability to manage successful campaigns will require strong data analysis competencies, with human and technological capabilities. As competition becomes more sophisticated, the ability to keep up will be directly connected to data analysis capabilities.
As far as the first point, this is what Jason Miller, Global Content Marketing Leader for LinkedIn, defines as the rise of hybrid marketers. Miller writes: “There used to be two ways to survive and thrive in marketing. You could be a specialist marketer, picking an area like brand and communications, events or email, and building up your specific skills to become an expert in your field. Alternatively, you could be a general marketer, specializing in strategy, brilliant at seeing the overall marketing picture and how all those different activities fitted into the plan for a business. The career path in marketing usually meant moving from the specialist to the strategic. As you became more senior and demonstrated your grasp of fundamental marketing principles, you could rely on your more specialist colleagues and their agencies to handle all the specific executional stuff.”
The hybrid marketer is then a “new marketer,” with communication and creative skills, but at the same time is someone who sees it as part of his role to learn any new, emerging skill that might have relevance to achieving their objectives – data analysis included. He is a marketer who doesn’t leave executional knowledge to others but is eager to continually learn. He’s a marketer who doesn’t stick to one specialization but seeks to acquire a working knowledge of any domain that might be relevant: SEO, data science, inbound marketing, design.
Time will tell what the evolution of this role will be. What we know for sure is that opening our respective knowledge to new domains, building a data plan, learning how to interpret charts, understanding how the psychology of design will impact our content marketing techniques, and owning company’s SEO strategy will facilitate creating a culture that values data. In this way, we get to drive data deeper into the DNA of our business by ensuring that key decisions are data-led and likely to result in stronger performance.