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The data scientist, examined – addressing the talent gap in data analytics

By / / In Insight /
It is now generally agreed that the process of analysing data is key to successful marketing. Collecting data from happy and consenting customers is fine, but putting that data to good use – identifying trends, optimising campaigns and examining effectiveness in detail – is still a skill that many marketing departments lack. How skilled is your data scientist? ...What’s that? You don’t have one, just a regular marketer trying to keep all the balls in the air (and dropping a few)? Find out why the science of data is so important – as is the need to fill that specific role.
data management, data scientist

A world full of data is a world full of opportunity for marketers, but we risk missing those opportunities if our organisation doesn’t have the right skill set. How can we ensure we’re equipped to take advantage?

Like many other industries, data-driven marketing is facing a significant recruitment challenge. We are experiencing an explosion in the amount of available information, but our ability to process that data is struggling to keep pace; the required technologies are out there, but many of us lack the expertise to actually implement them. Here we look at the talent gap that currently exists in data analytics, and what we can do to address it.

The importance of data analyticsdata scientist

Nowadays, almost every aspect of our daily lives involves the creation of data. From our shopping habits to our social media posts, any and all online activity is logged; studies estimate that by 2020, there will be a huge 44 trillion gigabytes out there. And, with new technologies promising to make more and more of that data actually usable (think object recognition and sentiment analysis in photos and videos, for example), that’s a whole lot for us to process.

To us as marketers, that information is invaluable. Through a combination of descriptive, predictive and prescriptive analytics, we can achieve a level of personalisation never before possible; identify trends, optimise our campaigns and examine their effectiveness in detail. No wonder a recent survey found that 72% of marketers now consider data analysis to be more important than social media skills.

The data scientist skills shortage

There is now widespread acceptance that data analytics is key to successful marketing. But when we come to actually implementing these strategies, myriad questions arise: How do we source our data? Clean and organise it? Mine it for patterns? What about writing algorithms? Or producing visualisations to demonstrate findings?

These — to quote actor Liam Neeson in the hit film ‘Taken’ — are a very particular set of skills, and it shouldn’t come as a surprise that many experienced marketers (who may well be extremely valuable in other areas) simply don’t have them. In fact, finding anyone with the desired knowledge is proving difficult, with 78% of businesses saying they have experienced challenges filling open data analytics positions over the last 12 scientist

While there is a skills surplus in other areas, such as social media, digital marketing and public relations, demand is clearly outstripping supply when it comes to data scientists. And what’s more, the nature of the role is constantly changing as new techniques become available. Those AI systems helping us to decipher social media posts are also shaping what is required of humans; there is now less of a need to ‘point algorithms in the right direction’, for example, but the quality of the data we provide them with is critical.

The impact

That data quality really is the key to successful analysis – your models are only as good as where they came from. If any of your information is outdated, inaccurate, duplicated or inconsistent, it will not drive optimal marketing decisions; instead, this dirty data will produce flawed insights and reduce lead engagement. That’s why data scientists report spending most of their time cleaning and organising data and why it’s essential you ensure you have the right expertise for the task.

Indeed, that goes for all aspects of the role. While a poor visualisation may not fundamentally skew your original findings, for example, it can certainly mislead or confuse decision makers, with similar effects. Because of the talent gap that exists, it could be tempting to rush in to data analysis without the right personnel, but be aware that the end result will reflect this. As Gartner found in a 2015 survey, ‘Companies surveyed with the most experienced analytics talent report significantly better overall company performance.’

Finally, another related impact of the shortage is that data scientists have become extremely sought-after individuals, helping to drive up salaries. Great news for them, of course, but not so good for small and medium-sized enterprises that cannot compete with larger firms when it comes to attracting the top talent. Not only are those smaller businesses the foundation of a successful economy, but they also benefit from being agile enough to act quickly on marketing insights, meaning there is a lot to be gained if we can make data-driven marketing more accessible to them.

What can we do?

So how do we address the data analytics talent gap? We can start with education and the provision of more good quality university courses. Fewer than one-third of US News & World Report’s Top 100 Global Universities offer degrees in data science, while just 20% of four-year institutions in the US offer at least one analytics programme – poor numbers for such an in-demand position. Of those programmes that do exist, employers report that many are too disconnected from the business world, producing graduates lacking in relevant skills.

It is clear that as an industry, we need to develop more links with higher education, to help develop courses that focus on specific marketing applications. And, as individual businesses, we should look to partner with institutions through mentoring and internship programmes. That way we can create valuable links with students, encouraging them to choose a role in marketing over other industries, and creating talent pipelines for our own companies.

But what about in the meantime? Well, when you do recruit, be knowledgeable about your skill requirements; unless you’re offering a top-end pay packet, you’re unlikely to find someone with experience in all areas. Instead, pinpoint what your priorities are and look for them – it may help to use a specialised recruiter. Invest in training and development programmes for your existing workforce as well, which will both improve morale and help ensure all that vital knowledge isn’t concentrated in a few individuals. If none of the above options are feasible, using a trusted service partner could be another way to go.

In the longer term, we have to look at the culture in our organisations and the industry as a whole. It’s one thing developing the necessary education and training, but we must make certain that a data analytics career path remains attractive, even if the financial benefits don’t remain at the same level as today. A few years back, Harvard Business Review famously declared the data scientist ‘The Sexiest Job of the 21st Century’, but is that a statement which would resonate with most in the field, or those considering it as a career option? Too often, any IT-related role is seen as the modern-day equivalent of shovelling coal into a furnace; we have to ensure that data analytics is a valued, central part of every marketing team, going forward. As the aforementioned Gartner survey stressed, make it part of your DNA – after all, it’s crucial to the future of your business.

Have an opinion on this article? Please join in the discussion: the GMA is a community of data driven marketers and YOUR opinion counts.

Tim Scargill
Author: Tim Scargill
Former IBM consultant and electronic engineering graduate |

Tim Scargill writes about all things technology-related. He is particularly interested in how emerging technologies will affect enterprise in the future. After completing a masters degree in Electronic Engineering at the University of York, he moved on to become an IT consultant at IBM UK. Gaining knowledge and experience of big data and its business applications, he specialised in the analysis and processing of sensitive data. Specific interests include big data analytics and strategy, natural language processing and machine learning. Find him on Twitter:

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