Marketing Strategy: Solve the Data Analyst Talent Gap

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Marketing strategy requires a whole lot of data analysis. Maybe that’s why ten years ago, the Harvard Business Review dubbed Data Scientist as the sexiest job of the 21st century.

Yes, you read that right – we did just use ‘sexy’ and ‘data’ in the same sentence. But here’s the thing: a decade ago, as the tech world started to handle more and more big data, having a team of scientists who could make sense of the numbers was unheard of and cutting-edge.

Over the years, having a data scientist team has become the norm. You can’t be a startup in Silicon Valley without a dedicated team of scientists using data to drive decision-making for company growth.

But in a post-pandemic world, many teams are experiencing talent scarcity across the board. The Great Resignation of 2022 has left teams without marketers, engineers, and, of course, data scientists. So, the question remains: will data scientists keep their title as the sexiest job of the 21st century as we enter the next decade, or will the role become extinct due to a lack of talent to fill open positions?

The Need For Data Scientist

Data is the cornerstone of everything we do, from innovating for the future to improving decision-making for company growth. By 2025, global data creation is projected to exceed 180 zettabytes, nearly double the amount generated in 2020 (64.2 zettabytes).

With such an enormous amount of data being generated, it’s evident that data scientists will continue to be in high demand. The United States Bureau of Labor and Statistics predicts that over 11.5 million data science jobs will be created by 2026, with a projected growth rate of 36% between 2021 and 2031 – much faster than other occupations. Furthermore, the median annual wage for this role is over 100k, which is well above the average US income.

So, data scientists have job security and are making a good living, but here’s the kicker: they’re still leaving their roles in under two years. What’s the deal?

Marketing Strategy Expectations vs. Reality

When the Harvard Business Review dubbed Data Scientist the sexiest job of the 21st century, part of the allure was its novelty. Big data was new, exciting, and felt like the future, capturing the attention of big tech companies.

However, in the rush to adopt data science, AI, and machine learning into their organizations, companies neglected to identify the goals of a data scientist. As a result, if you asked ten different data scientists what they do on a daily basis, depending on the industry, company size, and their experience, you’d receive different answers.

This gap between the expectations of data scientists and the reality of their roles creates a challenge. Because data science is a relatively new field, companies may lack the data infrastructure expected by their new hires. Consequently, new data scientists often spend a significant amount of their time responding to data requests, cleaning and transforming data, and sending it back. This is a far cry from the expectations that they would use data to solve larger revenue-driving questions for their companies.

So now we are in a situation where there is a greater need for data scientists than ever before, but we lack the talent to fill and retain these roles.

AI: The Solution for The Shortage 

We support and empathize with data scientists who were launched into a field that was still finding its footing. As a result, many data scientists have left their jobs or the field altogether. However, the fact remains that we live in a world powered by data, and we need a way to make sense of it all. That’s where AI comes in.

AI and machine learning will help us solve the data analyst and scientists talent gap. In today’s world, we have access to tools that enable us to analyze big data and derive meaningful insights from it. These insights can be leveraged to make better decisions and drive revenue growth.

With the availability of such tools, teams no longer need to have dedicated analysts and scientists, enabling them to move more quickly, make data-driven decisions, and be more independent. Basically, AI empowers your team all while saving your budget. In fact, here are 10 marketing strategies you can execute on in under a minute!

New Marketing Strategy: Transform Marketers into Data Scientists 

So, while we may currently have a shortage of data scientists, AI can help fill the gaps. And that’s great news for marketers worldwide.

Instead of relying solely on data science teams, AI can provide quick answers about campaign and marketing performance. Dealtale IQ is an AI-powered data scientist that your team can use. Our latest product, similar to ChatGPT, allows you to ask questions about marketing performance and get instant and accurate insights, enabling you to make quick changes to your marketing strategy.

Want to learn more about how Dealtale IQ can become the newest data analyst on your team? Schedule a demo with us here.

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