Why We Will Always Need Data Scientists

Low- and no-code tools put the power of data analytics and AI in the hands of non-data experts. They help organizations lower costs, speed development and collaborate faster and more extensively across the enterprise. They are an invaluable productivity tool for both data novices and experts alike. After all, expert data scientists also use low- and no-code tools to automate tasks where writing code from scratch would be an unnecessary effort. This gives them more time to do more work and explore other projects. 

The message is still the same. Low- and no-code tools remain productivity tools, not replacements. Data science experts are still necessary to keep the world of tech going.

Related readingCan Low-Code Tools End the Developer Shortage?

The Role Data Scientists Play

To illustrate the role these low- and no-code tools occupy within organizational data science and AI initiatives, let’s compare them to a tool almost everyone is familiar with: Word processors. Low- and no-code tools are akin to word processors’ spell check, autocorrect and formatting features. They make the writing process smoother and more organized, but, should you wish, you can write without them. 

In theory, I could have written this article on a typewriter, but it would have taken me much longer and involved more work. The value of word processors is obvious: in the long run, they enhance peoples’ work and amplify their output. Low- and no-code data science tools present the same value proposition. They shorten time frames from months to weeks, increase collaboration and reduce errors. They are well worth the investment.

Just as writing is about more than spelling words correctly and ensuring proper spacing, data science is about much more than pointing, clicking and organizing. It is about critical thinking, approaching problems, analyzing use cases and beyond. It requires human cognition. Low- and no-code tools can automate a data science workflow’s most repetitive steps, but there will always be unique problems and functions that only an expert data scientist can address by creating and revising code. 

3 Timeless Data Science Skills

In the face of the continued adoption of low- and no-code tools, it may seem natural to wonder: Should I go into data science? Should I bother specializing in it when software can automate some of it? The answer is unequivocally yes. 

Data scientists are still vital for organizations looking to maximize the value they get from their data. Data scientists are also the first line of defense when it comes time to oversee, monitor and troubleshoot projects and models. Their ability to dive into code to creatively solve problems and turn business problems into data problems differentiates them from users confined to low- and no-code capabilities. 

Here are the three essential and specific skills human data scientists bring to the table. 

Turning business problems into data problems requires data scientists to have the analytical skills needed to understand the business process behind the objective and translate that process into a problem statement that data science can solve. 

The second essential skill data scientists need is determining the scope of the data needed to solve a given problem. Today, the ability to understand data and how it can solve problems is more demanding than ever because of the increasing availability of new data sources with increased digitalization. 

The third and most critical skill data scientists need is the ability to select modeling techniques suitable for the problem and the data. Again, this skill is independent of the mode of executing the project, whether it be using full code or low- and no-code tools. 

Let me describe an example of these skills in action from my own experience. A few years ago, we were tasked by a major North American telecommunications firm to develop a churn model to predict its clients’ churn behavior. We decided to develop a model that would classify the customers most likely to churn. 

The company had a gigantic data warehouse with hundreds of tables and views distributed over several databases. Two of our data scientists spent several weeks in discussions with the business managers and the data warehouse administrators to understand the business processes and the data related to customer retention. Before writing a single line of code or using any modeling software, we spent a considerable amount of time simply mapping the business processes and the corresponding data that reflected these processes. The need to do this well, using the three skills I mentioned above, will not change with the progression of data science tools or languages. 

Even with today’s top-notch data and technology, extracting meaningful value from data and AI projects remains a formidable challenge. Just because an organization has data does not mean it is worth anything. And the more sophisticated a business case is, the more involvement you’ll need from data scientists to gain a return on your investments. People capable of achieving this will always be in demand.

Further readingWill Low- and No-Code Platforms Steal Developers’ Jobs?

Data Science Basics Remain the Same

Yes, data science has changed since the turn of the century. New languages emerge, new technologies crop up, new use cases demand our attention — change is constant. I am always analyzing books, articles, videos and the competition to see how the field is changing. There are always new problems to solve and new things to learn.

But the pillars of what it means to be a successful data scientist have not and will not change. Though data science looks a lot different than it did, it still boils down to the same methodologies and processes: preparation, modeling, deployment. It still requires creative minds to turn business problems into data problems. It still requires unique solutions from people who can dive deep into code and solve an organization’s toughest hurdles. And above all, it still requires people with an endless sense of curiosity and initiative.