Creativity in data science can be seen in anything from innovative modeling, thinking up original ways to collect data, developing new tools, and being able to visualize data process a few years down the line.
Yet, many of the most innovative, future-forward, and successful businesses combine data-fueled decision-making with human creativity. This keeps them relevant and competitive in the market.
Misconceptions about creativity
A common misconception about creativity is that it’s only useful for the arts—painting, writing, acting, and so on. It’s widely believed that the right side of the brain is more artistic and innovative, performing tasks linked to the arts and creativity. Meanwhile, the left side of the brain performs tasks that require logic, like science, and mathematics.
However, it’s not as straight-forward as this, and we can simultaneously engage both sides of our brain in a whirlwind of scenarios and tasks, including data analytics.
Creativity is about more than original ideas
Creativity comes in all shapes and sizes. Ingenious, highly original ideas are of course important, but sometimes the greatest test of creativity is taking an existing idea and giving it a new lease of life.
This is especially useful in the business world, where the same ideas can be applied in entirely new circumstances and across industries to produce varying results. For example, Henry Ford took his inspiration for the car assembly line from the assembly lines for processing meat. It wasn’t an original idea but the new application was a stroke of creative genius.
Data science is an art
In the same way, it would be dangerous to underestimate the crucial role of creativity in data science. Undoubtedly, a data scientist requires a foundation of technical skills that may not traditionally be viewed as creative. These include the ability to interpret, manipulate, and extract meaning from data, and then use it to build predictive models and generate business insights.
Building on this, Dr Jabe Wilson of leading global analytics company Elsevier, justifies data science as an art because of the need to use “exploratory workflows.” Like artists, data scientists create and follow their own creative flowcharts.
This means “starting with the right canvas in terms of good data, getting to the right model (using qualitative data and intuition), and putting the tools at your fingertips so inspiration can find you busily at work, whether you’re identifying candidate drugs for treating rare diseases or reducing the need to use animals in drug safety experiments.”
Humans will always have the upper hand
Despite the rapid growth of artificial intelligence, there’s no substitute for human critical thinking and reflection. Data Science isn’t all about feeding data into a systematic, rule-based machine to reach a hard-and-fast solution. It requires knowledge of what systems and processes to use and how to apply them, which also depends on a lot of factors that will be specific to certain projects or companies.
Working on alternative solutions to a complex problem over time, as opposed to relying on a quick-fix from a machine, will give you a deeper understanding of the problem, leading to a more effective and long-lasting solution.
As James Bridle puts it, “While even a mid-level chess computer can today wipe the floor with most grandmasters, an average player paired with an average computer is capable of beating the most sophisticated supercomputer.”
Spreading the wisdom
Data science is intricately woven into other areas of business. It’s vital to be able to package numbers and explain their significance in a digestible and understandable way, making them accessible and valuable to others in the company.
This certainly requires data scientists to get their creative juices flowing. Their challenge is to explain their conclusions to others in the simplest way, whether through bar graphs, pie charts, or infographics. Which format will allow others to most easily grasp the data and its meaning?
Revolutionizing marketing and sales
As digital transformation changes business processes and customer experiences, data science has never been more relevant to marketing and sales. When the task is to create engaging brands that attract potential clients, data can be used to analyse how individuals might respond to a certain type of message or incentive.
Predictive data models can provide guidance on what type of marketing is likely to attract consumers. This means data scientists have to think creatively about how to collect and present this data, collaborating with marketers to find imaginative ways to tailor content to individuals—a clear example of the merging of creativity and data science.
Happily ever after
Clearly, a marriage between data science and creativity isn’t so unusual after all. Creativity in data science can be seen in anything from innovative modeling, thinking up original ways to collect data, developing new tools, and being able to visualize data process a few years down the line. So, who said science can’t be creative?