Data scientists: The new keepers of creativity

By John Copeland

Posted on 10-14-2020

Let’s dispel this notion right away: Data scientists are not left-brain robots who quietly crunch numbers in some back room. Rather, they are among the most creative thinkers in an organization, and the work they do is essential to brands that want to get closer to their customers and deliver more personalized digital experiences.

Creativity takes on many different forms throughout an organization. For data science teams it manifests in abstract thinking, tapping their ingenuity to make customer experiences as impactful as possible.

Retraining the human mind

One of the fundamental principles of modern data science is that analysts question everything, even the obvious. After all, data science is about discovery. It’s about uncovering new truths about customers and not falling into the traps of conventional thinking.

However, while the human brain is incredibly powerful, it can be lazy. Take the classic phrase, “If it looks like a duck and walks like a duck, it must be a duck” – which we recognize as perfectly logical thinking. But such cognitive leaps are the antithesis in the world of data science, where even the most innocent assumption can lead to widespread bias.

This is especially relevant at a time when artificial intelligence and machine learning are becoming more prevalent in digital transformation. To write robust and inclusive algorithms, data scientists need to start from a blank canvas and build something completely new – which is the very definition of creativity.

The difference between lazy analysis and modern data science is like the difference between assembling a ready-made flat pack table and building a table yourself from a pile of wood and screws. With the former, you know exactly what the final product will look like and have the precise number of parts to get the job done. With the latter, you have full creative license to build a table in any shape you want, assuming you have the necessary skills and vision.

Successful data science teams increasingly apply this blank canvas thinking to the development of digital experience and even feature engineering for software and services, helping brands answer specific questions about their customers. By drilling down into raw data and combining or transforming it in new ways, data scientists create composite variables that can have more meaning in the context of their analyses, and more value for the teams they support.

Creativity in content delivery

With more companies transitioning to digital customer experiences and the COVID-19 pandemic compressing their timelines, data science has become indispensable. The march toward personalization and relevance on digital channels, for example, hinges on brands being as creative with the format and timing of their content as they are with the content itself.

This is especially true in the case of metadata, which summarizes basic information about data. For example, low-level metadata for a banner ad might include its format, which site it appeared on, and whether people clicked on it. But there are many more dimensions to explore. Did the ad include a picture, for instance, and, if so, what was it a picture of?

This might seem like a minor consideration, but what if the ad is promoting digital photography products? The team behind it could then tailor the image to individual prospects – i.e., serving a photo of dishes from a top-rated restaurant to a food photographer browsing the website. When marketers and data scientists work together to match their content to a customer’s interests in real time, engagement levels can skyrocket.

Bridging the language barrier

This brings us to one of the biggest challenges for data scientists: elevating what they do and making it accessible to the rest of the business. This is where misconceptions about date science often come from: a language barrier that often exists between technical teams and marketing, even as they work more closely together each day.

The onus falls on data scientists to become better storytellers and showcase their creativity to the wider organization in a way that feels relatable. As an analogy, imagine a concert pianist playing Beethoven’s Fifth Symphony for thousands of listeners. Most of that audience doesn’t speak the language of eighth notes and time signatures. What they understand is the emotion that comes through in the music. It’s the pianist’s job to translate the black dots and lines in front of them into an experience that conveys that emotion.

For data scientist to adapt, they must build closer relationships with other teams. Just as they challenge convention and seek new ways to play with data to improve their analyses, data science teams also need to broaden their understanding of how this data will be used. And that begins with exposure to the needs of the broader organization.

Making space for exploration

As with any creative pursuit, data scientists need time and headspace to do their jobs well. They need time to question and break problems down to their individual parts, to experiment, and to challenge each other.

Modern analytics solutions help teams reclaim this time by reducing the administrative burden that has traditionally eaten up so much of their day, allowing them to focus on big picture thinking and find ways to improve the algorithm to suit their needs.

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Brands view data science as a new frontier for their businesses, but to encourage discovery and differentiation, they need to give data science teams freedom to explore. There is a deep well of ingenuity to be tapped in these individuals, and leading companies are increasingly focused on bringing that creativity to the surface.

Topics: Analytics, Adobe MAX, Leadership, Creativity, Brand,