Why we need more data journalists, not data scientists
“There is a shortage of data scientists, as the demand for analytics rises.”
It’s an age-old discussion in the field of analytics: Is there a need for more data scientists, and data experts, to really get the most value and capability out of an analytics solution?
In this blog, I want to challenge this view, and ask an important question: Is there really a skills shortage of data scientists, or are we focusing on the wrong skill-set in general when it comes to hiring for an analytics role? What if there is more important criteria?
For me, it comes down to two key role distinctions: Data scientist, and data journalist.
The changing expectations of data scientists
Tom Davenport, a well-known thought leader in the analytics space, states in an article for HBR: “More enduring will be the need for data scientists to communicate in language that all their stakeholders understand—and to demonstrate the special skills involved in storytelling with data, whether verbally, visually, or—ideally—both.”
While this excerpt is from a post published in 2012, his words still remain true. There still are significant constraints in developing the skill-set of data scientists today.
A good data scientist can develop great models, extract intelligent insights, and solve complex business problems with analytics, but most lack the skill of telling a good story.
The data journalist, on the other hand, is a new kind of data scientist that lives up to the claim. They are effectively an analytics expert within an organization that can not only solve a complex data problem, but also tell a compelling story through the practice of data storytelling that engages different types of audiences, and helps people understand the context behind the numbers and the value it brings to the business.
A great data journalist is an investigator who gets a thrill from extracting information, but also has the skill to turn the facts into a compelling and digestible story using their own personal interpretation, knowledge and arguments to inspire decision-makers to care and take action.
Instead of just showing people the numbers and expecting them to do something with it, a data journalist goes the extra mile to give information consumers the best chance at finding value.
The business case for data storytelling and data journalists
Through my discussions with analytics executives, two main and recurring challenges with understanding their business through data are typically highlighted.
1. Lack of context: Often the business doesn’t see the relevance of analytics in its decision-making. Potentially, the lack of relevance is linked to the style in which the outcomes are communicated.
2. Imbalanced: We see an imbalance in the level of detail presented, it’s either too much or too little.
These challenges are the reason storytelling is seen as an increasingly relevant and crucial skill. Stories help convey a message and stimulate action. We use them to persuade a colleague or earn support for a project. So when it comes to analytical content, a data expert that knows how to tell a good story so the business value is unlocked and the data can have its day is just as important.
Are organizations prepared to take on more data journalists rather than data scientists? Or to invest in capability to enhance the skill of the data scientist, and provide tools to help them turn their “supermodels” into actionable stories that impact the business with right decisions?
With data stories expected to become the most widespread way of consuming our analytics by 2025, and with many investing in it as a capability today, it’s a priority that, at the very least, must be looked at more ahead of time. Otherwise, you risk falling behind.
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