Barriers To A Thriving Data Visualization Culture
It’s no secret: the explosion of data continues to revolutionize organizations. Across the world, forward-thinking leaders embrace this opportunity, pioneering new ways for their organizations to utilize their data. As data floods in, a new kind of data expert is rising up, equipped with the rare skills to bridge the gap between complex information and the human insight and understanding needed to harness it: the Data Visualization Practitioner.
While data scientists give you the raw material to be “data-driven,” dataviz professionals provide thedata vision: they make critical information useful to those who need it most. The tools they create don’t just give insight into the data, they provide sharable windows around which the organization can discuss the data’s use, potential, and validity, driving cross-department collaboration and innovation.
Organizations that have integrated data visualization professionals effectively have already yielded tremendous benefits: they’ve given directors of critical public transportation systems the ability to spot and mitigate catastrophic overloads; they’ve created novel, life-saving methods for doctors; they’ve transformed the public’s understanding of global health.
But — despite these examples — dataviz practitioners, again and again, fail to realize their full potential for their organizations.
To understand the challenges, let’s envision a dataviz professional named Alejandra. She’s got mean Python chops, a knack for data exploration, and expertise in visual perception and human-centered design. She’s surrounded by brilliant leadership in a data-driven org. Yet she often struggles to bring her skills to fruition in her org, for three main reasons:
In many organizations, unlocking the power of dataviz practitioners rests largely on leadership. But leadership — pressed to deliver products — typically has little space to explore new processes and methods. This limits their ability to establish a robust dataviz culture, dataviz professional Bridget Cogley points out.
Alejandra’s managers often come to her with a chart request that will get them by on their tight deadline, rather than collaborating to create powerful and nuanced data visualizations.
Needing charts that feel familiar and trustworthy, leadership often must respond to the introduction of new visualization tools by asking, “Can you use charts I’m familiar with, like the kind they have in Excel?”
As a result, leadership leaves Alejandra has little room to apply her expertise, ultimately costing the organization the opportunity to understand and use its data more effectively.
Think about Alejandra again: her dataviz skills bridge both tech and communication — an incredible opportunity for her org to integrate across domains. But also a huge challenge: she has to learn to communicate its role to wildly different stakeholders, both of which see half her work as lying in someone else’s domain.
The challenges for each domain are distinct:
While her org has skilled communicators and designers, they already have a rich toolset of methods for communication. When it comes to dataviz, the need to leverage information design means the work seems to sit squarely within the domain of less practical approaches that designers tend to dismiss as overly theoretical and academic.
