3 key roles in Data Visualization

Intulog • June 20, 2020 • 4 min read

In the last post, we looked at how data visualization has exploded into the mainstream with the COVID-19 pandemic. But this global interest is only an acceleration of the already growing centrality of data in our jobs and lives.

After all, as estimates suggest, the total data created, captured or replicated in the world is estimated to reach 175 zettabytes (ZB) by 20251. As the world grows into greater levels of digital saturation, visualizing this data in order to make it understandable in human terms is becoming an ever more pressing need.

The ubiquitous growth of data visualization

While data visualization has had a key role in several industries such as technology, finance, and retail, we are now seeing the rapid spread of visualization solutions within a variety of different industries and verticals.

In manufacturing, for instance, dashboards allow managers to track important metrics and KPIs related to quality assurance, production performance and downtime, supply chain, and worker safety among others. Visualizing such vast volumes of data allows for early detection of patterns and trends, improving efficiency and productivity and reducing waste.

In education, schools are better able to track and understand relationships between student attendance and academic performance to focus greater resources on those at risk of chronic truancy or dropout. In higher educational institutions, visualizing student success data such as enrollment, retention, and graduation rates allows for smarter decision-making about the provision of academic and other infrastructures.

Healthcare is another growing beneficiary of data visualization, from hospitals using electronic patient records and admissions data to narrow down on possible care gaps to public health agencies visualizing public datasets to make crucial decisions about population health, such as vaccination programmes or preventive health services. Infographics and data visualizations also help communicate crucial health messages to patients and consumers.

Human Resources (HR) is another domain rapidly opening up to the possibilities of visual explorations of data. Whether it’s building ideal candidate profiles by visualizing data of current top performers and leaders or mapping retention strategies and employee turnover, data visualization is fast becoming a must-have.

Journalism is also betting big on data visualization. Even before the COVID-19 pandemic media outlets have been visualizing a variety of news events from elections to sports to natural disasters to a variety of civic and political issues.

Diverse roles behind effective visualization

Technology has certainly played a vital role in the growing relevance of data visualization. Digitization is permeating into virtually every space of activity, IoT enablement is seeing a steady rise, and visualization tools have become far more user friendly and affordable. Indeed, it seems as if data visualization is now as easy to execute for interested amateurs as for professional organizations.

However, effective data visualization depends on the cooperative efforts of several kinds of experts, each bringing different skill sets to the table:

The domain expert

Not all numbers are alike. While, on the surface, the difference between marketing data and public health data may seem to simply be a question of inputting different sets of metrics, this is most often not true. Giving meaning to a line graph or scatter plot depends on having a context for what different patterns mean. Every process has esoteric tendencies and drivers that must be taken into account, the understanding of which is supplied by domain experts. With the COVID-19 pandemic, for instance, experts on public health and data visualization have urged understanding the basic principles of epidemiology and the different definitional nuances before attempting to visualize data around spread of the virus2.

The data scientist/analyst

Of course, at the heart of data visualization processes is the data scientist who uses techniques such as machine learning, statistical modelling, and artificial intelligence to meaningfully process data. Without strong understandings of what data means, what tendentialities it contains and how they can be predicted, visualizations amount to nothing more than modern art.

The visualization expert

While visualization skills are often seen as only one part of the broader repertoire of data scientists, the importance of visualization experts is gradually being recognized. The best visualizers aren’t necessarily the strongest numbers people. After all, data visualization requires the combination of a scientific understanding of data as well as an aesthetic understanding of visual design. Without the latter, data visualizations can become excessively complex and cumbersome or fail to engage. Understanding a specific audience and how to communicate to them, and how to adapt principles of visual design to that understanding is the vital contribution that data visualization experts bring to the table.

The profusion of data in every aspect of our lives is useless unless it is effectively translated into actionable insight and acted upon. While the promise of AI may come to fruition in the future, data still needs to be translated into patterns understandable by humans in the present. As weaving effective narratives out of data becomes key to virtually every kind of business today, data visualization is becoming a vital force across the board.

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