If a data scientist identifies a critical insight, but no one around them understands the discovery, does it matter? The value of even the most groundbreaking insights can be limited if their significance is lost or distorted in translation. This is a reality that many organizations face as they invest in data initiatives to drive outcomes, such as better decisions and operational efficiency. Among the most successful tools for bridging this communication gap, data visualizations help organizations transform data-driven information into meaningful, digestible, and actionable intelligence.
The case for visualizing data is clear given the statistics related to graphical information delivery:
Such findings support the notion that visualizations can be powerful vehicles for quickly and clearly communicating insights, creating a shared understanding of data-driven intelligence, and accelerating decision-making processes. It is easy to imagine the business value that well-crafted data visualizations can deliver to professionals across many functions and organizations.
Adeptly visualized data helps analysts and their audiences perform exploratory analysis, which can reveal initial patterns, trends, and outliers. Data visualizations also support explanatory analysis that aids analysts in understanding causation and correlation between variables.
Whether plotting data points using Excel’s chart features or developing a dynamic dashboard in Tableau or Power BI, a range of professionals—including frontline personnel, project managers, data scientists, and software engineers—can increase the efficiency and impact of their efforts by learning how to implement visualization tools and techniques. For example, administrative teams can develop visual reference tools to inform their processes and report trends and outcomes. Managers can study the principles of data visualization to cultivate interpretative proficiency and develop a shared vocabulary with their teams. Analysts can identify patterns revealed in graphical depictions of data, and practitioners in more technical fields can produce dynamic visualizations in R and Python to explore large data sets for advanced analysis and modeling.
Data visualization is likewise applicable across industries, as several use cases illustrate:
In all scenarios, such data applications are only as effective as the graphic displays that practitioners design to render them accessible.
Knowing the elements that make data interpretable through persuasive, clear, and reliable visualizations is critical for amplifying informative data narratives. To create impactful data presentations and avoid common data visualization hazards, designers must have an understanding of:
Let’s go back to our original question: if a data scientist identifies a critical insight, and their colleagues and stakeholders don’t understand it, does it matter? The answer, it seems, is that insights can languish unnoticed or misinterpreted if they are never clearly conveyed. Reaping the oft-touted rewards of data analytics requires skillful communication of relevant insights. Without that level of clarity, organizations will never be able to capture maximum value from their investments in infrastructure and data collection. Equipping teams to produce compelling and accurate data visualizations is just one essential step toward realizing the full potential of data resources. In our next blog, we’ll explore other strategies organizations can implement to boost the ROI of their data initiatives through effective data communication practices.
Data Society provides customized, industry-tailored data science training solutions—partnering with organizations to educate, equip, and empower their workforce with the skills to achieve their goals and expand their impact.