Data visualization is a tool for comprehension, storytelling is a tool for persuasion. Here is the hypothetical scenario displayed on the map above and one that I-O practitioners may find relatable: 50% of employees at XYZ Co. voluntarily leave after just one year or are put on performance improvement plans. As an internal consultant at XYZ Co., you use the map visualization to identify a high overall turnover rate in the Midwest region. You do a little digging and discover that hiring managers for that market are conducting unstructured interviews as their sole means of candidate selection. You know that structured interviews have significantly greater predictive validity than unstructured interviews (McDaniel, Whetzel, Schmidt, & Maurer, 1994). You create a slide deck for all hiring managers that explains the difference between unstructured and structured interviews, provides an example of a structured interview template, a dot chart showing the difference in turnover rate of the Midwest region vs the rest of the company, and even include a correlation matrix showing relationships between interview types and key outcomes like performance. You leave the meeting feeling confident that all hiring managers will be converting to structured interviews right away. Fast forward a year later and not a single manager has converted, what happened?
As scientists, I-O Psychologists are often naive in their belief that simply laying out the facts unencumbered by fluff or anecdotal evidence should be enough to gain support for our suggestions to managers and organizational leaders. The reality is that many leaders want to make evidence-based decisions without having to be presented with the evidence, at least not in its original form. Sticking with our example, there is research showing that receptiveness to switching to structured interviews was actually higher when study participants were told a story in which a structured interview resulted in a favorable outcome versus simply being given evidence as to why structured interviews are better (Zhang, Zhu, Ritter, Thiele, 2019). The data scientist in us may loathe qualitative feedback but this is where we find the anecdotes that help craft a story. Without breaking any promises for anonymity, use real information from the organization to provide actual examples that support the research and your data visualizations. If there aren’t any stories to be crafted from the organization itself, draw from your own experiences or from other organizations. What matters is driving home the point made in the data visualizations by putting faces and names (even if fictional) to the relevant takeaway.
Key Takeaway #5: Use storytelling to add a human element that supports the data visualizations and evidence.