Quantitative Data Needs Context
/A Wall Street Journal interview illustrates the importance of context when presenting quantitative data. The segment, “95,000 Hours Saved: Unique Ways Companies Are Tackling Worker Frustration,” describes several ways efficiency can be quantified, but some of them invite questions.
Two examples illustrate lessons from Chapter 9 of Business Communication and Character about comparing and explaining data. What do the numbers really mean? Why do they matter? To help students think more critically about data, consider discussing these examples from the WSJ story, posted as a podcast on YouTube:
1:58: To reduce meeting time, Shopify eliminated 12,000 events and saved 95,000 hours. I have questions, and students should too. What percentage of events does this represent? What types of events? What is the context for 95,000 hours: how many employees work how much time total? What was the result? What was gained? What was lost? Are employees doing something productive with their new-found time, or are they working fewer hours, or have jobs been eliminated? In other words, so what?
6:35: AT&T reduced time by eliminating a process that sounds insane: listing on an expense report everyone who came to an employee celebration (e.g., an anniversary). The company saved 28,500 hours. As of January 2023, AT&T had about 161,000 employees (down from 280,000 in 2017!). I’m curious about the number of parties and how much time people spent entering names. Also, with 161,000 employees working, let’s say, a 35-hour week for 50 weeks per year, that’s 281,750,000. 28,5000 represents 0.0101% of the total work hours. Is that significant? Maybe.
Regardless, the data point seems a bit silly when extrapolated, but the process was silly too. I wonder why the process existed and whether employees do similar tasks that might reflect management’s distrust. That seems to be a more useful question for the company to address.
Without a fuller picture of the “efficiencies,” these numbers seem more like sound bites than meaningful statistics for decision making. This is a news report, so let’s hope companies are clearer about why this matters when they communicate with employees and shareholders.
