The ability to present data and facts in support of an argument within the context of a story is a skill worth developing. It will help you sway audiences with more compelling presentations.
Previously, I outlined a process framework for analytical storytelling and how a communications employee named Allison was using it to justify a budget.
Allison was asked to prove the hypothesis that employees who take advantage of company-sponsored training programs are more likely to stay with the company and get promoted.
It might seem counterintuitive, but her professional approach was to gather data in an attempt to disprove the case. Why? Because if she could not disprove it, then the hypothesis is true, and she could present definitive conclusions and recommendations that her leadership could trust for decision-making.
Here are the two critical steps to take once you have some data and want to reach a conclusion or make recommendations others can rely on.
Step 1: Collect and analyze objective data.
In Allison’s case, she needed employee data.
The human resources, or people operations, department has a wealth of employee data, often in a multitude of different systems. Gaining access or exports comes with approvals and privacy considerations, so planning ahead is important.
Realize that any exports or data files you get are often dirty. What I mean is, the formatting and categorization won’t likely be consistent, so expect blanks in some fields and nontypical values in others. Filtering and cleaning out that junk is essential, and often a bit of a project. Expect to spend at least 50% of your “analysis time” cleaning the data, so if you are not a spreadsheet wiz or data scientist, recruit one to help.
A few considerations when analyzing data:
• Identify important data and metrics. For Allison’s analysis, the data consisted of people who completed various training and when, along with records of promotions and turnover. For metrics, several possibilities come to mind: The percentage of employees who completed various training, the length of tenure with the company for those with and without training, the percentage of employees promoted and how quickly for groups with and without training, and so on.
• Measure apples to apples, using consistent units. It’s important that you’re measuring and analyzing using the same consistent units.
• Answer: “Compared to what?” To be understood, data and metrics must be related to a historical standard or benchmark. If the metric is the percentage of employees who completed training and are still with the company five years later, then this must be compared to a similar measure, such as the percentage of employees who stay with the company for five years.
Step 2: Find your narrative and present your case visually.
With the evidence, Allison was ready to present her case, and the most powerful way to do that is to weave it into a story with a beginning, an intriguing middle and an end, and include the evidence visually.
While today’s powerful analytical tools provide a wide variety of fancy visuals, many are interesting to look at but difficult to understand. Simple is better. Stick to these simple charts most people are familiar with: bar charts, line charts, pie charts and gauge charts. Further, know what charts to use and when:
• To illustrate trends and historic results, use a line chart.
• To show percentage comparisons, use a pie chart or gauge chart.
• To illustrate relationships between data, show comparative bars or line charts.
• To present important numerical results (with or without subcategories), provide summary tables with minimal columns.
To improve your design, your charting program defaults may not be the best choice. Learning how to change chart colors and styles will make your work stand out.
• For on-screen presentations, choose lighter chart colors. Most tools default to deep, saturated colors, which is only better for print.
• Reduce unnecessary noise by eliminating grids, keeping axis lines light gray and keeping tick marks and legends to a minimum.
• Provide comparison data when possible, in a complementary color, to help with comprehension.
Once Allison had her data, she needed a narrative. She decided to tell the journeys of three employees — from first employment to using education benefits to their current destination — using her data to support her conclusions.
She used three charts to make her points:
1. She opened with three simple pie charts to illustrate the percentage of employees who completed mandatory skills training in IT, tools and process, and policy; those who completed optional skills training; and those who used tuition reimbursement.
2. She used bar charts to compare relationships between each group (completed training and didn’t complete training, in each category) to that group’s average employee engagement scores.
3. And finally, she used a line chart to show how long each group of employees stayed with the company compared to the company average.
Analytical skills make you a more valuable communicator.
During her five-minute time slot in the budget meeting, Allison told her story supported by data.
She concluded that skills training led to higher engagement; that optional skills training resulted in faster career advancement within the company and lower turnover; and tuition reimbursement kept entry-level employees on board a year longer than average, but over 80% would leave the company a year after graduating, with a total cost twice as high as turnover.
She recommended using the tuition reimbursement funds for mandatory training compliance and additional optional skills training opportunities.
Leadership was impressed. When pushed on the tuition point, she was able to pull up a slide with the details, and leadership made a decision on the spot. The company now understands the data for most of the education benefits it offers.
Allison earned increased credibility with leadership as a trusted, data-driven communicator. By following Allison’s lead in using a more analytical approach to tell compelling stories, you may advance your career too.