To Be An Effective Communicator, Take An Analytical Approach To Storytelling
Stories are powerful communication tools. We tend to pay more attention to narratives because they carry more emotional content and are easier for us to remember.
A good story follows this basic structure:
• Beginning: Setting the stage and introducing the protagonist and antagonist characters.
• Middle: Rising action that reveals a conflict and builds to a climax.
• End: Falling action that shows the consequences of the climax and a resolution.
When it comes to making sound decisions, facts matter. That’s why a foundation of trustworthy business communication is built with verified data. Communicators, however, often face challenges when asked to analyze and tell data stories:
• Creative communicators are fluent in words and images, often not math and data.
• Executives are usually fluent in numbers, charts and formulas.
• Unless you are a scientist or engineer, you likely have little formal training in data analysis.
• Data can be difficult to gather and filter and can require analysis to identify what is valuable.
For over a decade, I’ve helped large organizations and corporate communications teams use data analytics to gain insights and make data-driven communications decisions using our email intelligence platform, PoliteMail. With my background in computer science and previous 15 years running a tech-focused communications agency, I have firsthand experience combining the creativity of communications with the logic of data analysis to make a positive impact on the growth of businesses and careers
To address the challenges listed above, consider utilizing the following processes and tools as a framework for building the skills of effective storytelling with data.
Step 1: Start with good questions.
While big data can be powerful, it’s all too easy to overwhelm your message with too much data and lose the message in the details. Begin with your conclusion or claim, and keep it in mind as you work, with a focus on presenting the best evidence. Keep the details organized as supporting evidence, having it ready for if and when you are asked about it.
Let’s say our colleague Allison was tasked with answering the question: “Is our budget for employee education paying off?”
Intuitively and anecdotally, she believed employees who took advantage of the company-sponsored educational programs performed better and rose up through the ranks faster. But how could she prove it when asked to justify the budget? This led her to think of some other good questions.
• How many employees took advantage of the training programs?
• Were certain groups or roles more likely to take them than others?
• How could outcomes for those with and without training be compared?
• What was the total cost relative to the total benefit to the company?
Thinking through a set of good questions provides the targets to dig for answers.
Step 2: Identify your data sources.
In order for Allison to answer her questions, she needed evidence and had to collect the data. While anecdotal evidence and personal observations can be compelling stories, presenting truth with facts, more often than not, requires a more professional investigation and evaluation of data than a handful of stories or case studies.
What data would be required to do this? Here’s a good list:
• Total quarterly cost of training and education courses.
• Records of training courses started and completed, categorized by type of training courses and by people, roles and business units.
• Data about performance reviews, promotions and turnover.
• Survey data from managers and staff.
It’s important to identify your data sources before creating your hypothesis because this ensures you will actually have the information to answer your questions. Save your anecdotal evidence and personal observations to use as emotional connections in support of the data.
Step 3: Compose a testable theory.
In the scientific method, you begin with a hypothesis and attempt to disprove it using controlled experiments and data. If you can’t disprove it, then you have a good theory.
It isn’t good enough to simply find a data point that supports your theory and call it a day. That approach is simply an expression of confirmation bias.
Instead, take the opposite approach: Try to find the data to disprove it. If you do, you have learned something! Now you can start again with a better theory.
Allison’s initial theory was: “Employees who complete training are more likely to get promoted and stay with the company longer.” Because she was justifying a budget, she needed to get more specific and answer the return on investment (ROI) question. If her initial theory proved true, she could then apply some cost/return numbers to it.
She quickly uncovered that the “employee education budget” included four primary expense categories:
• Management and professional skills training.
• Security, IT systems and software tools training.
• Specific process, procedure and policy training.
• Tuition reimbursement.
This breakdown of expenses allowed her to dive deeper into the data and refine her hypothesis, as it became obvious not all training was equal in reach or results.
Tell a compelling story with data.
Communicators often create presentations with the intent of inspiring action and moving minds. Creating a narrative is a powerful tool, but without asking the right questions or analyzing the right data, any conclusion is at best left open to challenge, and at worst it leads others astray.
By following the steps outlined above, you’ll be able to better test theories, present stories based on facts and advance your career because you’ll be seen as a more trustworthy contributor to better decision making.
By Michael DesRochers, Founder of PoliteMail Software, an email intelligence analytics platform for Outlook and Office 365.