Explain Your Data Visuals to Build Trust With Your Audience

Content marketers are increasingly using data storytelling to promote their products and for good reason. As I explain in my infographic, “Hook Your Audience: Tell High-Impact Stories With Your Data”, there are multiple benefits to this approach, including the following:

• An estimated seventy percent of typical audience members are visual learners.
• Visuals are processed much faster than text.
• Visuals encourage audience members to focus on a topic.
• Visuals help presenters simplify complex concepts.
• Simplifying complexity fosters trust and strengthens your ability to persuade others.
• It’s easy to share a visual via email or social media.

These advantages apply whether you use external data or data culled from your company’s internal database. However, and far from trivial, data can be exploited for nefarious ends. Mark Twain warned, “Facts are stubborn, but statistics are more pliable.” Economist Ronald Coase, the 1991 Nobel Laureate, similarly worried about the ill effects of manipulating data. Ben Bernanke, former chair of the Federal Reserve of the United States, said, “Aggregate statistics can sometimes mask important information.”

In a world of growing suspicion about putative facts, it should be a no-brainer for a content marketer or superstar speaker to apply quality standards when preparing infographics to share information. This means, among other things, clearly explaining what your data represents, how it was collected, how it was cleaned (if at all), and how you selected the format and timeframe. If you are using external data, you must understand how the raw data is assembled and then modified for things like outliers, nonsensical data points, and missing numbers.

A failure to take responsibility to use good data to create visuals is dangerous. If end users realize you have utilized imperfect inputs, they will likely lose faith in whatever else you provide in the future. This translates into a weakened ability to grow your sales, expand market share, and enhance your brand value. If you cherry pick or otherwise willingly provide low quality numbers to decision makers who are using your data and related visuals for compliance purposes, you could end up in litigation or being investigated by regulators for alleged fraud or malpractice.

Other than data breach scandals and related lawsuits (and there are many), data-related problems include, but are not limited to, the following:

• Presenting hypothetical investment returns for a start-up without telling the end user the numbers are projections only or stating they are projections but not being candid about the underlying assumptions used to calculate the “what if” data
• Not first testing an algorithm or model that uses primary source data to calculate the data you provide to end users (in visual form) and later discovering your algorithm or model incorrectly calculated data you gave to others
• Allowing a conflicted party to change data instead of properly having safeguards in place to avoid someone inappropriately inflating or deflating data to their benefit. You don’t want someone to artificially boost profitability to secure a bonus or reduce numbers to hide a loss
• Using data that is stale or does not reflect structural changes in an economy, an industry, or the operations of a company such as using historical travel data after 9/11 or the period of COVID lockdowns when flights were reduced to a trickle
• Not considering discontinuity in data reporting such as what happens when a major exchange halts trading in a particular stock due to unusual volatility, company announcements of a merger, or order imbalances
• Ignoring outliers that skew data such as including Elon Musk’s total compensation in a data set of CEO salaries, bonuses, and options
• Interpreting an economic statistic such as the Consumer Price Index as a failsafe indicator of inflation since, according to the Bureau of Labor Statistics website, CPI measures price changes for urban consumers and wage earners and not individuals who live in rural communities.

The list of data problems is long. Other challenges include inconsistent formatting, inaccurate data, duplicate data, unstructured data, and irrelevant data. As we know, GIGO or Garbage In, Garbage Out rears its ugly head if we create aesthetically beautiful and seemingly powerful visuals based on inaccurate and inferior numbers.

I experienced data adversity firsthand on a former project that required me to create a data visual of valuation numbers for structured investments owned by a Fortune 50. During my review of the file provided by an outside party, I noticed irregularities. After trying to replicate numbers that seemed off, I asked the provider to explain how their model worked, what assumptions they used, and the kind of testing protocols they used to ensure integrity of what they gave their clients. Long story short, my persistent inquiries led to their reluctant conclusion that their model was flawed. This clearly spelled trouble. I ultimately received accurate numbers that made sense, but it cost us all time and money. Imagine if I had provided executives with infographics based on the bad numbers.

Data visuals are popular and powerful. I urge content marketers and professional presenters to use them whenever possible with the proviso to verify the condition of the input data.

If you want to talk about your data visualization needs, drop me a line or visit my website page about data visualization at.