by Katie Kruger
When companies measure their culture, they often get data that captures the organization as a whole. That information is just the tip of the iceberg as far as truly understanding the full nuance of the culture. It is really important for companies that measure their culture to also look at their survey data through a demographic lens, because that can reveal truths about your organization— both positive and negative—that might have been overlooked previously.
When we measure culture, we are looking to see if the data tells the story of a question that we are looking to answer. During that process, we collect demographic data, which is often thought of as gender, race, age, etc. Often, however, we are collecting other kinds of demographic data as well (or instead), such as business function or an employee’s location within the broader organization.
There are two main reasons why an organization would want to collect such information. The first reason is that a larger effort paints a picture, but demographics tell the detailed story. Flying over an ocean gives a beautiful bird’s eye view, but there’s nothing like having your toes in the sand. Digging into that metaphorical sand can really tell us what is happening in the mini-cultures within an organization.
The second reason is that we all need to understand, gain, and retain the support for our ethics and compliance initiatives. We all need the numbers to back up those things that we want to do, which is what makes collecting demographic information really worthwhile. Organizations have finite resources, and they have to make strategic decisions surrounding how to utilize those resources, as Ethisphere’s Chief Strategy Officer, Erica Salmon Byrne, pointed out in a February 2024 “BELA Asks” episode of the Ethicast. In that episode, Erica talks about how compliance training suffers from the “peanut butter” approach, in which training is spread thick and even across an organization to ensure that everybody gets the training that they are supposed to have. She goes on to say that sometimes you have to figure out how to satisfy 1) knowing that everybody got what they needed without 2) giving out too much peanut butter, since not everybody always wants to eat peanut butter sandwiches, so to speak.
Erica jokes that perhaps she beat the peanut butter metaphor to death, but it really stuck with me as I’ve done work with our clients here at Ethisphere. There is a fine line between giving what’s necessary and giving too much. We all need something different to be successful in life, just as everybody needs something different in an organization. These needs vary from person to person, from organization to organization. I keep thinking about the peanut butter metaphor because it can go even further. Just like any other food, peanut butter is getting expensive. It is not always possible, and can be outside of the budget, to give everybody the same thing, the same amount of time, or the same resources. Maybe senior leadership can’t visit every pocket of the organization. However, by collecting that demographic information, we can really see where we should be utilizing those finite resources.
When conducting a culture assessment or a culture survey and an organization wants to gather specific demographic data, what are they typically looking for? Is it more than simply filtering enterprise-wide questions by a dimension of diversity?
We are looking for that story that is hidden within the data. Every data set has a story to tell. The trick is understanding how to properly contextualize it so the story holds up under scrutiny. With a culture assessment, we’re looking for that story. We’re looking for things that maybe we don’t have an idea of or a pulse on yet. Outliers, biases – these are the types of stories that we will find in the data that may not have been considered at the enterprise-wide level.
An example of something that I’ve seen in some of our clients’ data recently is what could be called the “tenure smile” representing cultural perceptions based on how long employees have been with the organization. Those who have been there the least and those that have been there the longest have pretty favorable perceptions, so if we visualize it, it would look like an inverse bell curve. Those that have been there the shortest amounts of time have generally favorable perceptions, so they’re at one end of the line, like the corner of a smile. Those that have been there for a while, and are in the middle of the tenure range, have less favorable perceptions across the board or perhaps within a given aspect that we are measuring. Those employees that have been there the longest, like the newcomers, have high favorable perceptions.
It makes sense if you sit and really tease it apart; those that have been there the least are perhaps closer to their onboardings or they have just been hired and they’re really amped about what’s happening with the values within the organization. Meanwhile, the people who have been there the longest might be senior leaders or people who are there at the company because they really believe in what’s happening within the organization.
The people in the middle? Maybe they are considering leaving the organization because they don’t think that leadership really embodies the values. Maybe they’re they’ve spoken up about observed ethical misconduct and nothing happened, so they’re a little more jaded about the state of things within the organization. This is an example of one of the trends that we look for, to see if there is a reason to dive even deeper into the data. Maybe those people aren’t jaded, after all. Maybe they just need a refresher on the values of an organization, but those are the stories that we might not even begin to tell unless the data brings the story to us.
When talking about tenure smile, there is a tendency to wonder if there is a particular “tenure band” in which employees are most likely to hit that bottoming out period. It would be easy if there was a universality to that, but when it comes to this kind of data, it is really important to collect what is valuable to your organization. If we’re looking at Ethisphere’s data from April of 2020 to now, we see the highest ethical favorability in those that have been with the company less than one year. The “smile” decreases in the next band, measured in increments of one year, and “bottoms out” at those that have been with the company between five and six years. Starting at seven years, the data begins the climb again, raising to the next highest point with those that have been at a company for ten or more years. Results at your company may be different because tenure can be heavily dependent on the age of an organization and the age groups within it. If we collect data surrounding age bands and generations, Ethisphere has a ton of great research on how different generations view different pieces of ethical culture. Those pieces will show tenure can cross-reference with age in a way that creates heavily nuanced results that are highly dependent on how a given organization exists in the world.
OVERLOOKED DEMOGRAPHICS & OUTLIERS
Another question that arises frequently is whether there are commonly overlooked demographics that the company would be wise to consider. Once again, the answer to this heavily depends on what data you are already collecting within your organization. Anytime you can layer your survey results with other metrics that you already have, it multiplies the value of the data that results. Aligning what you are collecting in a culture measurement with other HR data, aligning it with click metrics, aligning it with real-world locations within the enterprise… those factors all bring potential value.
Often, we will work with clients at Ethisphere and find something in their data that catches our notice, yet we can’t easily tell what the story might be behind it. When we ask the client if that detail is surprising to them, the client often provides additional detail that was not in the culture survey, but provides critical context to the survey results. An outlying demographic group or data point might be the result of a location that had a lot of turnover. Maybe the collected demographic is made of respondents that are new to the parent company due to recent acquisition. When we put the datapoints in the correct context, sometimes outliers suddenly make a lot more sense.
When including demographic information in a culture measurement, it really goes back to those questions about the business as a whole. Ask yourself how you plan to use the information and work backwards from there to better understand what data you should collect. Are you going to roll the survey out to leaders across different market areas? Then you should probably collect information about those market areas first. Are you going to roll it out to leaders in a given country? Then collect that information. Do you want to look at how different cultures in different countries affect the trainings that you provide or the way your trainings are received? You need to collect that information from those targeted countries.
The Ethisphere 2023 Culture Report had some very interesting results in it about age cohorts, specifically around Gen Z employees, how often they experiences workplace bullying, and the extent to which they reported it. That report involved some eye-catching data that was so far outside the average that it commands your attention. But what if you find a similarly dramatic data point within your own assessment? You’ve done the work of collecting the data from your employees, you’ve spotted a substantial anomaly within a particular demographic…what could or should you do with that?
This is an important issue that gets to the heart of why we collect demographic data in the first place.
Data can tell us where things are going well. It can tell us where things need improvement. At this point, we’d recommend you lean into both sides of those kinds of discoveries. Find out what’s going well when you spot a really positive data point, find how and where you can celebrate the work that’s been done, and dig in to see how it could be replicated elsewhere in the organization. Conversely, take those negative data points and use them to find out where you need to make an improvement.
For the ethics and compliance officer, there is a natural tendency to want to fix everything. However, it’s likely more productive, and more achievable, to pick a couple of data points that you and your team can really focus on, take action, and really dig in. That’s where you get the story and the detail behind the data. And this is often a hard part as well; it can involve some self-reflection on the ethics and compliance team’s part. It can involve self-reflection on senior leaders’ part or managers’ part. Discovering potential personal bias that prohibited you from seeing this story before, or even realizing that this trend was there can be pretty difficult to uncover. This crux is where the hard work of the data collection comes in, but also the beauty.
I got into data analysis because I think data is so beautiful. It tells us a story at times that we didn’t even realize existed. No part of data collection or analysis is easy, but by doing the hard work, you can really turn it into a Choose Your Own Adventure book for your organization. You get to change the ending based on the actions that you make from the data. And that is a beautiful thing.
ABOUT THE AUTHOR
Katie Kruger is a Data Analyst for Ethisphere, where she works on the Data & Services team, helping organizations properly contextualize the results of their culture measurement efforts.
