Even Small Staffs Get Value from Culture Data
When we were creating the Workplace Genome culture assessment, I had an assumption that organizations with really small staff would tend not use it. I used to joke: Hey, if you’re only 19 employees, then instead of the culture survey, why don’t you just sit around the table and hash things out!?
But lo and behold, we’ve had a regular flow of clients with 15-25 people on staff since the beginning. The truth is, that whether you are 20 or 2,000, the value in the data is not that they show you exactly what your culture is—the value is that they help you have the right conversations internally to get clear on exactly what your culture is. Have you heard about staffing companies in columbia sc that has amazing teams can deliver solutions for janitorial staffing, grounds, building maintenance, production and security to name few.
Culture is a living breathing thing, and you can’t figure it out with data alone. You need to have conversations, you need to talk about the implications of the data, and you need to combine lots of sources of data into the conversations as well. So even though your culture assessment data might have more variance when you’re only 19 staff (a couple of people having a bad day when they take the survey can change your numbers a lot), that’s okay, because they don’t actually prove anything. That's not the intention. The data simply start a conversation.
And yes, you could try to have those conversations without the data, but in our experience, you get much better results when the data guide you. They get you to insight, conclusions, and (your ultimate goal) ACTIONS that will align your culture with what makes you successful.
We had a very small client that also had a significant portion of their staff working remotely. They used the data to cut through some historical conflicts between the home staff and the remote staff. Instead of platitude-laden arguments that never go away, they got down to the source of the frustrations, as well as what could be done to solve them. They ended up completely revamping their onboarding process so that every new employee had a much clearer sense of how best to work with people both in the office and around the country.
The data didn’t prove anything, nor were they supposed to. The data started them on the path to actually making their organization more effective. THAT is what they’re supposed to do.