Org Survey Part 2: Analysis

This is the second part of running an org survey. You can find the questions in part 1.

What Does The Data Look Like?

First step is color-coding the spreadsheet to show trends in the numbers. I made a sample spreadsheet and generated random data – so it looks a little chaotic – but you can see how patterns would (hopefully) emerge if it was data from you know… actual humans.

I used conditional formatting and the colors matched the scale used – so green is good! And red is… not good.

This is all set up in the template spreadsheet. This is in the Google Docs folder with all the templates – including the survey templates from part 1.

My random number generators are not particularly happy with Management

How Does the Data Break Down By Team?

Now we can break out the answers by team! For each question you need a new sheet. The spreadsheet has a template one.

This might look like a lot of work, but it’s all automated. All you need to change is the B column and the chart title. If you pull in a different column, everything will regenerate and you’ll get a nice new graph.

There’s programming in this spreadsheet 😂

How Do Managers Compare?

In the graph above we can see that bunnies are either very happier or very sad, raccoons look to be least happy, and owls are somewhere in the middle. This allows you to see whether some teams seem happier than others, and it’s a good conversation to have with the manager of that team about why they think that is.

You can also use this spreadsheet to generate graphs for just individual managers by deleting some of the data. Make a copy, delete the data for other teams, and everything will regenerate. I make a master sheet for the overall team, and then I make a copy for each manager and delete the data for other teams so that they can focus on their own results.

You can also compare your overall graphs with the ones generated by the manager survey. Because the first set of questions are the same, this can be a really helpful way to see what managers aren’t feeling great about – and if they’re not feeling great about something, it’s pretty likely that filters down to their team.

Now What?

This is all just data. What’s next?

  • Think about the relationship between your manager results and your skip level results – does this give you any ideas of things you can focus on?
  • Think about your team breakdown – what’s the variation like between teams?
    • Are there ways that some teams are more set up to be successful than others?
    • Are there things the managers of less happy teams can work on?
    • Are there things that happier teams are doing particularly well that could be socialized better?

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