Feedback about our Salary Survey

We had some really great reactions to our Salary Survey and we appreciate all of your feedback! In this blog I would like to share some of the feedback we got, so you'll know what we'll focus on for next years round of our Salary Survey.

Most of the reactions were positive - both hiring managers and data professionals were interested in our results and thought there were some interesting insights shared in it. Many of the readers said that they were glad to see a Salary Survey that focused solely on our sector, since it can be tricky to find the right information in other places. 

We also got some positive feedback about how we chose to present our result, without superfluous graphics and selling attributes. It's always nice to hear that our graphs looks good :)  

The feedback we got that we are going to focus more on for next years version to improve our Salary Survey, is based on comments we got from you:

  • Geographical spread : We didn't ask about the geographical location of our candidates in the survey. This would allow us to investigate if there are differences in salary levels between Stockholm and other locations in Sweden. Also, we would like to include Norway, Denmark and Finland in our salary in the coming years.
  • Seniority : We didn't ask about how long our respondents had been working in their respective professions. This is very likely to affect salaries, and it would be interesting to look closer at the interaction between role, seniority and salary.
  • Compare our result to other salary surveys : Some of you wanted us to compare the results we have with other salary surveys out there. We are looking into this, but haven't been able to find any good ones. If you have any ideas, let us know! 
  • The 90th percentile : There were a few people wondering about the top earners, above the 90th percentile, and what distinguished them. This is definitely something that we will include in our next report, along with the  lower salaries as well (teaser: the top earners were either Managers or Data Scientists. They had a few things in common: their main outputs were Statistical models and/or Machine Learning Algorithms, and all of them worked for private companies. They were using different tools though - SAS Base, SQL, R, Python and even SPSS!). 

The main reason for not asking all of the above was first of all to keep the survey short. We don't want to take up too much time from respondents, since that increases the risk of dropout. Also, we didn't have that many respondents this year, which would make some of the above comparisons too speculative. The low number of respondents is probably due to the short data collection period and being a new company on the market. Next year we are confident that we'll get more respondents! If you have any additional thoughts about our report, or would like to see it. Let us know HERE.

As all of you surely know, high quality data is gold. Therefore I would like to end this blog post by saying a big THANK YOU to all of our respondents this year!