WHY ARE DATA ANALYST INTERVIEWS LIKE THIS

[u/Nearby-Cockroach8655]

I’m currently interviewing for data analyst roles (mostly entry level) and every take home task i get is the same. It’s a bunch of tables with masked data, and they all want actionable insights, except i feel there’s never enough information to actually find an actionable insight. you can rarely use domain knowledge as identifiable data is anonymised and they never provide enough historical data to see if differences really matter.

Do other people feel this way or have i gotten the wrong end of the stick, if so any advice would be appreciated.

EDIT: Thanks you for the advice in the comments, I think being curious, documenting my process for the analysis I can do and explaining what data I needed to delve deeper is the way to go.

[u/plantaloca]

At this point in my career, I’m more interested in the richness of data (metadata) instead of looking at raw data only. 

To scale, analytics needs a shared understanding of what we’re measuring. If what you get is data with not descriptive names (tables/columns), lacking a data dictionary, and vague context of the goal then what they’re hiring for is someone who will conduct analysis blindly. 

You don’t want to be part of that pack. You want to be someone that can think for themselves. Offer a better structure of their data, with clear labeling, metric descriptions and inputs, and formulas along with whatever insight you seek to pursue. 

Then you might show that their poorly maintained  data needs X, Y and Z, to be defendable against scrutiny and transparent enough for traceability.