Discussions

Okay so I’m gonna be real this semester I got stuck (well, I chose, technically) doing this capstone project on data observability. Thought it’d be chill and interesting, and honestly, it kind of is. I love getting into the weeds with tools like dbt and setting up tests, tracking lineage, all that nerdy stuff. But now we’re at the part where we have to present the whole thing to a panel including a couple people who aren’t super technical and I’m freaking out a little.

Like how do you explain something like “data freshness” or “pipeline monitoring” to someone who doesn’t spend half their life in SQL or Grafana?

Our use case is about a retail brand that kept making bad inventory decisions because of bad data stuff like stale customer purchase info, duplicated orders, and weird schema changes no one noticed until the reports were off by like 30%. We’re showing how better observability would’ve caught all that early. Makes sense in my head. But when I try to write it out in plain language, it either sounds super vague or like I’m just throwing buzzwords around.

Also random side note but maybe someone else can relate I have got three other assignments due this week and my brain is completely fried. Been tempted more than once to Google assignment writing services, just for like formatting help or editing, but I’m paranoid about running into shady stuff or plagiarism issues. Not worth it, I guess, but the stress is real.

Anyway, back to the actual project: is there a good way to break down the value of data observability that doesn’t make people’s eyes glaze over? I was thinking of doing a “before vs. after” kind of slide deck, like:

Before: analytics team finds out reports are wrong after execs already made decisions → chaos.

After: alerts fire as soon as schema breaks or data freshness drops → everyone actually trusts the dashboards.

Is that too simple? Or is that exactly the level I should be aiming for with this kind of audience?

Also curious if anyone’s worked with teams where the devs get it but the business folks are just like “why are we spending money on this again?” How do you get non-data people to care?

Would love any ideas, examples, or even just someone to validate that this stuff is hard to explain but super important. Appreciate you if you made it this far

Let me know what’s worked for you storytelling, visuals, analogies, whatever. I’ll take all the help I can get at this point.

Thanks in advance