My Adventures with the “Lake Score”
So, I decided to tackle this thing I called the “lake score.” Sounds fancy, right? Well, the idea was pretty simple, or so I thought. We had this massive data lake, tons of data pouring in, and honestly, nobody really knew what was good, what was junk, or what was just… there. My bright idea? Let’s make a score for each dataset! Like a health check, you know?
I figured, how hard could it be? My first thought was to check a few basic things. Stuff like:
- Is the data fresh? Or is it ancient history?
- Is it complete? Or full of holes like Swiss cheese?
- Does it even look right? You know, are the dates actual dates?
Easy peasy, lemon squeezy. Or so I told myself. I planned to whip up some scripts, maybe Python, run them, and boom – scores for everyone!
Then I actually dived into our “lake.” Boy, was that an eye-opener. It wasn’t so much a pristine lake as it was a murky swamp. Data from everywhere, in every imaginable format, and sometimes in formats I couldn’t even imagine. Some stuff was beautifully curated, sure, but a lot of it? Let’s just say “wild” is an understatement. My simple checks started looking not so simple anymore.
I started by trying to define what “good” even meant. I sat down and tried to write down some rules. For freshness, I thought, okay, data updated daily gets a high score. Weekly, a bit lower. Monthly? Hmm. But then one team piped up, “Our data only updates quarterly, and that’s perfectly fine for us!” Right. So “good” wasn’t a one-size-fits-all thing. That was fun to discover.
I pushed on. I wrote some basic scripts. First, I tackled completeness. Counted nulls, empty strings. That was okay for some datasets. For others, a “null” actually meant something important. My scripts were turning red, flagging things that were supposedly “bad” but were actually just… the way the data was. It felt like I was trying to fit a square peg in a round hole, over and over again.
Then came the accuracy part. How do you even automate checking if data is “accurate” without having some other “perfect” source to compare it against? Which, if we had that, why would we need the score for the first dataset? My head started to hurt. I spent a lot of time just talking to different teams, trying to understand their data and what they considered “quality.” It was less about coding and more about being a data therapist.
The tools out there didn’t help much either, to be honest. Some were super expensive and complicated, like needing a PhD to just set them up. Others were too basic, doing less than my cobbled-together scripts. It felt like there was this huge gap. Everyone talks about data quality, but actually doing it in a practical way for a messy, real-world data lake? That’s another story.
In the end, what did this “lake score” become? Well, it wasn’t the magic number I first dreamed of. It was more like a set of guidelines, a conversation starter. We ended up with a dashboard, sure, but the real value came from the discussions we had while trying to define the scores. People actually started talking about their data, its problems, and how to fix them. My scripts helped flag obvious issues, like a dataset suddenly shrinking or not updating for weeks.
So, the “lake score” wasn’t a total failure. But it wasn’t the clean, automated solution I naively envisioned. It was messy, involved a lot of human effort, and a lot of back-and-forth. It kind of showed me that with these giant data lakes, the tech is one thing, but understanding what’s actually in them and if it’s any good? That’s a whole different beast. You can’t just throw data into a pile and expect magic to happen. Someone’s gotta sift through it, and that someone often ends up being you, armed with a few hopeful scripts and a lot of patience.
