Why I Started Writing About Data
An introduction to this blog — why I'm learning in public and what I'll be writing about.
For a while, most of what I learned about data lived in scattered places — half-finished notebooks, screenshots on my desktop, and notes I could never find again. I’d figure out how to do something in SQL or Power BI, feel good about it for a day, and then forget the details a month later. This blog is my attempt to fix that: to learn in public, write things down properly, and build something I can point to.
I’m a computer science student who got pulled into the data side of things — marketing analytics, CRM systems, dashboards, and lately how AI fits into all of it. I’m not writing as an expert with ten years behind me. I’m writing as someone actively figuring it out, sharing the process honestly, mistakes included. If you’re on a similar path, I hope watching me work through these problems saves you some of the time I lost.
What you can expect
Here’s the kind of thing I’ll publish:
- Practical walkthroughs — how I built a specific dashboard, wrote a tricky query, or set up a report, step by step.
- Case studies — end-to-end projects on public or personal data, from messy input to a clear conclusion.
- Learning notes — what I picked up from a course, a certification, or a tool, distilled into something useful.
No fluff, no “ultimate guides,” no pretending to know more than I do. Just clear explanations of things I’ve actually done.
What I write about
Most of what I publish sits at the intersection of data and business decisions. A few themes you’ll see come up again and again:
- Marketing analytics — funnels, attribution, campaign measurement, and making sense of Google Analytics.
- CRM & Salesforce — pipelines, lead scoring, reporting, and keeping data clean enough to trust.
- Power BI & dashboards — data modelling, DAX, and designing reports people actually use.
- SQL — the queries, joins, and patterns I reach for when wrangling messy data.
- AI for business — practical, honest takes on where AI genuinely speeds up everyday analysis.
The through-line is always the same question: how do you turn raw data into a decision someone can act on? That’s what I’m here to figure out, one post at a time.
Let’s get into it.