Hi, I’m Jose
I write about data and practical AI the way I wish someone had explained it to me on the job: plain language, real examples, zero conference-stage fog.
For more than twenty years I’ve worked in analytics: Business Intelligence, data science, data architecture, and analytics management. Analytics Made Simple is where I put that time to use for people who need to use data, not just admire it.
What I’ve actually done
I didn’t learn analytics from a single course. I learned it in the places most of us learn it: messy warehouses, half-finished dashboards, stakeholder meetings that went sideways, and the quiet work of making a number trustworthy enough to act on.
Across more than two decades, that work has looked like this:
- Business Intelligence. Building reports and models people can open on Monday without a decoder ring. Defining metrics so two teams stop arguing about whose revenue is “right.”
- Data science. Turning questions into experiments and models when a simple rollup isn’t enough, and being honest when a spreadsheet was the better tool.
- Data architecture. Shaping how data moves, lands, and gets documented so analysis doesn’t collapse every time a column changes name.
- Analytics management. Hiring, prioritizing, and protecting focus. Helping teams ship decision-ready work instead of infinite dashboard polish.
I’ve sat on both sides of the table: hands on the keyboard and in the room where leadership asks “so what?” That dual view is the whole point of this site. Tools change. The hard part stays human: clear questions, honest grain, and explanations that survive contact with real work.
Why Analytics Made Simple exists
I kept watching sharp people get stuck. Not because they lacked intelligence. Because data and AI get taught in jargon first and usefulness second. Someone smart asks a normal question and gets a wall of acronyms back. That’s a teaching problem, not a people problem.
So I write the way I brief a colleague who is busy and smart:
- Define the term the first time, then use it.
- Show a workplace example before the theory gets heavy.
- Prefer SQL you can run, briefs you can fill, and checklists you can steal over abstract purity.
- Cover practical AI without hype or fear-mongering. What it is good at, where it fails, and how to check the work.
If you want a path, start at Learn. If you want vocabulary without a textbook, open Key Terms. If you want the full library, use All articles.
How I think about the work
A few beliefs show up in almost everything I write:
- Clarity beats cleverness. A boring metric everyone trusts beats a clever one nobody can explain twice.
- Grain is destiny. Wrong level of detail creates beautiful charts that answer the wrong question.
- Good enough is a skill. Perfect data is late. Decision-ready data, with its limits stated, is useful.
- AI is a power tool, not a source of record. Great for drafts and exploration. Dangerous when you skip verification.
- Writing is part of analytics. If the brief is fuzzy, the warehouse will not save you.
When people reach out
Most messages are simple: a topic request, a question about an article, or “did I understand this right?” Those are always welcome.
Sometimes the note is heavier. A team is stuck choosing a metric definition. Leadership wants a one-page brief before a big build. Someone needs a second set of eyes on an analytics plan, a dashboard story, or how AI fits (or does not fit) into a real workflow. I enjoy that kind of problem. It’s the same craft as the writing, just applied to a specific situation.
If you’re in that spot, use the contact form and tell me what you’re trying to decide. A few concrete sentences beat a long pitch. I’ll reply when I can and we’ll figure out whether a conversation is useful.
Elsewhere
Find me on LinkedIn and GitHub. I also put a little humor on the walls: data jokes that fit in a real wardrobe live on the Etsy store.
Thanks for reading. Whether you only ever use the free guides or you write because something at work is hard right now, I’m glad you’re here.