Analysis
Extracting insight from clean data — from SQL aggregation and visualization to KPI design and ML experiments.
15 articles
-
Physics Has Models. Machine Learning Has Black Boxes.
A physics model can be worked out from first principles — derived, interrogated, and defended line by line. A machine learning model can only be trusted. The difference is not academic. It is the difference between analytics you can stand behind and analytics that runs on "trust me."
🌳 High Hanging Fruit -
How to Start Maintaining an Ontology as a Non-Technical Business Owner
An ontology is just the written-down meaning of the words your business runs on — what a customer is, when a sale counts, what "active" means. You do not need to code to own it. You need to decide, write it down, and keep it current. Here is how to start.
🍎 Low Hanging Fruit -
Think With Data. Rest to Create.
Analytical rigor gets you to the right questions. Rest is what gets you to the right answers. The data professional who never steps back is not working harder — they are thinking shallower.
🍎 Low Hanging Fruit -
Ontology vs Semantic Layer: What Each One Actually Is
An ontology defines what your business means. A semantic layer enforces those definitions inside your data stack. They are not synonyms, and confusing them produces dashboards that look authoritative but disagree with each other.
🌳 High Hanging Fruit -
Analytics Paints the Picture. It Does Not Prove the Story.
The job of analytics is to render reality clearly enough that the next move is obvious. Not to confirm the hunch in the room. Hyper-focusing from the start hides the elephant — and the elephant is usually the finding.
🍎 Low Hanging Fruit -
Start With Data Stakeholders Already Trust
Building analytics on well-known public datasets earns stakeholder credibility before you write a single custom pipeline. Once the framework proves itself on familiar ground, automation and analytics compound the value without the credibility risk.
🍎 Low Hanging Fruit -
The Silent Death of Orphan Data Pipelines
A data product loses value the moment active engagement stops — not because the pipeline breaks, but because data drifts, definitions shift, and timeliness erodes with no one in the loop to notice. The pipeline is the easy part. Keeping the output aligned with business reality is the ongoing obligation most organizations never plan for.
🌳 High Hanging Fruit -
Trust and Time Are the Real Currencies of Data ROI
Data analytics cannot return on investment if the underlying data is wrong. Incorrect data wastes time, erodes trust, and kills the credibility needed to justify the work. Trust and time are finite resources — protecting them is where ROI starts.
🍎 Low Hanging Fruit -
Dashboards Are Waiting Rooms: Interconnectivity Is the Endgame
Every dashboard is a hand-off to a human being. That hand-off costs time, introduces delay, and scales poorly. The mature data organization does not build more dashboards — it builds fewer, and automates everything the dashboard used to trigger.
🌳 High Hanging Fruit -
Don't Build Analytical Castles on Sand
Technical debt is just as real in analytics as it is in software. Brittle queries, undocumented assumptions, and untested transformations compound silently until something breaks.
🌳 High Hanging Fruit -
The Gas Gauge Is the Hardest Chart to Build
A gas gauge looks like the simplest visualization in the room. It is actually proof that you fully understand your metric — thresholds, context, and all. If you cannot build one, you do not know your KPI yet.
🍎 Low Hanging Fruit -
Building a Stock Prediction Classifier with scikit-learn
How to train an SVM classifier on Reddit sentiment, Google Trends, and price data to predict short-term NYSE stock moves — plus what the model reveals about signal quality.
🌳 High Hanging Fruit -
DuckDB for Financial Data Analysis
Use DuckDB to run fast analytical SQL over stock price data, compute rolling indicators, and build a local analytics layer — no server required.
🌳 High Hanging Fruit -
Visualizing Data with Python
Turn raw numbers into charts that reveal patterns — using matplotlib, seaborn, and plotly for static and interactive visualizations.
🍎 Low Hanging Fruit -
Low-Hanging Data Sources for Stock Market Prediction
A curated list of freely accessible data sources and their supporting research literature for augmenting stock prediction models — starting with the easiest wins.
🌳 High Hanging Fruit