Welcome to DataParables.
DataParables exists to bridge the gap between traditional data engineering and the new AI layer. Analytics, data modelling, and machine learning are usually taught as dry, jargon-heavy subjects. I believe the fastest way to actually understand a technical system — not just memorize its vocabulary — is through a concrete metaphor: a farm-to-table kitchen for a data pipeline, a courtroom for executive versus analyst reporting, a weather forecaster for statistical uncertainty. Every series on this site picks one everyday scenario and stays with it, article after article, until the underlying technical concept genuinely clicks.
About the Author
DataParables is written and edited by Dr. Khandoker Asadul Islam, Principal Data Modeller and Solution Architect based in Brisbane, Australia. Over 21 years in software development and data science, his work has spanned enterprise data modelling and analytics leadership at RACQ, business intelligence at Sedgwick, and research and innovation projects in AI, AR, and big data at SAP Research and the SAP Innovation Centre. He holds a PhD in Information Systems Engineering from the University of Yamanashi, Japan, and is a named inventor on two U.S. patents in behavioral pattern analysis and churn prediction, alongside published research on enterprise fraud detection and data consistency verification. You can find his full professional background at asadulislam.com.
How Articles Are Made
DataParables is an AI-assisted publication, and it is worth being plain about what that means rather than leaving it to be guessed at.
Every series begins with me: I choose the topic, set the arc of the twenty articles, and pick the governing metaphor from concepts I have actually had to explain to colleagues and clients over twenty-one years of practice. The individual articles are then drafted with AI writing tools working against that outline, which is what keeps a twenty-part series consistent in voice and structure from first article to last.
What reaches this site is a filtered subset of what gets drafted. An article is published only once it is complete — both of its illustrations finished, its place in the series settled, and its claims checked against what I know of the subject. Drafts that have not been through that pass stay unpublished rather than padding out the archive. That is why the site is deliberately small relative to what sits behind it.
What I do not claim is that every sentence here has been verified line by line against a primary source. What I do commit to is this: anything reported as wrong is corrected or withdrawn promptly, and errors are fixed in place rather than quietly buried. If you spot something that is wrong or misleading, the contact page reaches me directly and I read everything that comes through it.
Editorial Standards
A few things every article on this site aims to hold to: the technical claims should be accurate, not just plausible-sounding; the metaphor should illuminate the concept rather than distort it for the sake of a tidy story; and the "AI angle" in each piece should reflect a genuine, current shift in practice, not manufactured hype. Corrections are welcome, and factual errors get fixed, not quietly buried.
Mission
My goal is to help analytics professionals, data engineers, and technical leaders build a genuinely durable understanding of both the fundamentals and the AI-era shifts reshaping how this work gets done — one parable at a time.