This story was originally published on HackerNoon at: https://hackernoon.com/when-ab-tests-arent-possible-causal-inference-can-still-measure-marketing-impact.
Learn how to measure marketing impact without A/B tests using causal inference, Diff-in-Diff, synthetic control, and GeoLift.
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This story was written by: @radiokocmoc_l45iej08. Learn more about this writer by checking @radiokocmoc_l45iej08's about page, and for more stories, please visit hackernoon.com.

In many real‑world settings, running a randomized experiment is simply impossible. We’ll walk through Diff‑in‑Diff, Synthetic Control, and Meta’s GeoLift. We show how to prep your data, and provide ready‑to‑run code.

This story was originally published on HackerNoon at: https://hackernoon.com/why-data-quality-is-becoming-a-core-developer-experience-metric.
Bad data secretly slows development. Learn why data quality APIs are becoming core DX infrastructure in API-first systems and how they accelerate teams.
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This story was written by: @melissaindia. Learn more about this writer by checking @melissaindia's about page, and for more stories, please visit hackernoon.com.

In API-first systems, poor data quality (invalid emails, duplicate records, etc.) creates unpredictable bugs, forces defensive coding, and makes releases feel risky. This "hidden tax" consumes time and mental energy that should go to building features. The fix? Treat data quality as core infrastructure. By using real-time validation APIs at the point of ingestion, you create predictable systems, simplify business logic, and build developer confidence. This turns a vicious cycle of complexity into a virtuous cycle of velocity and better architecture. Bottom line: Investing in data quality isn't just operational hygiene—it's a direct investment in your team's ability to ship faster and with more confidence.

This story was originally published on HackerNoon at: https://hackernoon.com/why-accuracy-fails-for-uplift-models-and-what-to-use-instead.
When it comes to uplift modeling, traditional performance metrics commonly used for other machine learning tasks may fall short.
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This story was written by: @eltsefon. Learn more about this writer by checking @eltsefon's about page, and for more stories, please visit hackernoon.com.

When it comes to uplift modeling, traditional performance metrics commonly used for other machine learning tasks may fall short.

This story was originally published on HackerNoon at: https://hackernoon.com/turning-your-data-swamp-into-gold-a-developers-guide-to-nlp-on-legacy-logs.
A practical NLP pipeline for cleaning legacy maintenance logs using normalization, TF-IDF, and cosine similarity to detect fraud and improve data quality.
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This story was written by: @dippusingh. Learn more about this writer by checking @dippusingh's about page, and for more stories, please visit hackernoon.com.

The NLP Cleaning Pipeline is a tool to clean, vectorize, and analyze unstructured "free-text" logs. It uses Python 3.9+ and Scikit-Learn for vectorization and similarity metrics. The pipeline uses Unicode normalization, the Thesaurus, and case folding to remove noise.

This story was originally published on HackerNoon at: https://hackernoon.com/data-monetization-strategies-in-government-digital-platforms.
How governments monetize digital data to drive innovation, trust, transparency and economic value.
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This story was written by: @strgy. Learn more about this writer by checking @strgy's about page, and for more stories, please visit hackernoon.com.

Government data is not merely a by-product of governance, it's a strategic asset, writes Frida Ghitis. Ghitis: Government cannot be a data broker, but it should be the custodian of the value of the information it possesses.

This story was originally published on HackerNoon at: https://hackernoon.com/why-partner-data-became-my-toughest-engineering-problem.
Your partner portal isn't broken; your definitions are. How fixing "data lineage" cut deal registration time from 4.5 days to under 2.
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This story was written by: @aniruddhapratapsingh. Learn more about this writer by checking @aniruddhapratapsingh's about page, and for more stories, please visit hackernoon.com.

Partner systems slow down when data definitions drift. Real stability returns only when the model is cleaned up and workflows align around a single, consistent structure.

This story was originally published on HackerNoon at: https://hackernoon.com/pbix-is-not-going-away-but-powerbi-will-never-work-the-same-again.
PowerBI is shifting from "PBIX" to "PBIR". This article explains what actually changes, who benefits and how teams should prepare for the future without panic.
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This story was written by: @rmghosh18. Learn more about this writer by checking @rmghosh18's about page, and for more stories, please visit hackernoon.com.

"PBIX" packaged PowerBI reports into a single binary file, which worked well for individual authors but struggled at scale. "PBIR" replaces that model with a structured, project-based format that makes report changes explicit, improves collaboration and enables better governance. This shift doesn’t require immediate rewrites, but it does change how teams should think about building and managing Power BI reports long term.

This story was originally published on HackerNoon at: https://hackernoon.com/smart-fire-protection-how-ai-is-changing-preventive-maintenance-forever.
AI and IoT are transforming fire protection maintenance with predictive monitoring, fewer failures, and smarter, self-maintaining buildings.
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This story was written by: @sanya_kapoor. Learn more about this writer by checking @sanya_kapoor's about page, and for more stories, please visit hackernoon.com.

Fire protection is shifting from manual inspections to AI-powered preventative maintenance. With IoT sensors, predictive analytics, and digital tools, fire systems can now detect failures early, reduce false alarms, automate reporting, and improve compliance. Buildings are moving toward self-monitoring, self-testing fire safety systems that keep people safer while reducing operational risks and maintenance costs.