<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Data Modernization on carney.wiki</title><link>https://carney.wiki/tags/data-modernization/</link><description>Recent content in Data Modernization on carney.wiki</description><generator>Hugo -- gohugo.io</generator><language>en</language><lastBuildDate>Mon, 16 Feb 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://carney.wiki/tags/data-modernization/index.xml" rel="self" type="application/rss+xml"/><item><title>Simplifying Data Modernization in the Age of AI Agents</title><link>https://carney.wiki/blog/data-modernization/</link><pubDate>Mon, 16 Feb 2026 00:00:00 +0000</pubDate><guid>https://carney.wiki/blog/data-modernization/</guid><description>Modern data stacks were supposed to make things easier.
Faster insights. More self-service. Better dashboards. Cleaner pipelines. Fewer heroic spreadsheet rescues.
Instead, a lot of organizations ended up with complexity by default: more tools, more integration work, more handoffs, more cloud spend, and a growing gap between data work and real business outcomes.
Now AI agents are being layered on top of that mess.
That is not automatically bad. But it is risky if leaders treat AI like a shortcut around the fundamentals.</description></item></channel></rss>