@CivisAnalytics

Building a data-driven world

 Chicago
civisanalytics.com
Joined May 2013
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731Media
What if you could skip the dashboard backlog and just ask for the report? Civis built a tool that connects Claude directly to your data. Here's what one prompt looks like: [1/3]
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Claude pulled from a live dataset, built a visual report with charts and summary statistics, and had it ready to share. All in one conversation. Setup: about two minutes. [2/3]
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Watch the full walkthrough: youtu.be/5aAwX2VXWQg [3/3]

Set Up the Civis MCP Server with Claude Desktop

This walkthrough from the Civis Analytics team covers how to connec...

youtube.com
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Someone said your data pipeline is "broken" and now everyone is panicking. Let's start from the beginning. 🧵
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Misconception: broken pipeline means the data is gone. Reality: it means the data stopped moving. The source still has it. The pipeline just needs to be fixed or restarted.
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A good pipeline is invisible when it works. You only notice it when it breaks.
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Every org knows about technical data debt: messy pipelines, inconsistent fields, outdated exports. There is a second type that rarely gets tracked: trust debt. 🧵
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The repair for technical debt: fix the pipeline. The repair for trust debt: demonstrate reliability consistently, over time. No shortcut. You have to earn it back.
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Right now, orgs are asking if their data is clean enough for AI. That is the technical question. The harder one: does your team actually believe the data? If not, AI output gets the same skeptical treatment as everything else.
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Someone just said “we need to fix the data model,” and everyone nodded like that was a normal sentence. Let’s translate. 🧵
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Misconception: a data model is just technical plumbing. Reality: it shapes what questions your organization can answer.
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A dashboard built on a messy model is just a very confident-looking mess. A good data model makes data easier to find, easier to explain, and much harder to accidentally misuse.
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