Civis Analytics

Your CRM was built to store data, not to answer the questions fundraising and marketing teams ask every day. On July 14, we're going live with The NonProfit Times for a practical, no-jargon hour on how Claude is changing fundraising analytics: getting answers in plain language, keeping data governance tight, and building dashboards without waiting on a data team. Free to attend. Can't make it live? Register and we'll send you the recording. Save your seat → https://lnkd.in/eSKu87t8 #Nonprofits #Fundraising #AI #NonprofitTech

Today we're celebrating Whit Huntley's first work anniversary at Civis. 🎉 Thank you for your first year, Whit. We're lucky to have you. Here's to what's next. #WorkAnniversary #LifeAtCivis

Today we're celebrating Gabby Skifstad's 9th work anniversary at Civis. 🎉 Thank you for nine years, Gabby. We're lucky to have you. Here's to what's next. #WorkAnniversary #LifeAtCivis

Happy Pride Month from all of us at Civis Analytics! We're proud to partner with the Human Rights Campaign, America's largest civil rights organizations fighting for LGBTQ+ equality. Our data supports their important work moving hearts, minds, and policies that help all Americans live freely and safely. Data-driven advocacy is a force multiplier for equality. We're grateful to play a role in that mission. Wishing everyone a joyful and meaningful Pride Month!

If your team is running the same data pipeline by hand every week, there's a better way. This Thursday, Civis is hosting a live session on Workflows and Template Scripts: the tools in Civis Platform for automating recurring data jobs. We'll show how to schedule, chain, and monitor pipelines so they run without manual intervention. June 18 · 1 PM ET Register: https://lnkd.in/eK9aRjYK

What if you could ask your data a simple question and get back a published report? Civis built a tool that connects Claude directly to your organization's data. Setup takes about two minutes. Our team gave Claude a single prompt. It pulled from a live dataset, built a visual report with charts and summary statistics, and had it ready to share with the team. We asked for a version styled to our brand. Done in the same conversation. No coding. No waiting on the data team. No back and forth. Watch the full walkthrough: https://lnkd.in/eHzsUceF #AI #DataAnalytics #DataScience #Analytics #CivisAnalytics

Happy Pride Month from all of us at Civis Analytics! 🌈 Civis works with nonprofits, advocacy organizations, and mission-driven campaigns across the progressive ecosystem. We build the data tools and provide the expertise that help these teams reach the right people, at the right time, with messages that actually move the needle. That kind of precision matters especially when the work involves protecting rights, building coalitions, and winning on issues that shape people's lives. We're proud to support organizations doing important work for LGBTQ+ communities, and we're committed to being a resource for that work all year, not just in June. Wishing everyone a joyful and meaningful Pride Month.🏳️🌈 #PrideMonth #Pride2026 #LGBTQ #DataForGood

This June, three Civis colleagues are marking anniversaries. Darien Weems, Software Engineer: 2 years Owen Harrington-Woodard, Senior Solutions Architect: 5 years Caitlyn Knisley, CPA, Controller: 11 years That's 18 combined years of work that shapes what we build, how we operate, and who we are as a team. Grateful for each of you. Congratulations! #civis #dataforgood

Most AI vendors will tell you their product works "out of the box." What they mean is: it works out of the box if your data is clean, connected, documented, and structured correctly. For most organizations, that's not the starting point. It's the finish line. The pitch goes like this: plug in our AI, get insights immediately. The reality: you spend the first six months untangling a mess of spreadsheets, legacy databases, and fields named things like "column_7_FINAL_v3." We've seen this at organizations of every size. The tool isn't the problem. The assumption is. Real data readiness means: - Your data lives in one place (or at least talks to itself) - Someone owns the definitions (what does "active user" actually mean at your org?) - You can trust the numbers before you act on them AI can accelerate a lot of things. It cannot fix a data foundation that isn't there. The organizations getting real value from AI aren't the ones who bought the flashiest tool. They're the ones who invested in the infrastructure first.

Someone said your data pipeline is "broken" and now everyone is panicking. Let's start from the beginning. A data pipeline is the system that moves data from where it lives to where you need it. Source systems collect it. The pipeline picks it up, cleans it, transforms it into a useful shape, and delivers it somewhere: a data warehouse, a dashboard, a spreadsheet, a model. Repeat, usually on a schedule. A simple example: your CRM has donor records. Your fundraising platform has transaction records. A data pipeline pulls both, aligns them, and loads a combined view into your warehouse so your team can actually run reports without spending Tuesday manually merging two CSVs. That last part is the point. Pipelines replace the manual work that used to live in someone's Tuesday morning. Three things people get wrong about data pipelines: ❌ Misconception: A pipeline is a one-time setup. ✅ Reality: Pipelines run continuously or on a schedule. If a source system changes its format, or the connection breaks, or the vendor updates their API, the pipeline needs maintenance. ❌ Misconception: A broken pipeline means the data is gone. ✅ Reality: Usually it means the data stopped moving. The source still has it. The pipeline just needs to be restarted or fixed. ❌ Misconception: You only need a pipeline when you have a lot of data. ✅ Reality: You need a pipeline as soon as you have more than one system and a recurring need to combine them. That is most organizations. The reason data teams spend so much time on pipelines is not because the concept is complicated. It is because every source system is slightly different, every stakeholder wants data in a slightly different shape, and the whole thing has to keep running even when something upstream changes without warning. A good pipeline is invisible when it works. You only notice it when it breaks. What data concept should we explain next? Drop it in the comments 👇 #DataEngineering #DataLiteracy #Analytics #DataStrategy #NonprofitTech