Hack the Domo Stack: Kick Off the Year With a Data Cleaning Checklist

Every December, we run a small internal challenge at Domo called the Twelve Days of Data.

The tradition is simple. For twelve days, we focus on cleaning up our Domo instances. One cleanup task per day. A little progress at a time. The idea is to wrap up the year with instances that feels organized, intentional, and ready for whatever comes next.

We have a themed raffle each day for those who completed the previous day’s task. It’s not much, but it adds just enough fun to the process. For 2025, we gave LEGO Formula 1 cars and for 2024, retro board games.  

What started as best practice has turned into something people genuinely enjoy. It gives us a shared moment to reflect on what we built throughout the year, clean up what we no longer need, and head into 2026 with a clean slate. It’s a practice that fosters a thriving data culture, and we wanted to share so others can do the same.

Why we do this every year

Over the course of a year, a lot happens in Domo. New data sets get created. Cards evolve or fall by the wayside as questions change. Workflows solve problems that were urgent at the time and then quietly keep running.

All that growth is a good sign. But it also means clutter slowly builds if no one ever pauses to review.

Our Twelve Days of Data challenge gives us a natural check-in. It spreads cleanup across a reasonable timeline, highlights the most impactful tasks, and changes maintenance from a postponed, dreadful chore into a fun routine.

By the time January arrives, each instance feels quicker to navigate, more trustworthy, and easier to build on.

How the Twelve Days of Data are structured

Each day has a single focus area and a short checklist to guide what to review. The cleanup tasks you need most might vary slightly. Here’s how we break it down and why each day matters.

Day 1: Reviewing data set schedules

We start with data set schedules because they quietly drive a lot of downstream activity. We review scheduled refreshes and decide whether they still make sense. Connector data sets should be configured to run only when new data is available. Adjusting schedules helps reduce unnecessary processing while keeping data current where it matters most.

Day 2: Cleaning up dataflow schedules and triggers

Next, we look at dataflows.

Over time, dataflows often pick up extra schedules or triggers that are not needed. This day is about simplifying. A great start is dataflows running more than once a day. That’ll help you identify data set triggers that can be removed or have a condition added.  

Day 3: Fixing broken and removing unused cards

Cards are easy to create and even easier to forget.

On Day 3, we delete cards that are clearly unused and fix cards that are broken but still relevant. This  improves dashboard accuracy and makes finding what you need easier.

Day 4: Cleaning up data sets

Here,  we revisit the data sets themselves.

We delete unused data sets, fix broken ones, and implement naming conventions. Clear names and descriptions go a long way for searching and understanding.

Day 5: Pruning unused dataflows

After reviewing schedules, we come back to dataflows again.

Here, the focus is on removing flows that no longer support active work and cleaning up anything that has been left behind. Deleting datasets first means you’ll likely run into some dataflows with no inputs or no ouputs that make for easy cleanup. It’s also a great time to cleanup dataflow logic or upgrade from MySQL to Magic ETL V2.

Day 6: Reviewing workflows and Jupyter workspaces

Automation is powerful, and it benefits from regular check-ins.

On day 6, we review workflows and Jupyter workspaces to confirm they are still active, still owned, and still doing what we expect. Anything unused gets deleted, and anything important gets a quick documentation pass or logic update.

Day 7: Cleaning up pages

Pages tend to grow as teams and priorities change.

Day 7 is about deleting pages that are no longer relevant and consolidating where possible. This helps reduce clutter and makes navigation easier, especially for new users. It’s also a great time to convert dashboards to App Studio apps, or delete duplicate pages that have already been remade in App Sudio.

Day 8: Tidying up Spp studio apps

App Studio apps are the new dashboards.  

We separate studio apps from dashboards to match the product. You probably have more than a few apps to delete from testing and learning the new interface. Plus, taking the time to clean the flow of a studio app and its pages provides meaningful clarity to your data presentation.  

Day 9: Reviewing AppDB collections

AppDB collections quietly accumulate in the background.

On Day 9, we identify collections that are no longer tied to active apps or processes and clean them up.  Not everyone has AppDB collections, but we find it to go a long way for heavy users of pro-code apps.

Day 10: Reassessing alerts and scheduled reports

Alerts and scheduled reports should be helpful, not produce noise.

This day is spent deleting alerts that no longer provide value and reports that no one reads anymore or are expired. The result is fewer interruptions and notifications that people actually pay attention to.  

Day 11: Cleaning up Beast Modes and variables

This is one of the more technical days.

We delete unused or broken Beast Modes and variables and review logic to make sure it reflects how metrics should be calculated today. It’s a great way to catch subtle issues and simplify the card building experience before the new year begins.

Day 12: Wrapping up and looking back

The final day is a quick sweep.

We clean up our overview pages, revisit days we didn’t have enough time for, and take a moment to look back at how the instance changed over the year. It’s a satisfying way to close things out and feel ready for January.

Want to try this with your own team?

The Twelve Days of Data is very Domo culture-coded, but its principles and outcomes are applicable to all Domo users. It’s helped us stay intentional about keeping our Domo instance in good shape year after year.

If you are looking for a simple way to drive cleanup behaviors across your users, this might be a good place to start. Feel free to adjust the days, change the order, or focus on the areas that matter more in your environment.

And if you want to see how other teams approach cleanup and organization, the Domo Community Forums is full of ideas and variations from admins doing the same kind of work.

Happy cleaning!