Data Project Template
A board that follows a data source from "we should pull this" to "it's running in production," with quality problems tracked apart from ordinary bugs.
Data quality issues get their own card label so they don't disappear into the general bug list.
Why most data project boards stop matching the pipeline
Data work has a shape that a generic task board handles badly. A pipeline isn't finished when the code merges. It's finished when the numbers coming out of it have been checked, and it can quietly break again weeks later without anyone touching the code. Three habits sink most boards here.
Everything looks like a normal task until it's already in production. "Build the refund pipeline" reads the same as any other engineering ticket, so it gets closed the moment the pipeline runs, before anyone has checked whether what it's producing is actually correct.
A validation step exists in someone's head, not on the board. Row counts get eyeballed once, a dashboard looks roughly right, and the item moves to done. There's no column that forces the check to happen and leaves a record that it did.
Data quality problems get filed as ordinary bugs. A null spike in a column or a source that silently stopped updating gets the same card type as a broken button, and it competes for attention with unrelated engineering work instead of getting looked at as what it actually is: a problem with the data itself, not the code.
The consequence shows up downstream, usually in a meeting where someone asks why a number in a report looks wrong, and nobody can say when it started looking wrong or which of the last five pipeline changes might be responsible. That's what happens when validation lives in someone's head instead of on the board: there's no checkpoint to point back to.
This template gives a data source its own path across the board, with a validation step that's a real column rather than a step someone remembers, and a way to flag a quality problem as distinct from a regular bug.
The structure, and why each part is there
A new data source (a vendor file, an API, an internal export) starts here before any pipeline work begins. It's a placeholder for "we know we need this," which is different from "we're building it."
Building a pipeline is where scope quietly expands: one more transformation, one more edge case. A low WIP limit here means a source gets built and moved on rather than endlessly refined before anyone checks it against real numbers. It's the same principle that makes ShipSprint's WIP limits useful anywhere else on a board, just applied to a spot where "just one more tweak" is especially tempting.
Row counts, spot checks, a comparison against the source: whatever validation means for that pipeline, it happens here and the card doesn't move until it's done. A pipeline that runs is not the same as a pipeline that's correct.
A null spike, a source that stopped updating, a schema that changed upstream: these get tagged separately from ordinary bugs so they surface as what they are, a problem with the data, worth triaging on its own terms rather than mixed in with feature bugs.
A pipeline landing here isn't the end of its card's life. If a data quality issue turns up against it later, that's a new card linked back to it, not a reopened argument about whether the original work was done properly.
Where the pipeline is code, a branch and pull request tied to the card move it through the board as the work merges, and cycle-time reporting shows how long a source actually takes from identified to production, often longer than the build step alone suggests.
A request for a new data source, from an analyst, a stakeholder, another team, lands in one place first, rather than as a direct ask to whoever built the last pipeline. It gets decided on, not just started.
How to use it
- 01Start a workspace. It opens with a sample project already on the board so the columns make sense before you add a real source. Free for up to five people, permanently.
- 02Decide what "validated" means before the first pipeline gets there. Row count parity, a manual spot check, a comparison against a known-good export: write down the check, not just the column name.
- 03Keep Pipeline built capped. A source that's 90% built and endlessly refined is worse than one that's built, validated, and shipped with a known gap noted on the card.
- 04Tag data quality issues distinctly from the start. Retrofitting the distinction after a backlog fills with mislabeled bugs is much harder than starting with the separation.
- 05Link production cards to any quality issue that follows. A source that broke three weeks after shipping isn't a new unrelated bug. Keeping it attached to the original card keeps the history intact.
The board won't tell you a pipeline is producing wrong numbers. That still takes a person checking. What it does is give that check somewhere to happen and something to point to afterward, instead of relying on whoever remembers to look.
- A pipeline that runs and a pipeline that's correct are different milestones, so give validation its own column
- Data quality issues buried in a general bug list get triaged like feature bugs, which is the wrong lens
- "In production" isn't the end of a source's story, so link what breaks later back to where it started
Common questions
No. This is a task board for coordinating the work of building and validating a pipeline, not a data catalog or an orchestration platform. It sits alongside whatever runs your pipelines, tracking the human work (building, checking, fixing) rather than the jobs themselves.
A bug is usually a defect in code. A data quality issue is a problem with what the pipeline is producing: nulls where there shouldn't be, a count that doesn't reconcile, a source that stopped sending updates, none of which may involve a code change to fix at all. Tagging it separately means it gets triaged by someone who understands the data, not routed into the general engineering queue by default.
Delivery forecasts are calculated from your team's measured velocity as sprints complete, the same as for any other work on the board. There's no separate estimation model for data work, and no promise attached to it either, just a running number based on how this team has actually performed. It gets more useful once a few pipelines have gone through Source identified to In production and there's real cycle-time history to draw on.
Small recurring changes still go through Pipeline built and Validated. The column names don't change based on how big the change is. It keeps the same discipline applied whether it's the first version of a pipeline or the tenth adjustment to one already in production.
Yes. An analyst or a stakeholder can request a source through triage and follow it through validation without touching any code, and the wiki page for a source can hold the plain-language explanation of what it's for, separate from the pipeline's technical details.
Yes. All templates are included on the Free plan, which covers up to five users and two projects and doesn't expire. See pricing for larger teams.
Related pages
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