PROJECT MANAGEMENT

Track scope-creep trends across sprints in a warehouse

After each sprint closes, calculates the committed-vs-added work for the Asana sprint and appends the metrics to BigQuery so creep trends can be charted across sprints over time.

CategoryProject Management
Enginesim
Difficultyadvanced
Triggerschedule
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerScheduled run after sprint end date
  • ActionFetch Asana sprint tasks with timestamps and pointsAsanaAsana
  • LogicCompute committed vs added metrics and completion rate
  • LogicAssemble normalized per-sprint metrics row
  • OutputAppend sprint creep metrics to BigQueryGoogle BigQueryBigQuery

What it does

This workflow builds a longitudinal record of scope creep. At the close of each sprint it reads the Asana sprint section, measures committed work against work added after the sprint kickoff, and writes one summary row per sprint to BigQuery. Over many sprints this becomes the dataset behind a creep-trend chart, letting leadership see whether discipline is improving or eroding.

When to use it

Use it when a single sprint's number isn't enough — you want to know if scope creep is a one-off or a chronic pattern, and you want the data in a warehouse for dashboards.

How it works

  1. 1A scheduled trigger fires after the sprint's end date.
  2. 2The flow pulls all tasks from the Asana sprint project with their creation and completion timestamps.
  3. 3It computes committed task count and points, added-after-kickoff count and points, and the completion rate.
  4. 4It assembles a single normalized metrics row keyed by sprint name and end date.
  5. 5It appends that row to a BigQuery table for trend analysis and dashboards.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect AsanaTasks, projects, milestones — everywhere.
  2. 2
    Connect BigQueryDatasets, queries, schemas.
  3. 3
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  4. 4
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  5. 5
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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