Data & Analytics Pod. @data-analytics-pod

Data teams drown when every question becomes a ticket. The Data & Analytics Pod is standing capacity that works a metrics backlog instead: the questions your business needs answered, turned into governed pipelines, tested models, and self-service dashboards — sprint over sprint, on the platform you own.

The pod runs the stack as well as extends it: orchestration on Airflow or Prefect, modeling in dbt, streaming through Kafka where it's needed, and AI-powered data quality holding reliability at 99.9% — so the platform earns trust while it grows, and stays ML-ready from the infrastructure up.

Metrics Backlog Delivery

A prioritized backlog of business questions delivered as governed metrics and dashboards — visible progress every sprint.

Pipeline Build & Operations

New sources integrated and existing pipelines operated: monitored, tested, and fixed before broken data reaches a dashboard.

Self-Service BI

Documented, modeled data in Tableau, Power BI, or Looker that business teams query themselves — removing the data team as a bottleneck.

ML-Ready Infrastructure

Feature stores, MLflow-style experiment tracking, and DataOps automation — so when ML workloads arrive, the platform is already fit for them.

1. Embed — The pod takes over the stack (or the output of a Data Pipeline Foundation sprint), audits pipeline health, and shapes the metrics backlog with your stakeholders.
2. Ship weekly — Metrics, models, and pipeline improvements land weekly, with data quality and freshness tracked continuously.
3. Scale — Capacity flexes with the backlog — heavier during platform build-out, leaner in steady state — without losing operational coverage.

The Data & Analytics Pod is a menu item on the Scrums.com delivery catalog, available at every plan tier. Add it to your plan backlog and your delivery team schedules it like any other item — scoped, tracked, and reported through the SEOP. See Delivery Plan Tiers.

Who decides what the pod works on?

The metrics backlog does — and you own it. Stakeholder questions go in, get prioritized with the pod, and come out as shipped metrics. It's the same plan-backlog discipline as the rest of your delivery plan.

Does the pod also keep the existing pipelines running?

Yes. Operations is part of the engagement: monitoring, incident response on broken pipelines, and quality checks run alongside new build — one pod, both jobs.

What stack does the pod work in?

Yours. Snowflake, Databricks, or cloud-native warehouses; dbt, Airflow, Prefect, Kafka; Tableau, Power BI, or Looker on top. Everything stays in your accounts, as code.