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Checklist · Data Lakes

Data Lakes Launch Checklist for 2026

Use this data lakes launch checklist to stay on track as you design, build and deploy. Tasks are ordered by phase and priority so you always know what to tackle next. [launch guides](/resources/launch-guides) for additional frameworks.

9 checklist items Updated from migrated LaunchTry SEO content

Phase 01

Foundation

3 tasks
  • c1
    medium1 week

    Define goals and KPIs (Data Lakes)

    Write down what success looks like for your data lakes initiative—cost savings, query speed, user adoption rates—and define measurable targets to track progress.

  • c2
    medium1 week

    Identify target audience (Data Lakes)

    Map which teams, departments or user personas will depend on your data lakes; validate there's genuine pain worth solving before committing infrastructure budget.

  • c3
    medium1 week

    Audit current state (Data Lakes)

    Document current data flows, tooling, storage costs and pain points; flag compliance or governance gaps early so they don't derail your launch.

Phase 02

Execution

3 tasks
  • c4
    medium1 week

    Prioritize high-impact tasks (Data Lakes)

    Rank work by ROI and effort; focus first on quick wins that prove value to stakeholders and unlock budget for longer-term initiatives.

  • c5
    medium1 week

    Assign owners and deadlines (Data Lakes)

    Assign owners to each phase and set realistic deadlines; clarify decision authority so design and build phases stay unblocked.

  • c6
    high2-3 days

    Set up tracking (Data Lakes)

    Instrument ingestion, query latency, and user adoption with dashboards; establish a baseline so you can measure the lift your data lakes delivers.

Phase 03

Launch & Review

3 tasks
  • c7
    critical1 day

    Ship and verify (Data Lakes)

    Migrate a critical pilot workload through your new data lakes and validate correctness, performance and security before full rollout.

  • c8
    high2-3 days

    Measure against KPIs (Data Lakes)

    Measure adoption rates, latency improvements and cost per query against your KPIs from phase one; surface wins to secure continued buy-in.

  • c9
    critical1 day

    Iterate on results (Data Lakes)

    Gather feedback from users on query speed, schema design and support; iterate on governance and tooling based on real usage patterns.

Pro tips

  • Tackle critical items first
  • Review the checklist weekly
  • Adapt phases to your data lakes context