UNIVERSALData Platform Dashboard

Data Quality Dashboard Wireframe: Freshness and Rules

Pipeline freshness, load failures, and rule-level exceptions. The freshness table lists downstream reports, which is the column that makes the page usable during an incident.

Data Pipeline HealthAll domainstoPipelines on schedule142 / 148-2.7%Failed loads (24h)6+50.0%Oldest stale dataset31 hours+72.2%Rows loaded284M+4.1%Load volume and failuresFailures by pipelineFreshness by dataset
Page 1 of 2Freshness & Failures. 22 elements across 2 pages, every one editable once it is open.

What's Included

Page 1

Freshness & Failures

KPI card x4 (Pipelines on schedule, Failed loads (24h), Oldest stale dataset, Rows loaded)
Filter (Domain)
Date filter (Window)
Combo chart (Load volume and failures)
Bar chart (Failures by pipeline)
Table (Freshness by dataset)
Page 2

Quality Rules

Quality Rules and ExceptionsAlltoRules passing96.8%-0.9%Blocking failures3+200.0%Null rate on key fields0.42%+0.1%Duplicate keys118-31.4%Rule pass rate trendFailures by rule typeOpen exceptions
KPI card x4 (Rules passing, Blocking failures, Null rate on key fields, Duplicate keys)
Filter (Severity)
Date filter (Window)
Line chart (Rule pass rate trend)
Bar chart (Failures by rule type)
Table (Open exceptions)

Annotated, Not Just Drawn

6 elements carry a spec note: the grain of the chart, where the number comes from, what the filter applies to, what happens on click. They open in the editor and travel into the exported build spec, so whoever builds it in your BI tool reads the same thing the stakeholder signed off. Three of this template's:

  • Open exceptions

    Rule, dataset, severity, rows affected, first seen, owner, status. Rows affected as both a count and a share. 500 rows means nothing without the denominator.

  • Freshness by dataset

    Dataset, owner, expected cadence, last load, age, SLA status, downstream reports. The last column is what makes this page usable during an incident.

  • Pipelines on schedule

    On schedule means the last successful load finished inside its own SLA. Each pipeline declares its own, so this is not one global threshold.

Who This Template Is For

Analytics engineers and data platform teams who get asked 'can I trust this number' and want a page that answers it without a Slack thread.

How to Use This Template

  1. 1

    Open the template in datawirefra.me by clicking "Use This Template" above.

  2. 2

    Customize the layout — drag, resize, or swap any component to match your specific requirements.

  3. 3

    Label each component with your actual metric names (e.g., replace "KPI Card" with "Revenue MTD").

  4. 4

    Share via live URL or export to PNG/PDF to collect stakeholder feedback before building in Universal.

  5. 5

    Once approved, use the wireframe as your build spec in Universal.

Frequently Asked Questions

What is the Data Quality Dashboard Wireframe: Freshness and Rules?+

Pipeline freshness, load failures, and rule-level exceptions. The freshness table lists downstream reports, which is the column that makes the page usable during an incident.

Is this template free?+

Yes. All templates on datawirefra.me are free to use. Open the template, customize it, and export or share via live URL — no account required for sharing.

Can I use this for my Universal dashboard?+

Absolutely. This template is designed as a planning and alignment tool before you build in Universal. Wireframe first, get sign-off from stakeholders, then build with confidence.

Can I add or remove components?+

Yes. Every component in this template is fully customizable. You can add new chart types, remove what you don't need, resize elements, and rearrange the layout.

data quality dashboardpipeline monitoringdata freshnessobservabilitywireframe template

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