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Data Health answers one question: Is my data ready for analysis? Before diving into revenue trends or cohort performance, you need to know whether the underlying data is complete and current. Data Health surfaces pipeline issues proactively so you can trust your answers, avoid surprises, and scope your analyses appropriately.

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Why it matters

Analytics are only as reliable as the data behind them.
  • Stale data misleads: A “last 7 days” analysis is useless if the most recent 3 days haven’t synced
  • Proactive visibility prevents bad decisions: Catching a sync issue before a board meeting is better than discovering it afterward
  • Scoping saves time: Knowing which domains are ready helps you focus on answerable questions
Data Health tells you whether the pipeline is working. Attribution Health tells you whether your tracking is capturing marketing touchpoints. Both matter — a table can be perfectly fresh but still show 40% (direct) / (none) if UTM tracking isn’t set up properly.

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What we check

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When data updates

Key tables refresh daily. Our SLA guarantees fresh data through the previous day based on your reporting timezone.
  • Through yesterday: Complete and reliable. This is what we guarantee.
  • Today’s data: Incomplete. Do not use current-day data for analysis.
Real-time isn’t the goal. We optimize for accuracy over speed — ensuring data is correctly transformed, deduplicated, and enriched before it reaches your dashboard or warehouse.

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Platform-specific timing

Some platforms have longer sync windows due to API limitations:

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Why 14 days?

Key tables — orders, customers, sessions, and ad spend — should have fresh data every day. That is the normal operating state for an active e-commerce business. The 14-day threshold exists mainly for secondary tables that may not see daily activity:
  • A new subscription program might not have orders every day yet
  • Refunds tables depend on actual refund volume
  • Some niche integrations only fire on specific events
We flag tables at 14+ days because it is long enough to account for legitimate low-volume periods while still surfacing tables worth investigating.
If a core table such as orders, customers, or ad performance is stale, that is almost always a pipeline issue. If a secondary table is stale, check whether you would expect activity before treating it as a broken sync.

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Common scenarios

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What to do if data is degraded

1

Check specific tables

Identify which tables are stale. Is it one platform or multiple?
2

Scope your analysis

Avoid date ranges that depend on stale data. If orders haven’t synced since Jan 15, don’t analyze Jan 16–20.
3

Check Attribution Health

If data is fresh but results look wrong (e.g., high (direct) / (none)), the issue may be tracking, not pipeline.
4

Escalate if persistent

If staleness persists beyond 24–48 hours, reach out to your SourceMedium team — there may be an integration issue requiring admin attention. See When to contact SourceMedium for what to include.

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Before reconciling a number

If a dashboard value or warehouse query looks different from another system, check Data Health first. Freshness is only one cause; for the full cross-tool checklist, see Why would external reports not match the SourceMedium dashboard?.
Data Health is a readiness check, not a full reconciliation. It can tell you whether data is fresh and available. It cannot prove that two reports use the same metric definition, date basis, or filters.

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When to contact SourceMedium

Reach out to your SourceMedium team when:
  • A core table such as orders, customers, or ad performance remains stale after the expected refresh window.
  • A connected platform is empty when you expect recent activity.
  • Multiple core domains are stale at the same time.
  • Data Health looks healthy, but a discrepancy remains after checking metric definitions, filters, and date basis.
  • You need to know whether a freshness issue affects one table, one platform, one tenant, or all data.
Include the platform or table, date range, expected value, observed SourceMedium value, and the external report or query you are comparing against. That gives support enough context to investigate without a long back-and-forth.

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Example questions

You can ask about data health in natural language:
  • “How is my data health?”
  • “Can I trust my last 7 days of data?”
  • “Which tables are fresh?”
  • “What data do I have available?”
  • “Are my tables up to date?”
  • “When was my orders data last updated?”
Use the AI Analyst in Slack to run these checks. Just ask “How is my data health?” and get a real-time assessment of your table freshness and availability.

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Data Health vs Attribution Health

Check Data Health first. If data is stale, that explains why numbers look off. If data is fresh but attribution seems wrong, then check Attribution Health.

Attribution Health

Diagnose and improve tracking coverage for marketing attribution.

Data Freshness

Details on refresh schedules and platform-specific timing.

Why (direct) / (none) happens

Common causes of missing attribution and how to fix them.

Data Architecture

How SourceMedium structures and transforms your data.