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.What we check
| Dimension | What it means |
|---|---|
| Freshness | Has the table been updated recently? We flag tables that haven’t refreshed in 14+ days. |
| Availability | Does the table contain data, or is it empty? |
| Domain Readiness | Which analytical areas are usable — orders, customers, ads, attribution, etc.? |
When data updates
Most tables refresh on a 24-hour incremental schedule. Our SLA guarantees fresh data through yesterday based on your reporting timezone. This means:- Yesterday’s data and earlier is stable and complete
- Today’s data is not guaranteed and will be incomplete — avoid using it 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.
Platform-specific timing
Some platforms have longer sync windows due to API limitations:| Platform | Typical Lag |
|---|---|
| Shopify, Klaviyo, Meta, Google Ads | 24 hours |
| Amazon Seller/Vendor Central, Amazon Ads | 24–72 hours (API rate limits) |
| GA4 | 24–48 hours |
Why 14 days?
We use a 14-day threshold to flag stale data because:- Most business analyses look at 7–30 day windows
- A 14-day gap means you’ve lost visibility into recent trends
- It’s long enough to avoid false alarms from weekend or holiday pauses
Common scenarios
| What you see | What it likely means |
|---|---|
| Orders table is stale | E-commerce platform sync may be delayed or disconnected |
| Attribution table fresh but coverage low | Pipeline works, but tracking may not — check Attribution Health |
| Ad performance empty for a platform | That integration may not be connected |
| Multiple tables 14+ days stale | Broader pipeline issue — recent analyses across domains are affected |
| Single table stale, others fine | Platform-specific issue (API error, auth expiration, rate limits) |
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.
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?”
Data Health vs Attribution Health
| Data Health | Attribution Health | |
|---|---|---|
| Focus | Table freshness and availability | Tracking and UTM coverage quality |
| Question it answers | ”Is my data pipeline working?" | "Is my marketing attribution accurate?” |
| When to check | Before any analysis | When results look wrong despite fresh data |
Related resources
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.