Data Integrity Pre-Flight (Onboarding)

Data Integrity Pre-Flight (Onboarding)

1. 01

   Map the client's data landscape before committing to any observability tool

   Inventory all source systems, warehouses, and pipelines that feed client analytics or AI initiatives. Note data owners, update frequencies, and known quality issues.

2. 02

   Define data quality metrics that matter to the client's business outcomes

   Select 3-5 measurable dimensions such as completeness, uniqueness, timeliness, or validity. Tie each to a specific decision or report the client relies on.

3. 03

   Run a baseline data quality assessment across critical tables and streams

   Use profiling queries or a platform like Syncari to quantify current error rates, duplicates, and missing values. Record these numbers as the starting benchmark.

4. 04

   Establish alert thresholds and escalation paths for data anomalies

   Set concrete thresholds (e.g., row count drops below 90% of 7-day average) and define who gets notified and when. Document the response procedure for each alert type.

5. 05

   Document data lineage and ownership for every critical asset

   Create a lineage map showing where each data element originates, transforms, and lands. Assign a named owner responsible for resolving issues in each segment.

6. 06

   Agree on a remediation workflow with the client before go-live

   Define how detected issues are triaged, who fixes them (agency vs. client), and the expected turnaround time. Include a fallback plan for high-severity incidents.

7. 07

   Review the observability tool's integration complexity with the client's stack

   Assess whether tools like Monte Carlo can connect to the client's existing data infrastructure without custom code. Identify potential blockers and plan for them.