Communicating Data Quality Issues with the SBAR Framework

Introduced by Mark Freeman in the “Data Quality: Analytics and Serving” course, SBAR is a structured communication framework that’s highly effective for reporting data quality issues to stakeholders. It ensures our message is clear, concise, and actionable, especially when the problem is complex.

Here is the template with an example:

S: Situation: Present the immediate problem or concern in clear, factual terms:

NYC Parking Violation revenue dropped from $1.4M to $805k: A sudden and significant drop in reported revenue triggered an investigation.

B: Background: Provide relevant context and contributing factors:

Pipeline changes excluded Precinct 0; data had inconsistent county names and unknown agency codes: Recent transformations introduced silent errors by mishandling key fields.

A: Assessment: Analyse the root cause and business impact:

Missing Precinct 0 and school zone violations caused underreporting; county/agency issues hint at upstream problems: the root cause was data omission and poor input standards from source systems.

R: Recommendation: Present prioritised action items with clear ownership:

Immediate & Medium-Term Actions:

• Restored missing Precinct 0 data

• Normalised inconsistent county names

• Implemented data quality checks to flag missing precincts, unknown agency codes, and naming issues

Long-Term Actions:

• Collaborate with upstream teams to improve data collection processes

• Strengthen data input standards to prevent silent failures in future pipeline changes