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Academy module

Data Quality Fundamentals

Data Quality Fundamentals — profiling to monitoring flow — architecture diagram for Data Quality Fundamentals, Analytics Legends Academy module M076

As of 2026-08-16

Edited by , sole editor and publication director — 27 years in the SAP ecosystem. How these pages are produced, and where automation is used.

Data quality issues are the leading cause of business distrust in analytics systems. The consultant who quantifies DQ — with dimension-level scores, profiling evidence, and business-agreed thresholds — converts a vague complaint into a governance artefact and an advisory engagement. The DQ scorecard is the anchor. The six dimensions (completeness, accuracy, consistency, timeliness, uniqueness, validity) each require a different measurement technique; conflating them leads to remediation that never finishes. Post-go-live monitoring via Datasphere Data Quality Rules is the only mechanism that prevents DQ regression after migration.

What you will learn

  • Profile a data extraction scope across the six DQ dimensions (completeness, accuracy, consistency, timeliness, uniqueness, validity) — producing dimension-level scores for each object using SAP Datasphere profiling tools
  • Build a DQ scorecard for an analytics migration or go-live scope — with measured dimension scores, business-agreed thresholds, composite scores, and a remediation priority ranking
  • Diagnose the five DQ failure modes on SAP analytics engagements (no pre-modelling profiling, undefined thresholds, accuracy/completeness confusion, deferred deduplication, no post-go-live monitoring) and prescribe the correct remediation for each
  • Design a post-go-live DQ monitoring configuration in Datasphere — specifying Data Quality Rules, threshold values, alert routing, and remediation workflow for a production analytics estate

Data quality is the single most credibility-critical skill on any analytics engagement, and it is credibility-critical precisely because it is invisible until it fails in public. A consultant who cannot quantify a data quality issue — measure it against a defined dimension, express it as a number a business stakeholder can act on, and propose a remediation path with a cost and a priority — is the consultant who loses the room the first time a dashboard number looks wrong in front of an executive. This module builds the practitioner's data quality toolkit from first principles: the dimensions that make quality measurable, the scorecard that turns measurement into a governance decision, the profiling techniques that produce the measurements in the first place, the failure modes that recur across engagements, and the monitoring configuration that keeps quality from silently decaying after go-live.

The six dimensions of data quality

Prerequisites

  • Review core concepts first: C041, C038, C037

Outcomes

  • Profile a data scope using the six DQ dimensions and produce mechanically-derived dimension-level scores
  • Build and present a DQ scorecard with business-agreed thresholds and remediation priorities
  • Diagnose and remediate each of the five DQ failure modes on SAP analytics engagements
  • Configure Datasphere Data Quality Rules for post-go-live monitoring with threshold-based alerting

Full module available to members. The full module adds: the decision framework · the end-to-end scenario walkthrough · the KPI scorecard · the anti-patterns · the code blocks · the knowledge check · the diagrams.

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