Concept card

Data Quality Frameworks

As of 2026-07-23

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

What is Data Quality Frameworks?

Data quality frameworks range from simple null checks to statistical drift detection on production pipelines — the discipline of rules, metrics and remediation that keeps data trustworthy.

What it is

A data quality framework is the set of rules, measurements and remediation workflows that decide whether a number is allowed to reach a decision-maker. It spans the trivial (a not-null constraint on a key) and the statistical (drift detection on a production pipeline), and its real subject is not correctness but accountability: who is told when a value is wrong, and what happens next.

Why it matters

On an SAP analytics engagement, quality is the discipline that decides whether a semantic layer survives contact with the business. A dashboard that is 3 % wrong is not 97 % useful — it is unusable, because no one can tell which 3 %. The framework is what converts "the data looks off" from an argument into a ticket with an owner.

It is also increasingly a compliance surface rather than an engineering preference. For AI systems in scope, the EU AI Act requires appropriate levels of accuracy and robustness and that data governance practices be documented (EUR-Lex — Regulation (EU) 2024/1689). A model trained on a pipeline with no quality controls is not merely risky; it is difficult to declare.

How it works

Why it matters in practice

A drift-detection layer catches quality erosion that a one-time null check never will — the two belong at opposite ends of the same maturity curve, not as alternatives.
Remediation workflows, not just detection rules, are what turns a quality alert into a fixed dataset — detection without a remediation path is a dashboard nobody acts on.
This is depth-of-field knowledge consultants use to anchor rate negotiations — a client that trusts the data trusts the consultant who guarantees it.

Key points

The set of rules, metrics, and remediation workflows that keep data trustworthy.
Ranges from simple null checks to statistical drift detection on production pipelines.
Classified under Governance & Operations (Advanced) — depth-of-field knowledge, used to anchor rate negotiations.
Tagged: governance — surfaces in the Academy search alongside related tracks.
Data Quality Frameworks is mastered only when it changes a named buyer decision.
Start with the semantic contract and control model before demonstrating the tool.
Use current SAP, analyst, study, KG, and news signals as evidence, not decoration.
Separate verified facts from directional trends and modeled assumptions.
Define owner, metric, threshold, support path, and rollback before scaling.
For non-AI use cases, still define quality, adoption, and operating ownership.

Terms used on this page

Data owner: The business stakeholder accountable for the correctness of a data domain (not the IT team).
Data steward: The operational role that maintains master data quality day-to-day.
Decision owner: The accountable person who accepts the trade-off and funds the next action.
Semantic contract: The shared definition of business terms, metrics, entities, and access rules used by tools and teams.
Control plane: The layer that applies policy, access, lineage, monitoring, and escalation across the operating model.
Evidence grade: A label that separates verified fact, directional signal, modeled assumption, and field observation.
Adoption metric: The measurable behavior proving that the concept changed actual work after go-live.
Reusable IP: An artifact, checklist, or model that can be reused across clients without copying client-specific data.

Sources

Full card available to members. What the full card adds: the full decision framework · the SAP vs Snowflake / Databricks / Fabric comparison · the common pitfalls and their fix · the cheat sheet · the architecture schemas · the code blocks.

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