1. What is DQ Evaluation
Evaluation is the process that runs our data quality rules against live data and produces
scores.
Key facts:
• Uses the same process framework as MDG Consolidation and Mass Processing
• Rules are applied to active data in the system – Customer, Supplier, Material Master, and custom master data.
• Results are stored for later analysis.
2. Configuring Evaluation
Run Transaction Code MDCIMG -> Configure Evaluation.
We may use the standard Configuration ID – SAP_EVA or create our own.
Next, Configure Process Template. The Evaluation process will run based on this template.
Go to MDCIMG -> Configure Process Template -> Specify Process Template
SAP_BP_EVA is the standard default template provided by SAP for Business Partner Data Evaluation. Configuration ID SAP_EVA is assigned to this template. Pick this pre-build config or configure your own.
3. Scheduling an Evaluation
To schedule an Evaluation of your existing data, set below:
• Timing: Start date/time
• Frequency: recurrence (daily, weekly, etc.)
• Parameters: Description + Process template (as created in previous step)
4. Evaluation Step Results
The evaluation step output gives us:
1. Step Messages — execution errors/warnings
2. Evaluation Results — count of OK vs. Not OK outcomes, plus a chart split across evaluated tables (Tax Numbers, Basic Data, Description, Sales Data, etc.)
5. Score Calculation
Every rule evaluation produces five numbers:
MetricMeaningAvailableRows the rule could apply toCheckedRows actually in scope with a resultOKGood data resultsNot OKBad data resultsScoreOK ÷ (OK + Not OK)
5.1 Dimension Score Calculation
Consider below 2 important factors:
1. Impact: Is the rule important for the data quality dimension? Possible values are None (0), Low (1), Medium (2), and High (3)
2. Weight: Proportional to Impact. Medium impact weight = 2 × Low; High impact weight = 3 × Low, No impact = 0 weight
Example: Let’s take dimension as “Completeness” which has 2 rules
Score(S1) of Rule 1(R1) is 43.2
Score(S2) of Rule 2(R2) is 89.7
For this dimension, let’s say R1 has a Low (1) impact and R2 has High (3) impact.
Sum of impact = 4
Weight(W1) of Rule 1 = 1/4
Weight(W2) of Rule 2 = 3/4
Dimension Score = W1*S1 + W2*S2 = (1/4 * 43.2) + (3/4 * 89.7) = 78
Note: This score can be zero if impact is 0 or Rules score is 0.
6. Evaluation Results UI
Built on the standard SAP Fiori Analytical List Page (ALP) floorplan.
There are 3 UI zones (screenshot shown below):
1. Header/Filter bar — visual filters (e.g., Results by Outcome, Results by BP Category, etc)
2. Evaluation Items chart — smart chart for visualization, drill-down, filtering
3. Evaluation Items table — analytical table for detail, actions, export
7. Fixing Bad Data – Data Quality Remediation
Five practical remediation paths:
1. Delegate work — share the page link (with filters applied) to hand off correction work
2. Export evaluation items — download results table as XLSX
3. Navigate to single-object apps — fact sheet, Change Request, etc., per user role
4. Mass Processing — select 1+ rows to bulk-correct products/business partners
5. Export objects — select 1+ rows, export to XLSX for offline editing, then re-import via Mass Processing
8. Conclusion
From my experience, one of the biggest benefits of DQ Evaluation is that it transforms data quality discussions from subjective opinions into measurable KPIs. Instead of debating whether data quality is “good” or “bad,” stakeholders can rely on dimension scores, rule-level metrics, and trend analysis to make informed decisions.
You cannot Improve what you cannot measure!
1. What is DQ EvaluationEvaluation is the process that runs our data quality rules against live data and producesscores.Key facts:• Uses the same process framework as MDG Consolidation and Mass Processing• Rules are applied to active data in the system – Customer, Supplier, Material Master, and custom master data.• Results are stored for later analysis.2. Configuring EvaluationRun Transaction Code MDCIMG -> Configure Evaluation.We may use the standard Configuration ID – SAP_EVA or create our own.Next, Configure Process Template. The Evaluation process will run based on this template.Go to MDCIMG -> Configure Process Template -> Specify Process TemplateSAP_BP_EVA is the standard default template provided by SAP for Business Partner Data Evaluation. Configuration ID SAP_EVA is assigned to this template. Pick this pre-build config or configure your own.3. Scheduling an EvaluationTo schedule an Evaluation of your existing data, set below:• Timing: Start date/time• Frequency: recurrence (daily, weekly, etc.)• Parameters: Description + Process template (as created in previous step)4. Evaluation Step ResultsThe evaluation step output gives us:1. Step Messages — execution errors/warnings2. Evaluation Results — count of OK vs. Not OK outcomes, plus a chart split across evaluated tables (Tax Numbers, Basic Data, Description, Sales Data, etc.)5. Score CalculationEvery rule evaluation produces five numbers:MetricMeaningAvailableRows the rule could apply toCheckedRows actually in scope with a resultOKGood data resultsNot OKBad data resultsScoreOK ÷ (OK + Not OK)5.1 Dimension Score CalculationConsider below 2 important factors:1. Impact: Is the rule important for the data quality dimension? Possible values are None (0), Low (1), Medium (2), and High (3)2. Weight: Proportional to Impact. Medium impact weight = 2 × Low; High impact weight = 3 × Low, No impact = 0 weightExample: Let’s take dimension as “Completeness” which has 2 rulesScore(S1) of Rule 1(R1) is 43.2Score(S2) of Rule 2(R2) is 89.7For this dimension, let’s say R1 has a Low (1) impact and R2 has High (3) impact.Sum of impact = 4Weight(W1) of Rule 1 = 1/4Weight(W2) of Rule 2 = 3/4Dimension Score = W1*S1 + W2*S2 = (1/4 * 43.2) + (3/4 * 89.7) = 78Note: This score can be zero if impact is 0 or Rules score is 0.6. Evaluation Results UIBuilt on the standard SAP Fiori Analytical List Page (ALP) floorplan.There are 3 UI zones (screenshot shown below):1. Header/Filter bar — visual filters (e.g., Results by Outcome, Results by BP Category, etc)2. Evaluation Items chart — smart chart for visualization, drill-down, filtering3. Evaluation Items table — analytical table for detail, actions, export7. Fixing Bad Data – Data Quality RemediationFive practical remediation paths:1. Delegate work — share the page link (with filters applied) to hand off correction work2. Export evaluation items — download results table as XLSX3. Navigate to single-object apps — fact sheet, Change Request, etc., per user role4. Mass Processing — select 1+ rows to bulk-correct products/business partners5. Export objects — select 1+ rows, export to XLSX for offline editing, then re-import via Mass Processing8. ConclusionFrom my experience, one of the biggest benefits of DQ Evaluation is that it transforms data quality discussions from subjective opinions into measurable KPIs. Instead of debating whether data quality is “good” or “bad,” stakeholders can rely on dimension scores, rule-level metrics, and trend analysis to make informed decisions.You cannot Improve what you cannot measure! Read More Technology Blog Posts by Members articles
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