Sierra Digital · S/4HANA Readiness
Data Quality & MDM Advisory
Master data assessment across 8 domains · SAP MDG readiness · Data governance maturity
SLB
Overall Score: 66/100
MDM: Developing
8 Domains Assessed
DQ Score
66/100
Developing · Action Required
Overall DQ Score
66/100
Aggregate across all domains
Developing
Records Assessed
398K
Across 8 master data domains
All domains
Issues Identified
92K
Records requiring remediation
23% issue rate
MDM Maturity
2.0/5
Data governance maturity
Developing
Domain Score Matrix
8 master data domains × 5 quality dimensions — worst first
| Domain | Score | Completeness | Accuracy | Consistency | Uniqueness | Timeliness | Records | Issues |
|---|
Data Quality Dimensions
What each dimension means for S/4HANA migration readiness
Completeness
Percentage of required fields populated with non-null, non-blank values across all records in the domain.
S/4 Impact: Mandatory fields for S/4 document posting will cause migration rejections. Financial reporting requires 100% GL account completeness.
Accuracy
Degree to which data correctly reflects real-world values — verified against authoritative sources or business rules.
S/4 Impact: Inaccurate material master data corrupts MRP and procurement runs. Impacts order-to-cash and procure-to-pay process automation.
Consistency
Same entity represented identically across systems and tables — no contradictions between related records or systems.
S/4 Impact: BP/vendor deduplication requires consistent key fields. Cross-system inconsistencies are the top cause of MDM project delays.
Uniqueness
Absence of duplicate records — each real-world entity represented exactly once in the system of record.
S/4 Impact: Duplicate vendors and business partners are the #1 blocker in S/4HANA migrations. S/4 Business Partner model enforces single-record constraint.
Timeliness
Currency of data — how recently records were reviewed or updated relative to business change frequency.
S/4 Impact: Stale master data undermines S/4's real-time analytics capability. Embedded analytics require current data to deliver value to operations.
MDM Maturity Assessment
5-dimension capability assessment · Target: Level 4 Managed
Capability Dimensions
1 Ad-Hoc
2 Developing
3 Defined
4 Managed
5 Optimised
Maturity Overview
2.0
MDM Maturity Score
Out of 5.0 ·
Gap to target (Level 4 Managed): 2.0 points
Estimated remediation: 12–18 months with SAP MDG
Estimated remediation: 12–18 months with SAP MDG
What Each Level Means
1 Ad-Hoc
No formal DQ processes; firefighting mode
2 Developing
Some processes defined; inconsistent adoption
3 Defined
Standardised processes; documented ownership
4 Managed
Metrics-driven; proactive governance with MDG
5 Optimised
AI-driven quality; self-healing master data
SAP MDG Advisory Recommendations
Prioritised actions to reach Level 4 Managed before S/4HANA go-live
Immediate · 0-3 Months
Establish Data Ownership Framework
Assign formally accountable data stewards for each of the 8 master data domains. Define RACI across
IT, Finance, Procurement and Operations to eliminate the current "everyone's data is nobody's data" gap.
- Appoint domain data stewards with executive mandate
- Define data ownership policy and escalation paths
- Baseline current quality with agreed KPIs per domain
- Create data issue log with SLA-driven resolution tracking
Short-Term · 3-9 Months
Deploy SAP Master Data Governance
Centralise BP, Material and Finance master data management on SAP MDG. Implement workflow-driven
create/change/retire processes with approval governance to eliminate uncontrolled data changes.
- Deploy MDG-BP to consolidate vendor + customer into S/4 Business Partner
- Implement MDG-M for material master governance and deduplication
- Configure MDG-F for GL account, cost centre and profit centre lifecycle
- Integrate MDG workflows with S/4 change management processes
Strategic · 9-18 Months
Implement Data Quality Monitoring
Establish real-time DQ dashboards via SAP BDC and embedded S/4 analytics. Move from retrospective
data cleansing to proactive quality monitoring with automated alerts and self-service remediation.
- Deploy SAP BDC for continuous master data quality scoring
- Build S/4 embedded DQ cockpit with domain-level KPI tracking
- Implement ML-based duplicate detection across BP and material master
- Connect DQ metrics to executive data governance dashboard
Priority Improvement Roadmap
Sequenced actions to close the data quality gap before S/4HANA go-live
Quick Wins
0–3 Months
DQ Score Improvement
+6 pts
Effort Level
Low
Vendor deduplication sprint
Remove duplicate vendor records using fuzzy-match rules. Highest ROI action — eliminates dual payments and procurement leakage.
GL account completeness sweep
Fill mandatory GL account attributes (FSG, account group, tax category). Blocks S/4 financial close automation if left unresolved.
Inactive material archival
Archive materials with zero movements in 3+ years. Reduces migration scope and improves completeness scores immediately.
Data steward nomination
Name and induct domain data stewards for all 8 domains. Required governance prerequisite — zero technical effort, maximum impact.
Foundation
3–9 Months
DQ Score Improvement
+12 pts
Effort Level
Medium
SAP MDG-BP deployment
Consolidate vendor and customer into the S/4 Business Partner model via MDG-BP with workflow-controlled create/change/block/retire.
Material master enrichment
Populate missing classification, MRP, and procurement data. Required for S/4 embedded MRP and digital supply chain activation.
Z-table shadow data migration
Migrate high-risk Z-table master data into standard SAP structures with MDG controls, eliminating bypass of governance workflows.
BP timeliness programme
Implement annual BP review cycle with customer/vendor confirmations. Closes the largest timeliness gap across Business Partners and Customers.
Optimisation
9–18 Months
DQ Score Improvement
+9 pts
Effort Level
High
Real-time DQ cockpit via SAP BDC
Deploy SAP Business Data Cloud for live quality scoring dashboards. Shifts DQ from project activity to operational discipline.
ML-driven duplicate detection
Implement BTP-hosted ML model for continuous duplicate identification across BP and material master — prevents new duplicates at point of entry.
Equipment/Asset data enrichment
Complete functional location hierarchy, maintenance strategies, and measurement points. Enables predictive maintenance on S/4 PM/EAM.
MDM governance operating model
Embed DQ metrics into executive reporting cadence. Data Quality Council meets quarterly to review domain scores and approve remediation budgets.