Bridge strategies reframed through Lean Manufacturing's quality principles, applied to the data migration stream under RISE with SAP
This maps the top challenges for SAP data migration projects and how uMigrate solves them using Lean principles.
Explore uMigrate → Talk to us| # | Challenge | Eliminate Waste |
Build Quality In |
Create Knowledge |
Defer Commitment |
Deliver Fast |
Respect People |
Optimize the Whole |
|---|---|---|---|---|---|---|---|---|
| 01 |
High
Migration Treated as Load Activity, Not a Workstream
SIs own only the "L" in ETL. Extraction, cleansing, and transformation are dumped on business users who lack expertise. Data prep becomes the invisible bottleneck.
|
Stop rework loops from late-discovered data defects. Profile source quality in Week 1 — every defect found post-load is pure waste. ★★★★★5 |
Embed data quality gates at extraction and transformation — not first inspection at load failure. Use profiling tools before blueprint. ★★★★★5 |
Document source-to-target mapping rules and transformation logic as reusable IP — not tribal knowledge locked in one consultant's head. ★★★★☆4 |
Don't lock the migration approach until source data profiling reveals actual complexity. ★★★☆☆3 |
Automate extraction pipelines from Day 1. Manual CSV exports from legacy systems are the #1 calendar killer. ★★★★☆4 |
Don't dump 100-column LTMC templates on business users without guidance. Provide annotated templates with field-level instructions. ★★★★★5 |
Treat ETL as a first-class workstream with its own RACI, milestones, budget line, and lead — parallel to functional config, not appended after. ★★★★★5 |
| 02 |
High
Load Sequencing & Cross-Object Dependencies Unknown
No SAP-published, machine-readable dependency graph for 61+ LTMC objects. SIs discover failures at runtime. Each failure cascades across modules.
|
Eliminate trial-and-error discovery. Every sequence failure = rollback + re-load + re-validate. A validated DAG removes this entire waste class. ★★★★★5 |
Validate the dependency graph (topological sort, cycle detection) before the first trial load — not iteratively through failure. ★★★★★5 |
Build the dependency graph as a versioned, machine-readable artifact (JSON DAG) that becomes practice-level IP across all projects. ★★★★★5 |
Lower leverage — dependencies are structural, not optional. But defer custom object sequencing until standard objects are proven. ★★☆☆☆2 |
Precomputed wave assignments (e.g., via uMigrate) let teams load in parallel per wave — collapsing elapsed time dramatically. ★★★★★5 |
Don't burden teams with oral knowledge transfer of load order. Provide a visual, navigable graph — not a spreadsheet of notes. ★★★★☆4 |
Cross-module sequencing by a single architect — not per-module leads who discover collisions only when their loads fail against each other. ★★★★★5 |
| 03 |
High
Blurred Accountability Between SI, Customer & SAP
SOWs use ambiguous language ("assist," "support"). RISE adds a third party. No single Accountable per migration task. Disputes surface at cutover.
|
Ambiguous ownership = rework, finger-pointing, duplicate effort. Every task with two "A"s in the RASCI generates waste by definition. ★★★★☆4 |
Write binary acceptance criteria in the SOW: "SI loads agreed objects across 3 trial cycles. Business cleanses per SI rules within 10 business days." ★★★★★5 |
Publish a shared RASCI template at practice level so each new project doesn't re-negotiate the same ambiguities. ★★★☆☆3 |
Don't finalize the SOW until a joint workshop maps every migration task to exactly one "A." Premature sign-off bakes in gaps. ★★★★☆4 |
Clear single-owner accountability removes decision bottlenecks. Escalations resolve in hours, not weeks of email threads. ★★★★☆4 |
Give each person a clear, bounded scope so they can own outcomes with confidence. Ambiguity isn't flexibility — it's disrespect. ★★★★★5 |
A single RASCI spanning SI + customer + SAP for the entire migration lifecycle — not three separate views that don't reconcile. ★★★★★5 |
| 04 |
High
Chronic Shortage of Data Migration Specialists
The hybrid skill (SAP data models + ETL + business process) is rare. Functional consultants can't wrangle data; data engineers don't know SAP hierarchies.
|
Stop restarting from zero on each project. Every fresh ramp-up without reusable runbooks wastes 4–6 weeks of scarce specialist time. ★★★★☆4 |
Templatized runbooks per module (FI, MM, SD, HR) embed quality standards so mid-level consultants execute within guardrails. ★★★★★5 |
Build an internal "data migration guild" — 3–5 specialists who rotate across projects, accumulate lessons, and codify them into practice IP. ★★★★★5 |
Defer hiring permanent headcount until project pipeline justifies it — use guild rotation to flex capacity across engagements. ★★☆☆☆2 |
Invest in automation — staging table loads, pre-validation scripts, automated reconciliation — to reduce per-person manual effort. ★★★★☆4 |
Train and develop specialists through structured mentorship — don't burn generalist consultants by forcing them into unfamiliar data work. ★★★★★5 |
Staff migration as a practice-level capability (CoE), not a project-level hiring decision. One guild serves the whole portfolio. ★★★★★5 |
| 05 |
High
Trial Load Cycles Compressed or Skipped
Functional design overruns eat into migration time. SIs plan 3–4 trial loads but execute 1–2. Fixing post-go-live errors costs 5× more than upfront.
|
Manual reconciliation after each trial is pure waste. Automate the validate→reconcile cycle so humans focus on fixing, not finding. ★★★★★5 |
Minimum 3 full trial loads are a non-negotiable quality gate. Each cycle has defined entry/exit criteria. No shortcuts. ★★★★★5 |
Each trial generates a structured defect log with root-cause tags. Trial 2 should never repeat Trial 1's failures — that's knowledge creation. ★★★★☆4 |
Don't commit to a cutover date until Trial 2 exits clean. Calendar-driven go-lives with unvalidated data are the #1 project risk. ★★★★★5 |
Staging tables over file-based loads. Scripted reconciliation. Each full cycle should take days, not weeks — speed enables more iterations. ★★★★★5 |
Protect migration team capacity. Don't pull them into UAT firefighting for unrelated functional defects during critical load windows. ★★★☆☆3 |
Tie SI milestone payments to trial load completion, not just go-live. Incentive structure should reward quality iterations, not calendar compliance. ★★★★★5 |
| 06 |
Moderate
Historical & Transactional Data Scope Creep
SIs scope for master data, then get hit with open POs, WIP, and multi-year FI history. Each has unique load mechanics. SAP recommends against most historical migration.
|
Challenge every historical data request: "Will anyone query this in S/4HANA?" Data nobody uses is pure migration waste — archive it instead. ★★★★★5 |
Data migration architect present in blueprint workshops from Day 1. Scope decisions without migration impact analysis are defective by design. ★★★★☆4 |
Profile source volumes early and publish a "migration complexity scorecard" — record counts, object types, cross-dependencies — as shared knowledge. ★★★★☆4 |
Highest leverage here. Freeze scope at end of blueprint with signed-off object list. Changes follow change control with cost/timeline impact. ★★★★★5 |
Speed is less relevant — this is a scope control problem. Fast loading of wrong data makes things worse, not better. ★★☆☆☆2 |
Give stakeholders transparent cost/timeline impact of each scope addition. Respect their intelligence — don't just say "no," show the trade-off. ★★★★★5 |
Align migration scope to business process design, not legacy structure. Migrate what the new process needs — not what the old system accumulated. ★★★★★5 |
| 07 |
High
No Post-Load Reconciliation or Hypercare Plan
SIs treat go-live as the finish line. Reconciliation is manual or absent. Data issues surface in Weeks 1–4 when specialists have been demobilized.
|
Manual reconciliation spreadsheets are waste generators — slow, error-prone, non-repeatable. Automate or it won't happen under hypercare pressure. ★★★★☆4 |
Define reconciliation acceptance criteria before cutover: record counts, financial balances, open item matching, BP dedup checks. ★★★★★5 |
Document reconciliation patterns and variance thresholds as reusable templates. Next project should inherit, not reinvent, the playbook. ★★★★☆4 |
Don't declare go-live until reconciliation criteria are met. A live system with unreconciled data is a deferred crisis, not a delivered project. ★★★★★5 |
Automated reconciliation dashboards surface variances in hours, not weeks. Source vs. target with drill-down to individual mismatches. ★★★★★5 |
Keep 2+ migration specialists through 4 weeks of hypercare. Demobilizing at go-live abandons business users when they need help most. ★★★★★5 |
"End-to-end" means extraction through hypercare. The workstream closes when reconciliation signs off, not when data lands. ★★★★★5 |
| Avg. Impact → | 4.6 | 4.9 | 4.1 | 3.7 | 4.1 | 4.6 | 5.0 |