3. Why Data Will Make or Break Your S/4HANA Programme — Long Before Go-Live
- David Murphy
- Apr 14
- 4 min read
Updated: 6 days ago
Introduction
While process and technology get a high proportion of the attention, it is data that ultimately determines whether your design works in reality.
The Reality: Data is Not a Downstream Activity
In many S/4HANA transformations, data is treated as a downstream activity — something to be addressed once design is stabilised and build is underway, or where the scale of cleansing and enrichment is underestimated until it becomes a critical risk.
In reality, data should start immediately — if not yesterday, then at a minimum in parallel with system design, fully aligned to SAP Activate. Programmes that delay this almost always face avoidable delays, rework, and compromised outcomes.
Because while processes can be redesigned, data exposes the truth of how your organisation actually operates.
Why Data Matters More Than You Think
If process is the engine and technology is the vehicle, then data is the fuel.
You can design the best processes. You can implement the best system.
But without the right data — clean, structured, and governed — nothing runs.
And worse, many organisations only realise this when they are already running on empty.
The Hidden Complexity of Legacy Data
Most organisations are not dealing with a single, clean dataset. Instead, they inherit:
Multiple legacy systems with overlapping data
Inconsistent naming conventions across regions
Duplicate vendors, customers, and materials
Conflicting hierarchies and classifications
Missing or incomplete attributes
At first glance, this looks like a migration challenge.
In reality, it is a business alignment challenge.
Start Early — In Parallel, Not Sequentially
A common mistake is to wait until design decisions are made before tackling data.
This creates two problems:
Design is based on assumptions, not real data
Data issues are discovered too late to influence process decisions
Instead, leading programmes start data work alongside Explore and Design phases:
Profiling legacy data from day one
Identifying duplicates and inconsistencies early
Feeding insights back into process design
Highlighting where standard processes require real business decisions
Data should not follow design — it should inform it.
Alignment Across Locations: One Business, Not Many Variants
One of the hardest — and most valuable — aspects of data transformation is standardisation across geographies and business units.
Typical challenges include:
The same vendor created multiple times in different countries
Materials defined differently by region or function
Customers structured inconsistently across sales organisations
Without alignment, S/4HANA simply inherits fragmentation at scale.
Achieving alignment requires:
Agreement on global definitions
Clear ownership of master data domains
Willingness to challenge local variations that add little value
This is not a technical exercise — it is organisational change.
Establishing Data Ownership and Accountability
Clean data does not happen by accident. It requires clear accountability.
Strong programmes define:
Data owners (accountable for quality and standards)
Data stewards (responsible for day-to-day management)
Governance forums to resolve conflicts and enforce decisions
Without this structure, data decisions drift — and inconsistencies persist.
Policies, Standards, and Controlled Design
To create sustainable data quality, organisations must define:
Mandatory vs optional fields
Permitted values and controlled vocabularies
Naming conventions and data standards
Validation rules aligned to business processes
These decisions are critical.
If left undefined, the system becomes open to interpretation — and data quality deteriorates rapidly.
Migration and Conversion: More Than a Technical Task
Data migration is often seen as a technical workstream.
But successful programmes treat it as a business-led transformation activity, including:
Iterative data cleansing cycles
Business validation of migrated datasets
Reconciliation against legacy systems
Clear criteria for what data is migrated, archived, or retired
Migration is not just about moving data — it is about improving and cleaning it before it enters the new system.
The Critical Role of Business Validation
No amount of technical validation can replace business ownership.
Users must:
Review and validate cleansed data
Confirm that structures reflect how they operate
Take accountability for accuracy before go-live
Without this, issues simply transfer from legacy systems into S/4 — often harder to fix later.
After Go-Live: Preventing Data Decay
I have witnessed many programmes invest heavily in cleansing data — only to see it degrade quickly after go-live.
Avoiding this requires sustained focus across three dimensions:
People
Ongoing data ownership and stewardship
Clear accountability for data quality
Process
Defined workflows for data creation and maintenance
Approval mechanisms for new entries and changes
Technology
Validation rules embedded in the system
Controls on permitted values and mandatory fields
Monitoring and reporting on data quality
Without these in place, even the cleanest dataset will deteriorate.
Keep Data Clean — and Keep It Lean
One of the biggest risks post go-live is data bloat:
Unused materials created “just in case”
Duplicate vendors reintroduced
Poor discipline in maintaining standards
Over time, this erodes the value of the transformation.
Leading organisations actively manage this by:
Restricting unnecessary data creation
Regularly reviewing and archiving unused records
Enforcing governance consistently
The goal is not just clean data — but controlled, purposeful data.
Final Thought
S/4HANA programmes often focus heavily on process and technology.
But data is where those designs become real.
If you delay it, underestimate it, or treat it as a technical afterthought, it will dictate your outcomes — often at the worst possible time.
Start early. Align the business. Enforce standards.
And most importantly — don’t just clean your data for go-live.
Build the capability to keep it that way.
S/4HANA Transformation Series
When “Agile” S/4HANA Programmes Quietly Revert to Waterfall — and Why It Matters
Fit-to-Standard — But Which Standard Are You Actually Choosing?
Why Data Will Make or Break Your S/4HANA Programme — Long Before Go-Live
Right-Sizing Change Management in S/4HANA — Why Timing Matters More Than Headcount
Rationalising External Systems in S/4HANA — Why Integration Simplicity is a Strategic Advantage
Documentation from Day One — Why Documentation from Day One Matters
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