Why Master Data Should Come First
When finance teams consider automation, transactions are usually the starting point. Journals, invoicing, reconciliations, close. They are repetitive, rules-based, and visibly inefficient, which makes the business case straightforward.
What’s discussed less often is where many inefficiencies actually start. In practice, they start before the first transaction is created.
Most downstream finance issues aren’t caused by incorrect posting. They’re caused by systems posting transactions correctly, based on poorly governed master data.
This typically shows up as:
Incorrect default accounts or dimensions
Incorrect tax treatments
Incorrectly default properties for suppliers and customers (think payment methods, payment terms etc)
Duplicate records created under time pressure
Changes made without full visibility of impact
Once this data is in the system, every transaction that follows inherits the issue. The effort then shifts to correction, explanation, and reconciliation - often repeatedly.
Why is Master Data Is Still Handled Manually
Despite its impact, master data is still handled informally in many organisations. Requests arrive via emails or spreadsheets, approvals happen ad hoc, and changes are pushed through to avoid operational delays. Transactional processes are typically seen as the safest starting point for automation. The effort is visible, the benefits are easy to quantify, and the risks feel immediate. Master data, by contrast, is often viewed as low-volume, manageable, and not in need of the same level of control or investment.
That perspective is understandable, particularly in a cost-justification context. The issue is that it favours visible short-term gains over systemic efficiency.
What Automating Master Data Involves
Automating master data doesn’t remove oversight. It applies it earlier and more consistently.
In practical terms, this usually includes:
Standardized request intake
Validation rules before creation or change
Role-based approvals
Event-driven workflows
A clear audit trail
The objective isn’t speed for its own sake. It’s preventing avoidable rework from entering the transaction layer.
A useful illustration comes from Bayer, which automated large parts of its SAP master data governance processes. By introducing automated validation and approval logic for low-risk master data changes:
Requests that previously took up to two days were processed in seconds
Overall processing time was roughly halved
Around 40% of master data requests were handled automatically, freeing teams to focus on higher-value work
The efficiency gains weren’t realised in transaction processing itself. They were realised upstream — by reducing the volume of issues carried into transactions.
Why This Matters for CFOs
Transaction automation improves efficiency at the point of execution. Master data automation improves efficiency across every process that relies on that data. A single, well-controlled master data change can eliminate a significant amount of downstream correction and rework.
Consider a simple scenario. A new supplier is created and a high volume of invoices is processed through an automated OCR solution. The automation performs as expected, significantly reducing manual effort. Late in the period, it becomes apparent that the supplier payment terms were set incorrectly. At that point, the issue is no longer isolated. Every invoice processed using that data now requires correction The automation hasn’t failed. It has simply scaled a small upstream issue into a large downstream problem. This is the risk of automating transactions before stabilising master data.
A Practical Starting Point
This doesn’t require a transformation program.
Identify the master data objects that consistently drive rework
Automate creation and change workflows first
Keep rules simple, ownership clear, and approvals explicit
Closing Thought
Transaction automation makes finance faster. Master data automation makes finance cleaner. Clean data compounds efficiency in a way speed alone does not.
