Material and vendor deduplication across plants, consistent units of measure, and classifications that conform to the target.
SKU and customer master cleanup, obsolete-item retirement, and governance that keeps inventory data clean after go-live.
Finance and supply chain master data cleansed to audit-ready standards, with compliance-grade audit trails on every change.
Asset and equipment master reconciliation and governance so reporting and maintenance run on trustworthy data.
Customer, material, and finance data cleansed and validated against business processes for a clean Golden Record.
Finance and supply chain data quality score, before and after
Obsolete and dark records identified for retirement, not migration
Mainly customers, vendors, materials, and finance data, plus the object-specific data your processes depend on. We profile each object for duplication, completeness, and conformance before any cleansing begins.
With matching and survivorship rules agreed with your data owners, deterministic and fuzzy matching, owner review of edge cases, and full audit trails. Nothing merges without a defensible rule behind it.
You can, but it is far more expensive and disruptive in a live production system. Cleaning before the move means you migrate once, cleanly. Best practice puts 25 to 30% of total migration effort into data preparation for this reason.
Both. We can run a one-time remediation, but we recommend pairing it with a governance model so the data stays clean after go-live and you do not repeat the exercise in three years.
Directly. The cleansing and deduplication decisions become documented transformation rules that the migration reuses, so there is no disconnect between cleanup and the move.
Deduplicate, cleanse, and govern SAP master data so it moves clean and stays that way.
No two master-data estates are the same, so the scope is measured before any number is discussed. These are the factors that move the effort.
How many records fail matching, completeness, or conformance checks decides how much deduplication and cleansing the data needs.
Master data scattered across multiple instances and non-SAP systems takes more work to match and survive to a single Golden Record.
Obsolete and unused records are identified for retirement, not migration, which can shrink scope rather than grow it.
A one-time cleanse differs from standing up an ongoing governance model with named owners, controls, and audit trails.
Regulated industries need compliance-grade audit trails on every change, which adds documentation and review.
Best practice puts 25 to 30% of total migration effort into data preparation, which is why measuring it first pays back.
This is the cleansing and governance workstream. If your situation looks more like one of the alternatives, start there instead.
A trial conversion failed on duplicate or non-conforming master data.
Finance and the business do not trust the same master records.
You want to migrate once, cleanly, rather than clean up in production later.
You need compliance-grade audit trails on every data change.
You need the full move planned and run end to end: start with SAP Data Readiness and Migration.
Your main risk is reporting going dark at cutover: see SAP Reporting and Analytics Modernization.
You are still scoping and want a read before committing: begin with the 30-Day Truth Audit.
Assess, blueprint, and migrate your data with reconciliation you can prove.
Keep reporting alive through the migration and modernize it after.
We deduplicate, cleanse, and govern your SAP master data so it moves to S/4HANA clean and stays trustworthy, by the business and by finance. Every change carries an audit trail.
SAP data quality and governance is the work of deduplicating, cleansing, and governing SAP master data so it moves to S/4HANA clean and stays trustworthy. Thinklytics runs that cleanup with an audit trail on every change, so the business and finance both trust the data before, during, and after the migration.
We treat data quality as the foundation the whole migration sits on. We profile the real condition of your master data, remediate it against rules you approve, and put governance in place so it stays clean after go-live.
A decade on ECC without governance leaves a predictable mess.
Data quality has the highest return where master data is complex and the cost of bad data is operational, financial, or regulatory. These are the segments where cleansing before the move pays for itself.
Start with the 30-Day Truth Audit. We profile your master data quality and show you exactly what remediation the migration will require, before it costs you at cutover.