Here is a scenario that occurs in more Indian enterprises than most IT leaders are comfortable admitting. A procurement team needs to run a spend analysis. They pull vendor data from the ERP. They find that the same vendor — say, a packaging supplier in Pune — appears under seven different names: 'Sharma Packaging', 'Sharma Packaging Pvt Ltd', 'Sharma Pkg', 'SHARMA PACKAGING', 'Sharma Packing', 'Sharma Packaging LLP', and 'Sharma Pkg. Pvt'. The team spends a week reconciling the records before the analysis can even begin.
Multiply this across vendors, customers, items, and employees — across multiple entities, acquired companies, or regional offices — and you have a data quality problem that is costing real money in decision-making latency, procurement inefficiency, and audit preparation time.
Why Indian ERP Environments Have Particularly Severe MDM Problems
Several factors specific to Indian enterprise environments compound the data quality challenge:
- Multiple entities under one group: Indian business groups often operate 5-15 legal entities, each with their own ERP instances and master data created independently
- Legacy migration debt: Data migrated from older systems (or from Tally to SAP, for example) was rarely cleaned before migration
- Localisation complexity: Vendor and customer names in English transliterations of Hindi or regional language names have no standard form
- GST compliance gaps: GSTIN mapping to vendor master records is frequently incomplete or inconsistent
- High staff turnover in data entry roles: No consistent data creation standards enforced across teams
- Acquired companies: M&A activity brings in ERP instances with entirely different master data structures
The Four Domains of Enterprise Master Data
MDM covers four primary domains. Each has its own data quality patterns, business impact, and cleansing approach.
| Domain | Common Issues | Business Impact |
|---|---|---|
| Customer MDM | Duplicate records, inconsistent addresses, missing GSTIN | Billing errors, compliance risk, poor CRM insights |
| Vendor MDM | Duplicate codes, name variants, missing PAN/GSTIN | Payment errors, audit findings, procurement inefficiency |
| Product/Item MDM | Duplicate items, inconsistent descriptions, missing specs | Wrong procurement, poor inventory analytics |
| Employee MDM | Duplicate IDs, inconsistent name formats, outdated records | Payroll errors, DPDP compliance risk |
The MDM Implementation Process
Phase 1: Discovery and Profiling
Before you can fix master data, you need to understand how broken it is. Data profiling analyses your existing records to quantify: duplicate rate (what percentage of records are duplicates of other records), completeness (what percentage of required fields are populated), consistency (are the same values represented the same way across systems), and conformity (do values match expected formats — PAN, GSTIN, pincode).
For a typical mid-size Indian enterprise, we commonly find: 8-15% duplicate rate in vendor master, 12-20% duplicate rate in customer master, 5-10% of item codes active in the ERP that are either obsolete or duplicates of other items.
Phase 2: Cleansing
Cleansing involves standardising name formats, merging duplicate records (with survivor record selection logic), enriching incomplete records from verified sources, and flagging or deleting obsolete records. This phase is labour-intensive and requires domain knowledge — a vendor master cleanse requires understanding of business relationships, not just data matching algorithms.
Phase 3: Governance Framework
Cleaned data becomes dirty again without governance. A governance framework defines: who has authority to create new master records, what information must be provided before a record can be created, what validation rules the system enforces at creation, and what the process is for requesting changes. Without governance, you are mopping the floor with the tap still running.
Our data management practice has cleaned over 10,000 enterprise data records across ERP platforms including SAP, Oracle, Microsoft Dynamics, and Infor. The single most common finding: organisations have 2-3x more active records than they have active business relationships. The cleanup consistently reduces storage costs, improves query performance, and makes analytics materially more accurate.
Measuring the ROI
MDM ROI is measurable across three categories. Procurement efficiency: with clean vendor master, spend analysis becomes hours instead of weeks, enabling better supplier negotiations. Compliance: GSTIN-matched records prevent GST credit mismatches and audit penalties. Data-driven decisions: duplicate-free customer and product data produces accurate sales analytics for the first time in some organisations.
If your organisation is planning an ERP upgrade, migration to S/4HANA, or consolidation of entities — the single highest-ROI investment you can make before the migration is a master data cleanse. The cost of migrating dirty data into a new system is significantly higher than cleaning it before migration.