At IndexCo, we take the uncertainty out of data migration. Our End-to-End Data Migration Services are designed to deliver clean, structured, and business-ready data into your new ERP system, whether you're moving to SAP, Epicor, Oracle, or another platform. We don’t just move your data, we transform it into a trusted foundation for operational success.
Our structured 6-phase methodology guides you from legacy discovery to post-go-live stabilization, while preserving auditability, minimizing risk, and maintaining business continuity.
How It Works: Our 6-Phase Data Migration Framework
- Discover
Conduct comprehensive data readiness assessments, source system analysis, target system analysis and stakeholder discovery. - Map
Facilitate collaborative workshops to map legacy data structures to target ERP data models and business rules. - Build
Develop & Execute robust, repeatable transformation logic using deterministic routines to cleanse, enrich, and reformat your data. - Test & Reconcile
Execute validation cycles, and reconciliation reporting to ensure data accuracy and completeness. - Refine
Use iterative test-feedback loops to progressively enhance data quality across multiple migration cycles. - Cutover
Deliver precision cutover planning and execution using automation, audit trails, and rollback safeguards.
Key Benefits of Our Data Migration Services
- Reduced Risk
Early issue detection and resolution through multi-cycle testing. - Enhanced Visibility
Transparent data quality reporting at every stage of migration. - Improved Quality
Progressive refinement leads to high-confidence and ERP-ready data. - Accelerated Delivery
Proven methodology eliminates delays and inefficiencies. - Audit Compliance
Maintains traceability and data lineage across all stages. - Business Continuity
Seamless cutover minimizes disruption to operations.
With IndexCo as your data migration partner, you're not just moving data, you're laying the foundation for ERP success, compliance, and operational excellence from day one.
Case 1 - Data Migration for Epicor Implementation
IndexCo Data supports onboarding a customer to Epicor, ensuring data is clean, accurate, and structured for a smooth migration. Our data quality solutions are designed to accelerate deployment and minimize disruption:
- Data Readiness Assessment
Profile the quality, completeness, and consistency of legacy data before migration. Map legacy data to Epicor data models. Uncover gaps and anomalies against business rules. - Data Cleansing & Standardization
Apply business-approved rules to standardize and correct formats, eliminate duplicates, enrich missing attributes, and align to governance policies. - Effective Use of EPICOR’s Data Management Tools (DMT)
Leverage deep knowledge of Epicor’s Data Management Tool (DMT) for validation, reconciliation and optimized data loads during simulations and go-live cutover. - Faster Implementation and Lower Risk
With clean data, organizations experience fewer roadblocks, enabling quicker go-live and immediate ROI.
Case 2 - Data migration for Epicor to SAP S4/HANA Migration
1. Overview
Migrating from Epicor ERP to SAP S/4HANA represents a transformative opportunity - but also a significant challenge - particularly when it comes to the integrity and readiness of master data. This approach includes a comprehensive strategy and structured methodology for managing the migration of relevant master data objects. It emphasizes the critical role that early planning and proactive remediation play in preventing data-related issues from delaying or derailing the overall ERP implementation.
The approach also maps data migration tasks against the broader SAP Activate implementation phases, underscoring how tightly integrated and time-sensitive these activities are. By aligning migration milestones with project execution, organizations can mitigate risk, maintain momentum, and ensure that data migration supports, not hinders a successful go-live.
2. Governance and Planning Structure
- Establish a Data Migration Governance Board with IT and Business stakeholders.
- Assign Data Owners, Stewards, and Migration Leads for each domain (Material, Customer, Vendor).
- Develop a Data Migration Charter, including scope, timeline, tools, responsibilities, and success criteria.
- Define data migration phases aligned to the SAP Activate methodology phases.
3. SAP S/4HANA Implementation Phases and Aligned Data Migration Activities
3.1 Discover Phase
SAP Activities: Value discovery, high-level solutioning, business case development.
Data Migration Activities:
- Identify legacy systems and data scope.
3.2 Prepare Phase
SAP Activities: Project initiation, team setup, landscape planning, initial workshops.
Data Migration Activities:
- Define data migration strategy, tools (e.g., SAP Migration Cockpit or a 3 rd party ETL tool), and governance.
- Identify data owners, assign data stewards.
- Extract data from Epicor and perform profiling to understand the current data quality and completeness.
- Identify duplicate, active, obsolete, and redundant records.
3.3 Explore Phase
SAP Activities: Fit-gap analysis, design based on SAP best practices, finalize scope.
Data Migration Activities:
- Define mapping rules from Epicor structures to SAP fields (e.g., UOM, Material Group, Account Group).
- Create data mapping documents for all relevant objects (materials, customers, vendors, etc.).
- Begin building migration templates and transformation logic for standardization.
- Validate business rules with functional experts and process owners.
- Conduct first mock data load with a subset of data.
3.4 Realize Phase
SAP Activities: Configuration, development, integration, unit and integration testing.
Data Migration Activities:
- Cleanse data for duplicates, missing values, formatting issues.
- Apply enrichment logic for mandatory SAP fields using rules or lookups.
- Involve business users for validation and decision-making on obsolete data.
- Execute second and third mock data loads with full datasets.
- Finalize transformation logic, load sequences, and reconciliation tools.
- Perform user validation of loaded data in QA environments.
- Run reconciliation reports to compare source vs. target record counts, key field values.
- Sign-off required before progressing to final load.
3.5 Deploy Phase
SAP Activities: Cutover planning, training, go-live preparation.
Data Migration Activities:
- Execute final cutover data load after freeze on legacy systems.
- Validate load success, perform reconciliations, sign-offs from business.
3.6 Run Phase
SAP Activities: Business operations in SAP, support and optimization.
Data Migration Activities:
- Monitor data usage and integrity in live system.
- Archive migration logs and finalize lessons learned.
4. Best Practices
- Early Planning: Address data quality from the start, not as an afterthought.
- Use Data Quality Tools: Leverage specialized tools for profiling, cleansing, and monitoring.
- Data ownership resides with business units, not just IT. Involve data owners in each validation cycle.
- Iterative Testing: Perform multiple mock migrations and validations to ensure readiness.
- Align mock data loads with major test cycles (SIT, UAT).
- Automate reconciliation and reporting for transparency.
- Maintain detailed mapping, cleansing logs, and transformation documentation.
5. Challenges
Noted below are some challenges foreseen with legacy data quality when migrating from Epicor to SAP S4HANA. These challenges warrants the need for partners who understand the Epicor & SAP systems well to anticipate the system data requirements and are well prepared to work with the business on their mitigation and having the right quality data available for migration in alignment with the project milestones. The below examples highlight the need for a knowledgeable partner across both Epicor and SAP.
Assumption: The below items are just some examples and are not exhaustive by any means.
6. Risk Mitigation Strategies
- A partner be engaged right through the data migration cycle who has the expertise and knowledge to support the client end to end with data migration. Bringing in data migration partners mid way through the project introduces risks to the overall project timelines or will introduce sub quality data in the new system..
- Establish strict change control after final mapping approval.
- Maintain a single source of truth and version control on mapping and transformation documents.
- Use automated profiling and validation scripts to catch issues early.
- Build robust reconciliation reports with thresholds for exception alerts.
- Train users early to validate data contextually, not just technically.
- Realistic and multiple Mock Data Runs to:
- Validate data quality and integrity after transformation.
- Test the performance and reliability of ETL tools and migration scripts.
- Ensure completeness of mapping rules and handling of edge cases.
- Confirm all SAP configurations are ready to accept the data.
- Measure and refine migration timing and sequencing (critical for cutover).
- Provide business users with confidence and familiarity.
7. Benefits of High-Quality Data in S/4HANA
- Accelerated and smooth transformation with fewer surprises.
- Faster time-to-value through better process performance.
- Trustworthy analytics for real-time decision-making
- Enhanced customer and supplier experiences.
- Reduced compliance and audit risks.
Case 3 - Data Migration for SAP ECC to SAP S4/HANA Migration:
1. Overview
The transition to SAP S/4HANA is more than just a technical upgrade, it is a strategic digital transformation that enables enterprises to simplify processes, harness real-time insights, and position themselves for future innovation. One of the most critical, yet often underestimated, components of this transformation is data quality. Without high-quality data, even the most sophisticated S/4HANA system cannot deliver its full potential.
Data is at the heart of all enterprise operations, governing finance, supply chain, customer relationships, and compliance. S/4HANA, with its in-memory computing capabilities and simplified data model, relies heavily on clean, harmonized, and well-structured data.
Poor data quality can derail an S/4HANA project causing delays, cost overruns, rework, and reduced business confidence in the new system.
2. Governance and Planning Structure
- Establish a Data Migration Governance Board with IT and Business stakeholders.
- Assign Data Owners, Stewards, and Migration Leads for each domain (Material, Customer, Vendor).
- Develop a Data Migration Charter, including scope, timeline, tools, responsibilities, and success criteria.
- Define data migration phases aligned to the SAP Activate methodology phases.
3. SAP S/4HANA Implementation Phases and Aligned Data Migration Activities
3.1 Discover Phase
SAP Activities: Value discovery, high-level solutioning, business case development.
Data Migration Activities:
- Identify legacy systems and data scope.
3.2 Prepare Phase
SAP Activities: Project initiation, team setup, landscape planning, initial workshops.
Data Migration Activities:
- Define data migration strategy, tools (e.g., SAP Migration Cockpit or a 3 rd party ETL tool), and governance.
- Identify data owners, assign data stewards.
- Extract data from Epicor and perform profiling to understand the current data quality and completeness.
- Identify duplicate, active, obsolete, and redundant records.
3.3 Explore Phase
SAP Activities: Fit-gap analysis, design based on SAP best practices, finalize scope.
Data Migration Activities:
- Define mapping rules from Epicor structures to SAP fields (e.g., UOM, Material Group, Account Group).
- Create data mapping documents for all relevant objects (materials, customers, vendors, etc.).
- Begin building migration templates and transformation logic for standardization.
- Validate business rules with functional experts and process owners.
- Conduct first mock data load with a subset of data.
3.4 Realize Phase
SAP Activities: Configuration, development, integration, unit and integration testing.
Data Migration Activities:
- Cleanse data for duplicates, missing values, formatting issues.
- Apply enrichment logic for mandatory SAP fields using rules or lookups.
- Involve business users for validation and decision-making on obsolete data.
- Execute second and third mock data loads with full datasets.
- Finalize transformation logic, load sequences, and reconciliation tools.
- Perform user validation of loaded data in QA environments.
- Run reconciliation reports to compare source vs. target record counts, key field values.
- Sign-off required before progressing to final load.
3.5 Deploy Phase
SAP Activities: Cutover planning, training, go-live preparation.
Data Migration Activities:
- Execute final cutover data load after freeze on legacy systems.
- Validate load success, perform reconciliations, sign-offs from business.
3.6 Run Phase
SAP Activities: Business operations in SAP, support and optimization.
Data Migration Activities:
- Monitor data usage and integrity in live system.
- Archive migration logs and finalize lessons learned.
4. Best Practices
- Early Planning: Address data quality from the start, not as an afterthought.
- Use Data Quality Tools: Leverage specialized tools for profiling, cleansing, and monitoring.
- Data ownership resides with business units, not just IT. Involve data owners in each validation cycle.
- Iterative Testing: Perform multiple mock migrations and validations to ensure readiness.
- Align mock data loads with major test cycles (SIT, UAT).
- Automate reconciliation and reporting for transparency.
- Maintain detailed mapping, cleansing logs, and transformation documentation.
5. Challenges
Common Data Challenges During S/4HANA Transformation:
- Legacy System Complexity:
Multiple systems with siloed or duplicated data create inconsistencies that need to be resolved before migration. - Data Redundancy and Inconsistencies:
Duplicates, outdated records, and conflicting data definitions hamper harmonization efforts. - Lack of Master Data Governance:
Inadequate governance leads to uncontrolled data entry, resulting in inaccuracies across key domains like customers, vendors, materials, and financial accounts. - Unstructured and Obsolete Data:
Non-standardized and irrelevant data increase the risk of errors and inefficiencies post-migration.
6. Risk Mitigation Strategies
- A partner be engaged right through the data migration cycle who has the expertise and knowledge to support the client end to end with data migration. Bringing in data migration partners mid way through the project introduces risks to the overall project timelines or will introduce sub quality data in the new system..
- Establish strict change control after final mapping approval.
- Maintain a single source of truth and version control on mapping and transformation documents.
- Use automated profiling and validation scripts to catch issues early.
- Build robust reconciliation reports with thresholds for exception alerts.
- Train users early to validate data contextually, not just technically.
- Realistic and multiple Mock Data Runs to:
- Validate data quality and integrity after transformation.
- Test the performance and reliability of ETL tools and migration scripts.
- Ensure completeness of mapping rules and handling of edge cases.
- Confirm all SAP configurations are ready to accept the data.
- Measure and refine migration timing and sequencing (critical for cutover).
- Provide business users with confidence and familiarity.
7. Benefits of High-Quality Data in S/4HANA
- Accelerated and smooth transformation with fewer surprises.
- Faster time-to-value through better process performance.
- Trustworthy analytics for real-time decision-making
- Enhanced customer and supplier experiences.
- Reduced compliance and audit risks.