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Data Conversion

Streamlined Application Modernization
with ROAD Data Conversion Solution

Key Strengths & Capabilities

Point and click

Standardized extraction and validation with intuitive point-and-click interface

Process

Orchestrated data conversion workflows for streamlined processing

Privacy

Data masking during development to protect personal and sensitive information

Audit

Comprehensive inventory of data sources and applications, with full audit tracking

Validation

Support for multiple snapshots to enable testing and validation before production

Transformations

Flexible data handling: mask, encrypt, transform, or derive new values

Archive

Centralized, secure repository for archiving legacy application data

Reusability

Repeatable, reusable processes—develop once, apply multiple times

Data Conversion Solution
from Legacy Applications
to Modern Applications

Click here to access the ROAD for Data Conversion data sheet.
Datasheet
Process Flow

Business Requirements
for Data Conversion

Data conversion is critical in transforming data from legacy systems to new platforms while ensuring accuracy, integrity, and consistency.
These requirements ensure a smooth, secure, and efficient data transition, helping businesses leverage their data effectively in new systems.
The business requirements for data conversion include:

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  • Convert data from a legacy application to a modern system.
  • Merge multiple data sources into a single target.
  • Accommodate the unique requirements of each application instance.
  • Support sourcing from any RDBMS system or CSV files.
  • Extract, map, transform, validate, and load data into the target system.
  • Enable reuse of mapping and design across multiple clients.
  • Document the entire process for auditing and compliance.
  • Allow addition of new fields (calculated or derived values), exclusion of fields, and field merging.
  • Facilitate the selection of specific data subsets for migration.
  • Data Conversion Orchestration.
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Challenges with Traditional Process

  • Extracts are tailored for each data source.
  • Mappings are custom-built per data source.
  • Loading procedures differ across data sources.
  • The process is manually executed via scripts.
  • Intermediate data is managed in spreadsheets.
  • Processing time can range from several days to weeks per source.
  • No traceability or audit trail is maintained.
  • Once data is loaded, source systems may become inaccessible, hindering future access.