The Customer

Customer is the third largest Telecom Operator in the United States. The company provides broadband, voice and wireless services to consumers and businesses across the country. The company offers advanced entertainment services, data, voice and managed services to business, government and wholesale customers in local, national and select international markets through its high-quality advanced fiber optic network and multiple data centers. The company has 17 million access lines, 5 million broadband customers, and 1.4 million video subscribers across 37 states with 190,000 -mile fiber network.


Prodapt was supporting Legacy billing applications for our Client allowing their staff to participate in the development and implementation of the Ensemble billing application. When it came time to move to the new billing platform Prodapt managed all of the conversion activities from the source system including UAT testing.


  • Documentation on network and systems was not clear.
  • Interactions with onsite teams were quite challenging and demanding
    due to multiple time zones – CET, PST & EST.


Based on Prodapt’s Migration methodology, the conversion team developed a detailed data conversion strategy, UF conversion plan and design document for converting all the customer data to Ensemble.

The team identified all relevant data sources and undertook the initial data profiling and cross-system data analysis to design an effective plan to migrate the converted universal files to ensemble system.

The data conversion phase included extracting and loading the data and converting it into Universal File (UF) Format and then loading it onto ensemble application.

UF Conversion Process

The UF conversion process included copying the data from the FTP and loading it to client’s mainframe environment. The raw data was run through the conversion UF jobs to produce Ensemble UF files. During the process of UF conversion, a cross reference file was built to determine the BANs for new accounts and changes were converted to Ensemble price plans and charge codes through a mapping sheet that was supplied by the team.

Some of the important tasks involved during the UF conversion were
  • Data Mapping and Field-to-Field Mapping
  • Conversion Coding
    1. Cross reference file
    2. UF files for different data segments
    3. Error and warning reports for UF
  • Data Balancing Act
  • Issue Resolution and Retest of converted data

The team processed the UF files and created Oracle Tables. Scripts were run in validator and processed to check for errors and warnings such as referential integrity, Tables validation, Account Balance and others. The conversion team sent the report of the validator and integrity checks to the UF team to review error and warning.
A procedure for data balancing from legacy to Ensemble was performed to check if all the records were received. The data balancing procedure was completed and validated for the final conversion of the data into the production tables. The record counts and summary totals produced by each program UF were checked thoroughly and processed accordingly. Errors and issues generated during the conversion were reworked as per the plan.
After checking errors and issues, the team loaded the converted data onto ensemble system. The team also verified if ensemble system received all the records created by the extract programs. The UF files tested were transferred
to an environment where the conversion team were able to check and validate before loading the data into Ensemble database.

Testing team completed all the test cases, system testing and UAT testing. Most of the artifact including input master files, spreadsheets, output files from the extract programs, ad hoc reports and SCLM libraries for programs, JCL, Control Cards, Copybooks and VSAM define parameters were stored for future use. The team proactively documented the migration process along with the challenges faced for the ease of future conversions / migrations.


  • Millions of customer records were migrated to the ensemble system on time and budget.
  • Thanks to the effort spent on profiling the data and performing data model analysis, the data migrated was 95.5% accurate
  • Zero business disruption during the entire migration process – adhered to planned and scheduled system downtimes


  • IMS/DB
  • CICS
  • Cobol
  • Focus

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