Over time, data standards have been implemented and mixed across applications and databases, making change of any kind risky. Our Field Expansion solution reaches across the millions of lines of code that comprise your applications, both online and batch. It locates all field elements and group levels that might ever hold the data contained in an expanding field or column. Then, regardless of each field name, data type or current length, it applies controlled, standardized change tailored to your specific needs.
Mainframe Field Expansion
Why automate field expansion?
CHALLENGES
Underestimated effort
Application logic and data field definitions both require extensive adjustment to accommodate processing using fields that require data type changes. This can be a time-consuming, daunting task.
Combining forces
Multiple databases and the applications that access and support them must be consolidated as part of standard merger and acquisition activity. This can be costly without standardized change and correlating adjustments to business logic.
Complexities of scale
Expanding fields and changing data formats become more difficult as systems grow, and growing systems are the exclusive targets of field expansion initiatives.
Mainframe Field Expansion
OUR SOLUTION
Our Mainframe Field Expansion engagements begin by establishing an inventory of the prime database columns or fields that will change. We specify the data types and lengths for each, and defining boundaries for the application of these changes across the enterprise.
We also review phased approaches to change with customer stakeholders and determine a disposition strategy. A single-phased approach allows all changes to be made and tested as a single process. A multi-phased approach requires bridges to be constructed and de-constructed between the applications, or data replication to accommodate phased implementation of the data and application changes.
Upon confirming disposition strategy, we specify data types, lengths, and boundaries for change in our Field Redefinition Workbench. The data origin definitions with target field lengths and data types are then used to establish a comprehensive Change Specification.
Once the origin of data elements is mapped alongside the target field lengths and data types, the next step is to assess the context of the adjustments outlined in the change specification. We perform an automated impact analysis to identify all references to the prime fields within the source code and all related fields in programs across the environment. This process provides our customers with the ability to customize and track statements of interest, and the option to establish boundaries to limit the scope of changes, and to flag generic fields to be ignored.
Following the review, adjustment, and acceptance of the impact analysis, modifications are applied to the prime fields and to all related program variables. Next, the conversion engine changes all related field definitions according to the application flow, without regard to field names. MOVE, IF, and PERFORM statements are modified to accommodate data type changes.
Our tools group the modified components according to your naming standards for simpler introduction back into your configuration management system. It also allows cross-functional teams to easily identify requirements for QA and UA testing across various applications.
At this stage of a Mainframe Field Expansion engagement, additional tweaks are not uncommon. Therefore, we produce change rule reports for application and business teams to review, refine, or veto. Any resulting rule changes, customizations, or field expansions identified are applied to the change specification. Then, the impact analysis and conversion steps are repeated and any components affected by the revised rules are re-delivered within hours.
The powerful combination of automation, control, and customization offered by our Mainframe Field Expansion solution results in a project that is virtually pain-free, positioning your company to continue to grow for many years to come.
Over 35 years of mainframe modernization experience
More than 500 successful modernization projects completed
Over 2.5 billion lines of code refactored through automation
Progressive needed to accommodate an increasing number of policyholders and meet the business needs for expanded status, form, and address fields in their databases and related applications. This involved more than 30 database columns and 14 million lines of code.
Progressive
Casualty Insurance Company
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