Implementation of Incremental Reporting Services.

The majority of our opened support cases is caused by the current reporting design of IBM ELM.

The current reliance on full, non-incremental data processing for key services in IBM ELM—specifically the Data-Mart collection jobs (DCC) and the LQE re-index/validation processes—is a significant source of customer dissatisfaction. It leads to prolonged reporting outages, degraded system performance, and high operational costs from over-provisioned infrastructure and manual intervention. This hinders our customers' ability to derive timely insights from their data.

This "full reload" approach is a deliberate legacy design choice that prioritizes data integrity and simplicity. While operational workarounds like the cloned LQE server pattern can effectively eliminate reporting downtime during re-indexing, they do not solve the core inefficiencies of the poll-based architecture.

A strategic shift to a push-based, event-streaming architecture using a platform like Apache Kafka offers a definitive long-term solution. By creating a durable, replayable event log, this model enables all data services to become truly incremental. It eliminates the need for disruptive full re-indexing, provides near real-time data, and reduces operational overhead. This would transform these processes from disruptive, long-running tasks into efficient, continuous updates, resolving a major customer pain point. While this transformation is a major architectural evolution requiring significant investment, it is a direct investment in our customers' success, the business value of the ELM platform, and our long-term competitiveness.