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Figure 1 shows an operational data store (ODS) network, with bi-directional arrows representing the flow (replication) of information between the ODS and point-of-sale (POS), gift registry, inventory, strategic activities for a decision support system (which includes ad-hoc queries, reports, and dashboards), sales promotions, and rules engine (which sends the data to tactical, rules-based applications).

In this architecture, any application can publish, subscribe, request, and reply to the ODS, which consolidates, extracts, and transfers rich, up-to-date information from any other part of the network.

Figure 2 shows the evolution of the ODS, from its early stages (top of the diagram) to data consolidation (present day, bottom of the diagram).

In its early stages, an ODS consisted of separate tactical and strategic systems, populated via periodic batch updates.

In concept, the ODS, which contains all of a corporation’s data, could become the database of record, the single version of truth.

This action generally does not happen because of regulatory requirements and other considerations and the database of record remains on the existing systems, where it was resident for decades.

It is conceivable that RTBI systems may evolve eventually to ODS systems.In the early 2000s, the Gartner Group coined the term zero latency enterprise (ZLE).In its words, ZLE was “the instantaneous awareness and appropriate response to events across an entire enterprise.” This response was later renamed the real-time enterprise (RTE).On the one hand, it must be able to respond to complex queries from knowledge users, data-mining facilities, and rules engines using online analytical processing (OLAP).The database structures suitable for OLAP are characterized by fat keys that allow rapid searching of the database to respond to complex queries.

These warehouses or application networks may or may not turn into an ODS as consolidation occurs.

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Model updating using operational data introduction

Model updating using operational data

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