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Learn how to manage growing IBM Sterling Order Management data on IBM Cloud.


Note: Growing order data – Is it really an issue? This blog is Part 1 in a series.

The IBM Sterling Order Management database grows as sales increase and as new business units and channels are on-boarded to utilize a common business process. Usually, an upsurge in transactional data volume is observed over time, and this growing data becomes a problem for customers. The sales order data can grow significantly large for relational databases to provide an optimal response. A large data volume not only slows down the read and search mechanisms but also contributes to degraded performance of other transactions like inserts and updates.

In order to continue taking orders at a faster rate and address the degraded performance, read operations can be moved from the main transactional database to an efficient and more agile datastore. The active transactional database can then be trimmed down to house relatively small transactional data. For example, retain transactions that are still in the fulfillment process or transactions that are based on certain pre-defined criterion, which is justifiable with business use cases. This approach not only improves the performance of the reads but also improves the inserts and updates to the transactional database with reduction in the data volume.

Data stored in an external datastore is still accessible as readily as online data and benefits over purging and storing data on tape. As an option, this external datastore can optionally be subdivided. For example, by type of data or by time (sales orders older than X years).

A similar approach can be used for other data sources like mainframes to provide an enterprise-wide order search service.

The following diagram shows a high-level view of putting this all together to manage growing data in IBM Cloud.

High-level view

Some common use cases for order transactional data are as follows:

  1. Get order (definition, complete details, etc.)
  2. Get order status
  3. Get order history
  4. Search orders

This solution can be extended to other types of data like opportunities and quotes.

Up next: Part 2 shows an implementation strategy for this approach.

Tushar Agrawal