How High Volumes of Data can Impact Resource Matching

In orgs with thousands of resources, those resources and their related records must be considered. When processing high volumes of data, the org must balance its performance and stability with the quality of the results.

The first sign of this is slower performance from resource matching features.

To preserve its performance and stability, beyond a certain volume the org limits the volume of data and therefore the number of resources considered. It is not possible to predict or control which resources are excluded. When the data is limited, all resources matched are correct matches, but other suitable resources might be excluded from the results.

In extreme cases, the org might be unable to return any results at all.

Resource Matching at Scale

To balance performance and stability with the quality of the results, an intelligent approach is used whereby resources that fail to match all essential criteria are filtered out and the remaining resources are then ranked for suitability. This approach allows the org to quickly narrow down and evaluate the most suitable resources for common types of request. For a more detailed explanation of the filtering and ranking steps, see Intelligent Staffing Overview.

The volumes of data to consider can get very high after the filtering step. Therefore, the lower the number of resources remaining after that step, the easier it is for the org to complete the ranking step. There is no exact rule, but you can expect the org to work best when the number of remaining resources after the filtering step is between 100 and 1,000.

This consideration must be balanced against the needs of resource management teams. These teams typically handle a mixture of negotiable and non-negotiable criteria, and must pick the best option from a number of imperfect matches. Non-essential criteria and availability play a key role in this process, enabling the org to quickly surface the resources that are most likely to be a good match.

To ensure that your org efficiently processes a large number of resources, there are best practices that you can adopt. For more information, see Improving Resource Matching Performance at Scale.