Bloomberg Scheduled Mode: Will it reduce your spend?

Bloomberg have introduced, in addition to the two delivery modes of Bulk and Ad Hoc, a new commercial model for static data, referred to as Scheduled. Similar to the Ad Hoc mode, the Scheduled mode is a request-response service provided to offer optimised access to large data requests that do not frequently change. In practice, the Scheduled mode is a variation of the Ad Hoc mode with a fifteen minute minimal request scheduling time and a different commercial model. This means that Bloomberg require that a request be placed at least fifteen minutes prior to the response being needed. The commercial model for Scheduled mode is based on pre-agreed quotas per asset class and data category.

Clients can of course, have both modes (in addition to the Bulk mode) and use them in ways they see fit and within the constraints imposed by the data vendor for costs and response times.

We have been approached by several customers wishing to understand the impact of using the Scheduled mode on their data costs. As XMon models the Scheduled commercials, the simulation is executed in seconds within the engine.

XMon provides a hassle-free, detailed analysis of the Scheduled mode at the click of a button

The analysis of the Scheduled mode is done through our predefined report STD18 which runs a what-if analysis for this commercial model for any time period. In seconds, customers are able to see whether they would spend more, or less per data category and of course, a tallied-up global saving or loss.

The report extract below is an example showing an overall saving of 18% over the Ad Hoc mode for this example customer.

XMon Bloomberg Scheduled Mode Simulation

As mentioned above, the model requires technical changes as well so the move may involve changes to underlying application calls.

XMon provides a wealth of functionality to simulate, control and understand data costs. Reach out to us for more information about the Bloomberg Scheduled mode or to simulate this delivery on your data costs.