BACKGROUND

While costs associated with computers and memory storage products have been falling with technological improvements, available computing resources remain at a premium. For example, the processing of electronic communications has led to substantial increases in necessary memory usage and computing processing power. In many cases, computing centers tasked with implementing and maintaining these electronic communications are constrained by existing or aging hardware and software resources, and budgetary concerns regarding the purchase, upgrade, or repair of the hardware and software infrastructure components.

With the ever-increasing amount of data being processed, computing resources, such as memory and/or other storage, may be depleted and additional resources may need to be added to the system. Additionally, the data stored may be communicated between computing systems for processing. These communication requirements may result in slowed communications capability, as communication bandwidth on an organization's network may be a finite, limited resource.

For these and other reasons, improvements to data management capabilities, including intelligent compression of data, are warranted. The compression should be intelligent in the sense that the compression only removes data that is redundant or otherwise indicative of unnecessary information. In that way, data indicative of information of value is maintained despite the data compression.

Data compression procedures have been configured to generate data objects through netting or other offsetting. A new data object is accordingly generated. Unfortunately, a magnitude or other parameter of the new data object often runs afoul of one or more restrictions on decimal places. Mere rounding the parameter of the new data object to comply with the restriction(s) on decimal places may introduce undesirably significant error.

