RG Solutions® Features - Data Formation

User Defined

The user defines the

content and the extent of

the processing. Data

Formation is basically a

parameterized set of rules

that are used for the

formation of the data, and

these rules are triggered

automatically upon the

arrival of data.

Intelligent Assembly

RG Solutions® has its

intelligence inbuilt into the

product processing and is

directed by the user. The

processing understands

performance, financial, and

operational data and is

able to “blend” this

information that links IT

service costs to the

business consumer.

Integrated Processing

RG Solutions® relies on a

variety of sources to form

the business centric cost

information. It has the

ability to include and

merge customized user

data along with standard

performance data to create

the higher level of business

metric needed for IT Cost

Transparency.

RG Solutions^® Features:
Data Formation

RG Solutions® has extensive means for the formation of data within its processing environment. The user has control over the settings, rules, processes, and scope of data formation. The processing can be done manually or set up for unattended processing on a periodic basis.
Major Data Features
Imports	This is external data that is brought into the processing environment by the Import Processing. The transformation of the data is dependent upon the processing present in the corresponding Collection. The included types of data are dependent up on the user selection and processing options.
Hybrids	Hybrid Data is data that is constructed from information that is already contained in the repository, service, or business process. Generally speaking, this process is used to combine two data types to form a third (hybrid) data type.
Extract	Extract Data is similar to imported data, but the source of the incoming data is repositories, services, and business processes that are the offspring of the entity. This processing is usually used for the consolidation of similar types of processing.
Archive	Archives are the consolidation of Data Sets into a more summarized form and may include Week, Month, and Year based consolidations. Archives contain both Container and Summary Data.
Recreate	All data stored within the processing environment can be recreated using revised information. It can be done to add additional data from the incoming sources, or when data processing values have changed and the data must be reformed.
Major Processing Capabilities
ABC	The internal processing uses Activity Based Costing techniques for the allocation of usage. Generally, the allocations are determined by the processing based on the user defined requirements. ABC is also used for the usage allocations based on system sharing.
Workloads	Workloads are a method of classifying the actual system processing into different groups of similar processing. These groups could be business oriented or they could be IT oriented. There can be more than one classification requirement and the program processing is designed to handle multiple classification processes.
Sharing	The processing environment supports system sharing. This type of sharing is when a system is using the information from one or more other systems. The user defines the sharing relationship and the system processing automatically determines the shared usage.
Automatic	The creation of data within the processing environment can be done automatically or manually by the user. For automatic processing, the program allows for a Service or a Scheduled Program to complete the needed processing.
Rule Based	The formation of workloads and other user defined values is done using a rule based table. The table resembles a spreadsheet and contains the “lookup” values needed by the processing. When the processing matches a rule to the data, the rule will contain information used by the processing to fulfill the requirements.
Metering	The internal processing uses a metering framework for determining usage values. A Metering Framework uses a combined value of power over time to measure processing usage.

Major Data Features