The goal of quality maintenance management is to maintain the relevant information that is considered of value to an organization. This should be evaluated according to the particular business objectives of the concerned organization combined with the management policies and technical requirements of that organization.
The key to advancing this quality of the collected and acquired information is by: Building a layered structure of management information to be derived from the understanding of existing network information resources.
Knowing what is valuable according to a pre-planned structure of network information resources should lead to the exclusion of maintaining less valuable information. Un-managing less valuable information will have the added value of freeing more resources towards maintaining valuable information while alleviating the shortcomings caused by spending valuable resources on managing less interesting and less valuable information.
It is essential that the decision of what information to manage be totally up to the concerned organization that owns the information resource pool. This is a significant shift from the traditional vendor driven management solutions where organizations would have to adhere to the vendor’s solutions and only decides on the outcome and whether the results are useful or not. By this time of course management resources have already been expended.
Two motivations should drive the Quality of managed information to be collected by a particular organization. One is by solving a business problem or increase efficiency of the current methods of accomplishing work while reducing overall costs. If the solution doesn't save money while providing a better service, it probably isn't worth accomplishing. The other is for the Quality of managed information to be structured around the real work processes within the organization. Hence it should focus the tools toward those staff members supporting the managed area in a manner which makes their job easier and faster.
The goal of the Integrity maintenance management is to look for and to implement ways to preserve the collected data for as long as it is necessary and indeed useful as per the organization’s requirements determined by the network administrators.
NMS systems databases typically and rapidly grow very consistently. The rapid growth of database volumes could deepen the gap between data generation and data understanding. Hence Summarization is necessary as it reduces a large number of actual database tuples into a relatively small number of generalized descriptions.
Summary data is manufactured by analysing and manipulating detailed data to derive new technically refined (more meaningful) or business data. The data we capture about devices, such as their names, IP addresses, images running and traffic usage, is detailed data, as is the data required to record faults and maintain inventory, and respond to events. The data used to assess how well the device is performing, such as link utilizations, and resource usage per running services is in nature summary data.
Summary data often represents the aggregations or counts of detailed data across important technology or business hierarchies.
Historical data represents the state of the network at specific point in time. Consequently, historical data is non-volatile. Once the snapshot has been taken, the values are never changed, so that the image of the network reflected in the snapshot is never lost. Snapshots are taken regularly, such as on the last business day of the month, which allows the network administrators to compare the values in different snapshots. Some of the snapshots could be designated as baselines and stored as is without any additional summarization to enhance the value comparisons.
Future data is a projection of how the network will look at a future point in time. A network may have a number of different projections for the same future time frame to represent various scenarios or topologies maybe. Each projection is based on a set of assumptions about how the network environment will evolve. For example, you may develop alternate projections to study the impact of different link utilization growth rates on the VLAN network design topology. This future data is used as a metric against which historical data is analysed.
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