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Over 40% of those asked indicated that DIFFICULTY IN COLLECTING DATA was their primary challenge. Difficulty in collecting data stems from several sources.
Multiple Sources of Measurements - Collecting data is made difficult by multiple sources of measurements. With operator networks consisting of multi-vendor equipment, each with its own method of producing performance data, it is not difficult to see how an incoherent mix of data provides very little value to operators. Operators must manage and optimize end-to-end network Quality of Service (QoS) and Service Level Agreements (SLAs) across multi-vendor, multi-technology networks.
Management of End-to-End QoS - Operators are challenged by the need to collect performance data for multi-services offerings - voice, video and data – supported by different technologies, protocols, equipment, and vendors. Over time, more equipment types are added and the ability to manage end-to-end QoS for a specific service
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Security is one of the chief concerns of service provider executives worldwide. Many service disruptions go undetected with a transport layer view since calls or sessions can masquerade as normal traffic. |
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Amalgamating data from multiple components offers many challenges:
- Synchronization of collection intervals across components
- Uniform aggregation and rollup of data
- Ability to audit data
- End-to-end troubleshooting
Without a viable way to correlate all of this data, operators are left with low confidence in the accuracy of the collected data, which, in turn, impacts performance analysis, troubleshooting and ultimately, profitability.
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deteriorates because each network component produces performance data locally and has no indication of end-to-end quality or the impact on subscriber experience.
Data collection, especially end-to-end quality data, becomes difficult at very best. Service providers are often forced to deploy additional bandwidth as a safety measure, which is often an unnecessary and costly proposition.
Quality of Data – Difficulty in collecting quality data can be the result of many factors, including poor data access methods, diverse data sources, types, and formats. Quality of data, which can be fully enhanced with the KPI management model, is critical to operator success. Reliable and error-free data availability is crucial to managing network-wide QoS.
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Ability of multiple business entities to leverage data internally - Data collection with quality issues reverberates throughout the entire operator organization. Business entities struggle with information integration because of the low quality of data and its ability to be delivered throughout organizations uniformly. The use of faulty data or poor dissemination of quality data can have a negative impact on an operator's short- and long-term business objectives.
The Signaling Fix for KPI Calculations
With the responsibilities of the many network components nearing capacity to support the ever-growing mobile market, operators must exploit available network-wide monitoring systems. Signaling-based network monitoring systems collect information for the purpose of generating "xDRs" – Call/Transaction/Session
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