It is surprising to note that even top tier\/technology leaders cannot accurately predict where and when capacity issues will impact their networks. \u00a0This lack of visibility hurts CSPs considerably because as per our own studies, network events are behind can account for up to 50% of customer churn in high value mobile data services.<\/p>\n
![\"\"](\"https:\/\/dev.enki.studio\/test\/wp-content\/uploads\/2012\/03\/capman.jpg\")
And the Capacity Management problem doesn\u2019t really end there; in many ways it\u2019s like a supply chain process. Marketing owns the function of forecasting where service uptake will drive capacity needs across the network. When Marketing underestimates service uptake, there is a real and significant impact to potential revenue: On average, it can take about 3 months from when capacity is fully tapped in a Central Office (CO) to when new capacity can be added to your network.\u00a0 During that time, customers expecting service availability become hugely frustrated and begin to churn.\u00a0 Engineering groups are pushed into panic-mode, trying to react as fast as possible – often putting capacity in the wrong places due to inaccurate data – resulting in further congestion, service degradation, an inefficient use of capital.<\/p>\n
The message from CXO\u2019s is crystal clear \u2013 there is an urgent and dire need to find new ways of monetizing the data crossing their networks. This need is exacerbated with OTT content and net-neutrality. SLA and authentication based revenue models are absolutely dependent on knowing what types of content\/services are traversing your network, how much capacity they consume, and how utilization is driven by your consumer\u2019s interests and activities.\u00a0 This type of analysis requires a critical and intelligent binding of network and services data with business data to truly assess the financial impact to the CSP. Many Business Intelligence (BI) solution leaders will lay claim to abilities here, but actually fall very short of the mark.\u00a0 Instead, real experience suggests that solutions in the marketplace today either:<\/p>\n
- \n
- Can handle the financial aspects of your business but have no understanding of today\u2019s network dynamics in terms of capacity issues and services;<\/li>\n
- Can handle parts of your network very deeply, but do not correlate or provide a holistic view at the service level; or,<\/li>\n
- Can collect some network and service level information, but have no ability to incorporate business data to understand the impact to the business \u2013 i.e,. cost, subscriber behavior, propensities<\/li>\n<\/ul>\n
All the above challenges bring us to the inevitable question \u2013 what kind of approach does one take in order to tackle capacity management issues? How does one stop chasing traffic and focus on flattening the CapEx curve instead? In order to attain \u2018Capacity Management Nirvana\u2018, a proactive and scalable approach needs to be adopted by CSPs. An approach which not only intelligently binds network and business strategies based on the Data Tsunami realities but also brings proactive and predictive capacity management to the table. At the end of the day, a CSP should have access to all their capacity, the ability to leverage real and immediate feedback on the change in capacity as service uptake increases, and finally, the right tools and intelligence to get in front of what\u2019s coming.<\/p>\n