Saturday, 17 November 2018

From CSP To DSP: How CSPs Can Evolve To Beat The Competition - Part 2

A Knowledge Network Article By Comarch
Friday 03 November 17

In the first part of this article, we looked at how CSPs (Communications Service Providers) are evolving to meet competition from other business models. In this second part, we look at how CSPs can make use of the data that they accumulate. CSPs across the world have launched a number of initiatives using attractive ‘buzzwords’ such as data lake (a storage repository of raw data), data ocean (a vast collection of un-modelled data from a single business), data federation (software that aggregates data from many disparate sources) and so on. But many of these are quite disappointing for two main reasons:    • technical challenges o the difficult and costly integration of data currently stored in different systems and different formats o too low granularity of gathered data (eg too low frequency of generating source data, too small geographical coverage and too high volume of generated data) o the budget is too small for such a project o wrong selection of technological stack which is not able to fulfil the business requirements of the sponsor &bull…

In the first part of this article, we looked at how CSPs (Communications Service Providers) are evolving to meet competition from other business models. In this second part, we look at how CSPs can make use of the data that they accumulate. CSPs across the world have launched a number of initiatives using attractive ‘buzzwords’ such as data lake (a storage repository of raw data), data ocean (a vast collection of un-modelled data from a single business), data federation (software that aggregates data from many disparate sources) and so on. But many of these are quite disappointing for two main reasons:    • technical challenges o the difficult and costly integration of data currently stored in different systems and different formats o too low granularity of gathered data (eg too low frequency of generating source data, too small geographical coverage and too high volume of generated data) o the budget is too small for such a project o wrong selection of technological stack which is not able to fulfil the business requirements of the sponsor &bull…

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