Thursday, 14 December 2017

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
organisational challenges
o fear of revealing all the data from a specific manager business unit to the rest of the organisation (‘Big Brother’ syndrome) 
o lack of coordination in building data lakes
o too high expectations of higher management vs organisational capabilities
 
Another aspect is the quality of gathered data, as any decision which is based on the wrong data - by definition - will be wrong. Financial data, which is typically the first information to be gathered, is usually of a high standard because of legal regulations for financial reporting. However, if we want to perform a more sophisticated analysis, which is focused on the optimisation of the operational efficiency of an organisation, the quality of the gathered data is often poor and unreliable. 
 
All of this brings us to the conclusion that the only approach which might have any chance of success is the ‘small-steps’ approach. So instead of having big projects focused on gathering all the available data in one data lake (which could quickly become a data swamp), let’s define a few use cases which require a small amount of data from different CSP departments and which have the highest possible ROI of interest for all data owners. 
 
This will help to establish an initial coalition of data owners who will see a benefit, and not a threat, in revealing their data. If the realisation of these first, wisely selected use cases are successful, then we can expect a snowball effect, as other stakeholders start to think creatively about the utilisation of delivered analytical platform supporting different use cases.
 
This proposed approach is aligned with the well-known paradigm of putting tools, people and processes together in order to reach operational excellence. 
     
Operational Excellence
 
 
But it seems that a lot of organisations have forgotten how to implement this paradigm in their own organisations. The implementation should be aligned with some common sense, and will require some time, effort and patience from higher management; it is not just an implementation of a new tool, but a change in the mentality of the organisation triggered by new technological capabilities and evolving market business needs.  CSPs who do not understand this new digital economy force may soon become a division of OTT or other ICT company as a simple connectivity provider.
 
Summary
 
Operational savings
 
With an enhanced focus on data, CSPs should expect to be able to run their customer support functions more efficiently as the data will show them exactly where the problem areas lie. 
For example, telecom operators will be able to analyse network issues in real-time and use data to focus on improving services, and then offering the support in the areas that have the biggest impact on the business. 
Using the data, these operators will be able to predict network failures and increase bandwidth as needed. By collecting and analysing all of the data that passes through their systems, operators will be able to achieve things that are almost impossible to do manually.
Data driven efficiencies will be available across internal teams, leading to greater employee satisfaction as their work processes are optimised and the increased use of data facilitates more collaboration between co-workers, teams and department
 
Capital expenditure savings
 
CSPs are also looking to data to decrease their spending on upgrading internal systems. By analysing data, operators can ensure their network infrastructure development is done efficiently and within budget. 
 
Insights derived from the data will also help them create internal systems – such as those used by customer support and operations – that are built to purpose and can adapt to changing customer demand.
 
Increased revenue
 
CSPs have access to rich sources of data and they’ll be looking for ways to monetise this data to tap new revenue streams for the business. CSPs will start to evolve from communication service providers towards more specialised data service providers. 
 
Data analytics can be used to discover what people want from communication service providers, allowing them to develop more tailored services. CSPs will use customer data to improve levels of customer satisfaction and improve churn rates.
 
By analysing the data, CSPs will be able to see the peaks and troughs of demand. For example, network operators will be able to bolster their networks, providing stronger support for the service where and when the demand is highest.
 
While CSPs may also look to sell the data, (for example, local authorities could use it to track the movement of commuters through a city for transport planning, while sellers of advertising space could use it to create more sophisticated billboard tariffs), the laws on this vary from country to country. At the very least, the data would need to be anonymised.
 
Enhanced business development
 
While CSPs have the raw data, they need to develop the analytical software and expertise to analyse it. What’s the data telling them? What trends are developing? How can the data be applied? What are the use cases?
 
CSPs will be hoping that data can help develop and sustain self-organising networks, which will help automate performance monitoring and bring cost savings that communication service providers need if they are to remain competitive on price while making a profit.
 
By specialising in data provision, CSPs are preparing to service the demand which will be placed on their networks by connected devices. They’re future-proofing their service.
 
CSPs have high expectations for data. To remain competitive, they need to find ways to cut expenses, while providing a high-level of service – while not levying excessive charges on their customers. While this is possible with data, CSPs need to either partner with specialists or develop their own expertise in analysing the data to discover the insights that it reveals. 
 
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