CSPs handle data in motion to gain actionable insights

Communications service providers (CSPs) are starting to take advantage of new technology that enables them to analyse the sheer volume of their data, much of which is in motion. Data analytics tools are transforming CSPs’ businesses by allowing them to gain actionable insights from their vast data sources, write Linda Austin and Neil Lilley.

Service provider networks and systems, and other sources such as social media, produce vast amounts of data, much of which is in motion in this increasingly mobile world. Until recently the sheer volume of this data and added complexity of mobile data sources made it impractical to extract many of the key insights offered. Technological advancements, combined with the hyper-competitive and increasingly mobile world, are driving service providers to differentiate themselves by extracting and utilising this data more effectively.

LindaTo that end, many CSPs have begun making significant investments in big data analytics tools that
handle not only data at rest, but also data in motion. However, CSPs should ensure that their efforts are focused on gaining actionable insights – rather than simple exploration – to maximise the value of analytics investments. CSPs can gain actionable insights and derive the maximum return on their big data and analytics investments by:

1. Implementing horizontal analytics architectures
2. Including real-time capabilities
3. Supporting specific use cases based on domain expertise
4. Adopting closed-loop action

Delivering data in real-time is insufficient unless there is a process to convert that real-time data into actions that drive value. CSPs need to make sure they are analysing the right data, converting this data into customer insights quickly, and making use of those insights across the entire organisation in order to create value for the business. This process is what we call the insights in motion framework.

Traditionally, CSPs have focused their analytics initiatives in one of three different domains – network, IT and business. Understanding all three areas, as well as the relationships between them, is essential to helping organisations use their data effectively. This framework looks at three key components:

Quality-of-Insights (QOI): determining which information is relevant and where to get the data in order to create reliable insights
Time-to-Insights (TTI): making sure the data is immediately available to generate actionable intelligence across the organisation
Return-on-Insights (ROI): ensuring that the insights created will drive business advantages across the organisation. Understanding the potential business advantages from strategic  intelligence is what determines the QOI

NeilRecent and current investments in analytics solutions are still dominated by analytical silos; that is, analytics solutions focused on a specific type of data and supporting a specific application. Examples of such investments are probe solutions or CDR analytical tools. These vertical solutions are a natural evolution, using existing, but comparatively narrow, data sets. Taken one implementation at a time, vertical analytics solutions appear to require relatively low investments of time and expense. However, these economies disappear as more data sources become the subject of analysis, and as more internal users seek to develop applications that address their particular needs.

By contrast, horizontal solutions are designed to pull key data from multiple and diverse sources, perform pre-processing, and make this big data available to a variety of applications, each tailored to the needs of different users and use cases. These horizontal platforms lower investment of both time and money to incrementally meet the needs of various internal users and external partners.

Perhaps the greater drawback of vertical analytics solutions is that they miss many of the possible and
useful insights that could be attained simply by including diverse data from multiple sources. In contrast, horizontal analytics architectures can correlate data from all possible – network and non- network and physical and virtual – sources. For example, consider that probes can provide detailed insights into the quality of service that each individual customer is receiving. If that quality is inadequate, probe data typically cannot explain why. However, session events, performance metrics and other data sources can reveal the cause, even for an individual customer. A horizontal analytics platform can collect cause and symptom data from multiple sources, and correlate those metrics by customer identifier and session, creating a customer experience record that is valuable to several different use cases – such as customer care, operations, or marketing – and which cannot be derived from any vertical, siloed analytics environment.

Existing analytical deployments mostly consists of offline – (batch) – tools. This approach can reveal patterns and trends, which can be useful for a number of use cases, including long-term capacity planning and customer segmentation. However, newer approaches make real-time analytics possible and practical, supporting many more use cases, such as pro-active customer care or real-time resource optimisation.

Consider targeted marketing, where many use cases can be supported by offline analytics. Robust,
experience-based marketing can be supported if realtime capabilities are available and analytics are
integrated with policy, a centralised catalogue and self-care. In this case, the actual, recent customer experience can be added to the customer profile, thus allowing a more appropriate offer – such as retention, cross-sell, or usage incentive – to be matched to the individual customer based on whether the customer’s recent experience was below or above average. CSPs could even develop a Service Level Index (SLI) that weights recent experiences to better predict risk of churn as well as receptiveness to various incentives. According to McKinsey’s 2012 article ‘Using big data to boost marketing capabilities’, more than 50% of telecoms companies that conduct Customer Level Marketing (CLM) projects achieve a 10% or greater improvement in their EBITDA performance.

Domain expertise is the secret ingredient for deriving actionable insights for service providers from big data. After all, most analytics tools are akin to blank spreadsheets. Just as a spreadsheet must be configured with formulas and formats to be useful, so too must big data analytics tools such as big data storage tools or complex event processing engines be configured with data models, business rules,
thresholds and the like in order to support a given use case. In order to gain useful insights, you must know what data elements, in what combinations, and at what thresholds truly matter to the question at hand. Primary customer research, network expertise and other knowledge guide the development of these rules.

Many CSPs still have manual analytical steps to support their business. Real-time automated analytics
will enable closed-loop action, driving decisions efficiently. The greatest value is derived when big data insights are connected to business processes, thus enabling closed-loop action, where data drives insights and insights drive actions – network configurations, work orders, customer marketing offers, and so on – without human intervention. This ‘data to cash’ process is where the horizontal architecture, real-time capabilities, and tailored applications supporting specific use cases all come together to drive significant improvement in operations and customer experience, and ultimately return on investment.

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