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Unlocking Big Data for Operational Intelligence

Chris Menier
12/27/2016
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In a competitive environment where consumers and businesses have more service providers to choose from than ever, cable companies' ability to capitalize on superior networking technology and service capabilities can only be realized to its full potential if customers are confident that their quality of experience is second to none. In this sense, the customer's overall quality of experience has become cable's most important product.

The way to meet these customer experience and operational cost challenges is to reduce the impact of service impairments through preventive action and more rapid responses to issues that directly affect customers. This requires a comprehensive, highly automated and very granular approach to gathering, correlating and analyzing data from every component that contributes to overall performance, whether in the core, the edge, transport links, customer premise equipment or personal devices for services on the go.

Fortunately, the facilities, network and device technologies underlying cable's move into this new service era have been designed to accommodate the need to generate useful performance data, ensuring that operators have plenty of data to aggregate and correlate in the analytical process. As a result, MSOs are now dealing with a big data environment that requires industrial-scale prescriptive analytics to provide actionable insight into everything pertinent to assessing service performance and ensuring superior customer experience.

Such use case-driven applications optimized for cable must be able to look across all relevant points of functionality tied to any given service in real time and put this vast amount of data into context to diagnose the causes behind emerging and customer-perceived problems with minimal human intervention.

Defining the capabilities of a cable operational intelligence framework
To adapt a service operations model, cable operators need customer-aware analytics built on top of an operational intelligence framework that leverage data mining processes aggregating useful data from every available source in real time and at massive scale. The OI framework must be equipped with interfaces that support integration with all existing data platforms, including HFC telemetry, change management events, element management system (EMS), network management system (NMS) alarms, customer-care interactions and others outside the operator's domain.

With the ability to use all these resources for aggregating performance data across the end-to-end cable infrastructure, an operational intelligence framework can provide operators with everything they need to capitalize on the cost-saving and QoE benefits of pre-emptive operational efficiency and reduced response times to outages and systemic issues. Process engines in the Operational Intelligence Framework can be categorized as follows:

  • Large-scale real-time ingest: The magnitude of the collection layer tied to all these data-generating resources is far beyond what is often claimed for cable-oriented OSS systems. It must be capable of collecting massive amounts of network streaming and enterprise stored data that can scale well beyond typical enterprise platforms.

  • Data enrichment and correlation: In preparation for applying analytics processes, all the incoming data must be instantly categorized and correlated as it applies to any use of any service underway at any given moment.

  • Machine intelligence applied to anomaly detection and diagnostics: With customer-aware analytics, cable operators can establish reference baselines of aggregate system performance from the very local service level to regional and national levels. Leveraging all the information generated through the above processes, the analytics application continually performs analysis of all anomalies needing either preventive or alarm-responsive action.

  • Connected: Through a series of APIs, the discovered anomalies and solutions must be seamlessly integrated within the existing processes and support systems.

    A Tier 1 MSO's use of operational intelligence
    One example of how a cable operator is using customer-aware analytics to boost the customer experience is the strategy of a top North American MSO that wanted to differentiate between plant and CPE-based issues to minimize truck rolls, which cost approximately $80 per roll. In this case, the application has been directed to maintain an automated real-time view of baselines and anomalies across the MSO's footprint in all its regions.

    The operator is able to instantly isolate the issue before customer call volume intensifies. Once any given anomaly crosses a machine-learned threshold (rather than a pre-set threshold) in terms of numbers of people affected, duration of the issue or other parameters, the platform can zoom in on the data pouring in over a selected period of time from the affected area to perform the correlations and analysis essential to identifying the root issue.

    The operator can also direct the analytics application, using standard APIs, to automatically interact with IVR systems to create messages that will be heard by any affected subscribers who call in to customer service. Additionally, the framework can take actions on specific devices in the home; for example, it can interact with the ACS to push new firmware, reboot the device, reset factory defaults and change the WiFi channel, etc. The subscriber can be immediately informed that the operator is taking remedial action, what the nature of the problem is and when it should be fixed.

    Based on the field experiences of this MSO and others, once the operational intelligence framework is in place, about 30 percent of the truck rolls ordered with previous procedures can be eliminated. In the first year alone, the total in annual cost savings was $70 million.

    By quickly identifying and addressing root causes to issues that are having a tangible impact on customer experience in any location, cable operators can lower the costs of service visits and call center operations while minimizing the impact of performance lapses on customer experience. With a highly automated approach to identifying, prioritizing and proactively addressing emerging problems, operators can manage maintenance operations to maximum effect and sustain and drive improvements in customer satisfaction.

    A full technical paper on this topic was presented at the SCTE Cable Tec Expo 2016 in September.

    — Chris Menier, Vice President, Products & Strategy, Guavus

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