Smart Ingest Solution for Telecommunications

Transcription

1 Smart Ingest Solution for Telecommunications White Paper Author: Ben Woo Neuralytix, Inc. Doc#: Published: 2/24/2014 Last Update: 2014 Neuralytix, Inc. and/or its affiliates. All rights reserved For more information, or visit

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3 Neuraspective Big Data is an ecosystem that stems from historical and predictive analytics to real-time critical analyses and responses. Neuralytix believes that real-time systems will be the prime focus of Big Data customers as users look to create as much differentiation as possible. In the telecommunications industry, it is all about sustaining service levels. By ensuring high service levels, carriers can increase revenue, reduce customer churn and optimizing network operations. Hitachi s streaming data platform is a leader in helping the telecommunications industry to deliver, improve and extend service levels to its subscribers. Key Findings Real-time and right-time are different; both are necessary. Customer churn can be as costly as trying to acquire 10 new customers for every lost customer. Telecommunications carriers need to deliver different service levels over the same network to improve profitability. Over-the-top (OTT) are keys to differentiation and profitability, but requires predictable, reliable and sustainable networks. Neuralytix, Inc. Doc#: Page 3 of Neuralytix, Inc. and/or its affiliates. All rights reserved For more information, info@neuralytix.com or visit

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5 Table of Contents Key Findings... 3 Overview... 6 Real-time versus Right-time... 7 Big Data Analytics... 7 Real-time Analytics... 7 Going Over-the-Top (OTT)... 9 New Revenue Generation Churn Reduction The Hitachi Streaming Data Platform for Telecommunications. 12 Smart Ingest Continuous Monitoring and Learning Continuous Query Language (CQL) Increased Dependencies on Telecommunications Networks Conclusion About the Author About Neuralytix Neuralytix, Inc. Doc#: Page 5 of Neuralytix, Inc. and/or its affiliates. All rights reserved For more information, info@neuralytix.com or visit

6 Analysis Overview Big Data has many uses across all industries. In particular, the telecommunications industry can benefit in numerous ways to help it to accelerate growth and profitability. One specific challenge to the telecommunications industry is the dynamism in customer requirements. These include provisioning, service levels, customer service and network efficiency just to name a few. The end goal for telecommunications companies are: Create new revenue opportunities; Reduce customer churn; and Reduce cost while improving operational efficiencies. Traditional Big Data analytics help in the longer term, strategic nature of achieving the goals listed above. However, in many instances, Big Data analytics is simply too slow to react to this vast competitive market and the needs of their customers. Instead, telecommunications companies have turned to analysis of real-time streams of data. This enables the telecommunications provider to react immediately, and in some cases proactively, to optimize their networks and minimize possibilities of outages and consistently meet and even exceed service levels, creating opportunities for new revenue generating services. Page 6 of 16

7 Real-time versus Right-time The difference between real-time analytics (processing of a data stream on an immediate basis) as opposed to right-time analytics (processing of data that is collated first, and processed in batches) is a matter of time and granularity. Big Data Analytics Big Data analytics, many which deploy data management platforms such as Hadoop and other NoSQL databases, area right-time analytics platforms. These analytics platforms gather up massive amounts of data and process it in parallel to produce a holistic view of a particular environment. These types of analytics are very useful in understanding the overall picture of an enterprise, or specific operations. The output provided can help telecommunications executives to make draw conclusions regarding the provider s overall ability to meet service levels (e.g % uptime across the network). It can also help them isolate areas for improvement. What it does not do, is provide the granularity of real-time analytics. Real-time Analytics Analysis of real-time (or streaming) data can provide very specific response. For example, a particular high value (or high margin) customer, may not be receiving the bandwidth promised. This may not show up in the Big Data analytics as it provides a much broader view of the network. The network may indicate that across all customers (within a given purview), that all service levels are met Neuralytix, Inc. Doc#: Page 7 of Neuralytix, Inc. and/or its affiliates. All rights reserved For more information, info@neuralytix.com or visit

8 this may be a result of overprovisioning of one customer, but underprovisioning of another. That is not to say Big Data analytics are wrong, it is just does not provide the granularity required. Telecommunications providers need both streaming and Big Data analytics. Hitachi s streaming data platform has proven itself successful in providing telecommunications providers with the necessary realtime streaming analyses necessary to achieve the major business objectives listed earlier. Since so many advanced services are dependent on telecommunication networks, the ability to monitor, react, and improve operations from edge-to-core in real-time moves from necessary to critical. While the telecommunications industry is a prime consumer of realtime technologies, real-time criticality extends to other adjacent industries such as retail and financial services. Investment banks and hedge funds rely on both the real-time capabilities of the telecommunications networks as well as the exchanges all over the world to deliver data so that critical trades can be made in fractions of a second. Delays could amount to millions or billions of dollars in lost productivity. Retail customers make up their minds quickly from the time they visit a retail store (or online store) to deciding whether they will stay, browse and/or make a purchase. Page 8 of 16

9 Real-time analytics are critical in understanding the profile of each individual customers, so that service levels can be set, loyalty recognized to improve the chances of the customer making a purchase. Going Over-the-Top (OTT) While the provision of telecommunications services have been highly democratized over the last several decades, one area that service providers can differentiate, are the OTT services they offer. These may include the inclusion of real-time video service (e.g. Netflix or Hulu), rich audio services, and real-time data and information services. These OTT services are mission critical. Many customers are paying a premium in order to receive these services. While the relative value of Netflix versus receiving real-time business data that may lead to a large deal can be debated, the customer (whether consumer or enterprise) still associates value with the premium paid. Smartphones have increased both the data and information convergence and the reliance by individual customers on its telecommunications provider. Recent data shows that revenues associated with voice is down, while data and video is up. Smartphone LTE traffic is increasing, but the price of bandwidth is eroding. OTT players now have a choice of providers. Securing partnerships with these OTT players can be lucrative for telecommunications providers. Neuralytix, Inc. Doc#: Page 9 of Neuralytix, Inc. and/or its affiliates. All rights reserved For more information, info@neuralytix.com or visit

10 New Revenue Generation Streaming data analyses can ensure quality of service (QoS) and therefore provide consistent performance over the network to ensure proper delivery of OTT services to premium subscribers. Real-time analyses can also provide the necessary adjustments to the network to proactively adjust and optimize the quality of experience (QoE). Hitachi s streaming data platform, in conjunction with JDSU s PackPortal, delivers the granularity and fine-grain analytics for OTT services like video broadcasting. Through real-time analyses of the network, Hitachi s streaming data platform is able to provide immediate identification and resolution of network issues that may affect the QoE by customers in many cases it can also anticipate potential issues and remedy them before customers experience any impact. The continual learning that Hitachi s streaming data platform provides allows the service provider to have the necessary granularity and visibility into the content provided by the OTT player as well as the customer experience to ensure it meets the service levels agreed to by the customer and the provider. Churn Reduction Churn is the biggest obstacle facing service providers today. Commoditization of underlying network service augmented with accommodating and managing explosive growth while maintaining (QoE/QoS) exponentially increases pressure on service providers to ensure optimal network operations. Page 10 of 16

11 Neuralytix believes that for every customer that churns, there is a flow on effect. We believe that for every churn, as many as five other customers will choose a new carrier at the next opportunity. This problem has an additional compounding effect. For every churn, the carrier has to replace essentially six customers. That is in addition to new revenue that it needs to generate, augmented by any perceived negative sentiment resulting from the churn. Since customer acquisition is more difficult that customer retention, Neuralytix believes that ultimately, for every customer churned, the carrier has to expend over 10 times the effort necessary to replace the initial lost customer, and simply be back to square one. Given the highly competitive market in which carriers participate, this can have a devastating impact on the carrier s ability to sustain growth and/or profitability. In the example listed above addresses specific customer experience needs. However, the backhaul provider and network require the equal level of granularity and visibility (and therefore, by extension, the analyses) necessary to handle the explosive traffic volumes that providers are experiencing to day. Hitachi s streaming data platform is able to handle the volume of complex events that mobile operators and backhaul providers generate. The real-time analysis provided by Hitachi s streaming data platform ensures effective service utilization and QoS across the backhaul links even before the subscribers experience any loss of QoE on the edge. Neuralytix, Inc. Doc#: Page 11 of Neuralytix, Inc. and/or its affiliates. All rights reserved For more information, info@neuralytix.com or visit

12 The granularity and visibility provided by Hitachi s streaming data platform allows operations staff to improve time-to-resolution and pinpoint individual service disruptors (such as individual packet loss) to provide monitoring and automated adjustment of the network configuration, sizing and design to optimize service delivery. The Hitachi Streaming Data Platform for Telecommunications Hitachi s streaming data platform has several key differentiators that make it an optimal solution for the telecommunications industry: Smart ingest; Continuous monitoring and learning; and Continuous query language. Additionally, Hitachi s ability to build a streaming data platform partner ecosystem e.g., the partnership with JDSU, creates unique solutions. Hitachi s streaming data platform complex event processing (CEP) connects to JDSU. Essentially, the combination allows a carrier to monitor every aspect of its operations from it backend all the way to the edge. It is the edge component that most carriers have the most problems, especially given the variable pricing, service level agreements and expectations of each individual subscriber. Page 12 of 16

13 Figure 1: Key Functions of Hitachi s streaming data platform (Neuralytix 2014) In reference to Figure 1 above, Smart Ingest A big part of the success of Hitachi s streaming data platform is due to its smart ingest capabilities that allow it to provide real-time alerts and visual insight into the stream of incoming data (Step 1). Smart ingest also allows the data from the stream to be analyzed and action taken immediate upon ingest (Step 2). These actions are based on policies that are continually updated and maintained by Hitachi s streaming data platform. Continuous Monitoring and Learning A key part of Hitachi s streaming data platform is the ability to interact with data outside of the platform. This data may be held in Neuralytix, Inc. Doc#: Page 13 of Neuralytix, Inc. and/or its affiliates. All rights reserved For more information, info@neuralytix.com or visit

14 consolidated traditional Big Data datasets, such as those managed by Hadoop (Step 3). This data is used to provide the intelligence (Step 4) to update continually the policies and actions necessary to respond to the rapid stream of data being ingested. Continuous Query Language (CQL) While standard query languages (such as SQL) provide the ability to query databases, querying an incoming stream of data is very different. Hitachi s streaming data platform provides its continuous query language (CQL) that enables telecommunications providers to make in-memory queries of data in order to achieve the high performance and real-time characteristics necessary. CQL also provides the ability to query data in sliding time windows so that analyses are not bounding by time-series of the data streams, further enhancing the granularity and performance of the platform. Increased Dependencies on Telecommunications Networks As the number of location based services increase, as well as the individualization of programs offered by other industries, the dependency on telecommunications networks will increase. This will put additional pressure on an already stressed network. Without solutions such as that offered by Hitachi s streaming data platform and JDSU, it may be impossible to meet all the needs of end-users. Page 14 of 16

15 Use cases that are dependent on telecommunications networks include location based marketing campaigns, mobile healthcare providers, life sciences research, automotive capabilities, fraud detection and law enforcement. These are only the more obvious use cases! Conclusion The dependencies on telecommunications over the next several years will increase exponentially as the number of data creation and consumption devices increase (e.g. Internet of Things, smart meters, smartphones, etc.) With a hyper-competitive market, carriers need to differentiate in three areas: Deliver new revenue generating services; Proven predictable, reliable and sustainable network performance; Optimize network design and provisioning as the number of subscribers and the number of service levels grow. Neuralytix believes that Hitachi s streaming data platform solution provides a stand-out platform for complex event processing. When combined with the JDSU probe, the solution addresses the highpressure environment of the telecommunications industry. The unique solution will offer the ability to reduce risk of customer churn; open new revenue opportunities and help proactively optimize the edge-to-core design and provisioning to ensure optimal performance. Note: Neuralytix, Inc. Doc#: Page 15 of Neuralytix, Inc. and/or its affiliates. All rights reserved For more information, info@neuralytix.com or visit

16 All rights for trademarks and names are property of their respective owners. About the Author Benjamin Woo is the founder and Managing Director of Neuralytix, Inc. He is a recognized, celebrated, provocative market visionary and thought leader. Mr. Woo frequently speaks at industry and customer events worldwide and is often quoted by leading business and technology press. Mr. Woo has dedicated his entire career to the data and information industry. He uses this diverse set of skills, knowledge and experience to provide in-depth market insight and advice on key aspects of the IT marketplace to both vendors and buyers of technology. Mr. Woo also advises Wall Street clients and other interested stakeholders. Mr. Woo serves on a number of advisory councils of leading storage manufacturers advising them on strategies and direction relating to the industry. Prior to founding Neuralytix, Mr. Woo was the Program Vice President of IDC s Worldwide Storage Systems Research, where he led a team of analysts responsible for advising clients on the evolution and trends related to data storage system. While at IDC, Mr. Woo also initiated the research on Big Data. About Neuralytix Neuralytix is the global leader in contemporary and relevant IT market research and consulting. We take a holistic and forward-looking approach to research, which makes us unique and the most relevant research firm in the IT industry today. Neuralytix is the standout leader in contemporary IT market research and consulting. Our research helps enterprise end-users, vendors and the financial community make the best possible decisions as it relates to their investments in technology. It releases them from anchoring their decisions in archaic and out-of-date segmentations that only serve to hold back opportunity. Instead, our contemporary and forward-looking views sets up the framework that optimizes immediate and future gains, competitive advantage and enterprise value. Neuralytix, Inc Lexington Avenue, #3 New York, NY USA Page 16 of 16