Monetize Big Data

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Monetize Big Data Optimize your unique position to capture opportunities

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Take advantage of Big Data Understand Big Data Review Big Data’s evolution Gain from Big Data opportunities Understand—data is the new global currency of business Review Big Data success stories Review our recommendations Transform strategically About the author

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Big Data is a phenomenon brought about by rapid data growth; complex, new, and changing data types; and parallel technology advancements—and it brings huge possibilities. By optimizing these enormous amounts of structured and unstructured data, CSPs are in a unique position to capture these opportunities and create new revenue streams. Take advantage of Big Data Daily, from our mobile devices, we download news from home Wi-Fi; connect with friends on Facebook Messenger, Google Hangouts, WhatsApp, WeChat, or Viber; watch movies while waiting for the train; settle bills using online banking; browse the web to find the best air ticket price; and more. Each of us is now a walking data generator, providing two critical things—location and identify. McKinsey Global Institute, in the June 2011 report, estimated that locationbased data alone will generate $100 billion of value to CSPs in the next 10 years. Asia is the leading region for personal location data generation simply because of its population density and high volume of mobile devices used.

Big Data is a paradigm shift. At a tactical level, the analytic paradigm is shifting from analyzing data using well-known schemas to finding hidden relationship patterns. At a strategic and management level, it’s an opportunity for business transformation and a decision-making evolution—making data-driven decisions that are speedier and better. To take full advantage of Big Data opportunities, communications service providers (CSPs) must move beyond the tactical level of only implementing tools. It’s time to start engaging in transformational workshops to identify business value that can be achieved through optimal use of new and old information sources. The key is formulating a strategy with a roadmap to transform and monetize these opportunities.

Understand Big Data Big Data is a class of data challenges—due to increasing volume, variety, velocity, and complexity, which are beyond the capabilities of traditional software, architecture, and processes to effectively manage and use it. To illustrate the impact of Big Data, in April 2013, three economists published an eye-opening paper. It said Google™ Trends data was useful in predicting daily price moves in the Dow Jones industrial average, reversing their earlier research published in 2010. What’s the key difference? The data—faster data (in real time) is better, and bigger amounts of data matter when it comes to predictions.1

1

F orbes, “Big data gets bigger, now Google Trend can predicts the market,” April 2013

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Structured data is the millions of transactions and information that operators have used to run their businesses for many years. Unstructured data is the billions of transactions that are as varied as data from office documents, sensors, survey responses, or call center operations. • Volume—Data volume is the primary attribute of Big Data. It’s not possible to quantify Big Data volume. But in general terms, when you start to worry about data, that’s your Big Data volume. • Variety—Big Data comes from a greater variety of sources than ever before. These sources range from structured to unstructured data—text, audio, video, and human language—and semistructured data including XML and RSS feeds. Plus, multidimensional data can be drawn from a data warehouse to add historic context to it. So, with Big Data, variety is just as big as volume, and volume tends to fuel itself. • Velocity—This is the speed and frequency of data generation and its delivery. It is also the velocity or capability to analyze data in real time or streaming. This is fundamental when you have to connect analytics and action-ability in real time. • Complexity—This is the inherent characteristics needed to do analytics on a wide array of data—structured and unstructured, under a global environment, and in real time. As a result, the difference in quality, costeffective forms of processing, security, regulatory, and compliance and access requirements, at different stages of the data life cycle form the complexity of Big Data. And the combination of any two “V” forces—volume, variety, and velocity—increases analysis complexity.

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Structured information it used to run the business • Devices • Session • Context • CDRs, XDRs • Subscriber data • Usage • Deep packet data • Network QoS • Web mobile behavior, transactions

Billions of interactions

Millions of transactions

Unstructured information it used to gain insight on business drivers • Online sales • Downloads • Call notes • SMS • Web chat • Blogs • Social networks • Mobile apps • Sensors • Survey response • Emails • Office documents

Figure 1: What is Big Data to CSPs?

Big Data sources are generally one of two types: • High-variety unstructured or semistructured data, which contains human language or rich media • High-volume, high-velocity machine-generated data, which can be messages or sensor readings Today, we find that 80 percent to 85 percent or more of new data is human-generated in an unstructured format. Structured data that operators collect amounts to millions of transactions and is the information they have used to run their businesses for many years. This area is familiar territory—they just haven’t applied it to areas outside their traditional operations to generate value, either because they didn’t see the value or were concerned about privacy governance. Unstructured data is a far more complex and new terrain for operators. There are billions of transactions to take into account and sources can be as varied as data from office documents, sensors, survey responses, or call center operations. Unstructured information can be used to gain insight into business drivers, and bringing together data from structured and unstructured sources is at the heart of operators’ plans to derive value and generate revenue and differentiation from Big Data. Table 1: Big data implications to CSPs BIG DATA CHARACTERISTICS

IMPLICATIONS

Volume and variety—Data collected by CSPs are growing in terms of volume (from GB in 2000 to TB in 2010) and variety (few different voice services in 2000, thousands of Internet services in 2010).

It is not just question of efficiently (cost, operability, manageability, and security) storing TB and petabyte (PB) of traffic, but it is fundamental that the capability easily add new protocols, format, and manage semi- and fully unstructured traffic—for example, logs, social media, multimedia.

Velocity—Policy and charging rules function (PCRF) are just the beginning of new era where telecommunication networks will adapt themselves in real time to subscriber’s needs, profile, and quality of experience (QoE) management. The network SLA is replacing subscriber QoE.

PCRF and future software-defined network (SDN) must be driven by sophisticated real-time analytic systems, able to identify in real time, issues at the subscriber level. Sophisticated analytics are needed to compare events with historical trends to keep the right decision.

Value—CSPs need to monetize data to support increasing investments in network to support growing traffic. They need to create new revenue streams from their data.

CSPs must extract more value from the information they collect. They have to switch from service analysis to preference analysis to activate new ecosystems partners. The models are Google, Amazon, Facebook, and LinkedIn.

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Review Big Data’s evolution Gartner analysts predict that data will grow 800 percent over the next five years and that 80 percent of that new data will be unstructured.2 Further, Gartner predicts that nearly 70 percent of all business intelligence vendors will incorporate natural-language capabilities into their applications by 2016. 3 The result—users will be able to perform searches and analyze data using natural language or even voice commands rather than traditional SQL queries. Big Data requires technology enablers for Big Data, and technologies such as an intelligent data operating layer (IDOL) that can “understand” all forms of content, not just text, in any language. Integrating unstructured data with structured data will be a challenge, but it will provide business value. For example, performing analysis on content from social media—a list of what people’s interests are based on what they “say” on their accounts and their friends—will enable mobile and ad experience personalization. Technology solutions that embed natural language understanding and provide Telco-specific analytics solutions, such as deep packet inspections, are the preferred choice.

Gain from Big Data opportunities Customer loyalty is diminishing. They are easily attracted to other CSPs by a more attractive offer, plan, or new device. Traditional revenue streams, such as short message service (SMS) and international direct dialing (IDD) calls, are being eroded by over-the-top (OTT) players such as Facebook, Google, WhatsApp, Skype, WeChat, and Viber. Meanwhile, the explosive use of mobile devices creates complexity and big demand on network capacity. While there is a need to generate more revenue, keeping an eye on expenses is the other end of the equation. Key business drivers for CSP today are: • Customer-centric and customer experience management objectives—to remain in the game and enhance revenue • New business models—to create new revenue streams • Risk/financial management—to prevent revenue leaks • Operational optimization—to optimize network and operating costs

2

F orbes, Big Data—Big Money Says It Is A Paradigm Buster, June 2012

3

 artner, Predicts 2013: Business Intelligence and G Analytics Need to Scale Up to Support Explosive Growth in Data Sources, December 2012

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Currently located two miles from sports store

GPS Video conferencing

Transfer data

Data transmission

Experience providers

Social media

Digital coupons

Web browsing

Understand customer use, behavior, and interests. Targeted products and marketing offers Understand customer experience across network, services, and social conversation. Network optimization

Digital music

Streaming video

Saves department store coupons

Six slow data sessions yesterday

Monthly data usage mostly used on social network and email

Browsing baby websites last two months

Connect with OTT players, advertisers, and verticals. New business models Data is the new currency of business

Figure 2: Can CSPs avoid becoming a data pipe?

Understand—data is the new global currency of business Information is at the heart of transforming, to become communication experience providers, in addition to core services providers. While the notion of average revenue per user (ARPU), understanding the customer and personalizing the experience, and connecting and monetizing with ecosystem are not new, traditional information management approaches are no longer sufficient to address the emerging demands of agility. To understand opportunities brought about by Big Data, the following table illustrates example of Big Data levers.

“HPE is the only vendor who could understand SBM’s true issue in usage data management. HPE Dragon streamlined our CDR operation and can now leverage our view of customer usage to provide a better customer experience.” Akihiro Muranaka, manager of Rating System Development Department, Information System Div., SoftBank Mobile

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Table 2: Big data lever grouped by functions FUNCTION

BIG DATA LEVER

Marketing

• Targeted products and marketing offers (personalization) • Cross-selling • Location-based marketing • Customer microsegmentation • Sentiment analysis • Enhancing the multichannel consumer experience • Customer/product life cycle analysis • Churn analysis (predictive analysis)

New business model

• Geo-targeted advertising and couponing • Revenue sharing with digital retailers • Monetization of (obfuscated) subscribers’ data • In-store behavior analysis • Insurance pricing (for example, go-as-you-drive insurance) • Health care monitoring (for example, senior citizens or family members) • Safety tracking of family members

Network engineering

• Network optimization (for example, wi-fi offload) • Quality of experience • Proactive resolution (QoS policy)

Customer services and operation

• Customer experience management • RT FAQ broadcasting

Finance

• Fraud management and prevention • Revenue assurance

IT

• Operational efficiency

Review Big Data success stories Gain operational efficiency and customer experience management With billions of usage records per day, SoftBank Mobile (SBM) realized the business benefits to be gained by improved management and better access to their usage records repository. HPE Dragon RED—rather than additional storage—was deployed to compress huge customer data record (CDR) archives. The unlimited scalability architecture, high-compression ratio, high-load performance, and ready-made processes of data analytics enabled SBM to achieve these business benefits. Scaled systems to keep pace with double-digit growth in billing data volumes over six months; reduced archiving area by 90 percent without additional storage; and improved operational efficiency, including faster daily packet CDR query performance. Provide targeted offers and new business model Two major North American carriers and a tier-one CSP in Asia Pacific deployed an HPE Smart Profile Server (SPS) so they could partner with an advertising company and collaborate with OTT players to create new revenue streams. With input from HPE SPS Customer Sentimental Analysis—a result from social network analysis and mining, and call record analysis—the solution correlated a live session with networkbased data. It then provided in real time (milliseconds), a meaningful masked profile—based on customers opt-in approval. The profile was used by ad networks, publishers/destinations, and others through real-time bidding marketplaces. This improved advertising effectiveness and offered customers a more personalized digital experience. It also generated targeted offers/plans for customers. By providing a personalized customer service based on customer sentiment coupled with Service Experience Analytics, the result was happier customers, better stickiness, and stronger customer lifetime value, while creating new revenue streams.

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Use new business model—machine-to-machine Some CSPs offer pay-as-you-go car insurance. Telemetric devices are plugged into the car’s OBD port. Information collected from these devices provides data to insurers. Rates might be lowered from 1 percent to 45 percent if the device finds the driver drives less over time, detects no high speeds or erratic braking, and finds little late-night or rush-hour driving. Gartner analysts predict that data will grow 800 percent over the next five years, and that 80 percent of that data will also be unstructured.4

Gain operational efficiency and customer care By combining information from networks, call centers, and social networks, you can transform from a reactive to proactive approach in customer care. Following are the key features and benefits: • Anticipate questions from customers with a better understanding of what is happening in real time • Significantly reduce calls to customer care by broadcasting frequently asked questions to every touch point—in real time • Improve first call resolution and reduce average handling time

Review our recommendations To optimize Big Data’s benefit, we provide a broad set of recommendations to proceed down the journey of Big Data, eventually evolving it into a core competence. Formulate strategy and roadmap Big Data-related initiatives will bring about significant changes for business, organization, technology, process, and potentially the industry. It sounds quite obvious that a strategic plan is required. However, most companies start at a tactical level by buying tools, with no strategic plan in place. This most often ends up with costly and ineffective solutions or inaction. The power of the plan is to create a shared vision among senior executives, technology professionals, and data scientists of the potential business values. As Big Data initiatives evolve, investments typically involve competing strategic priorities. Senior executives should oversee this to ensure efforts are aligned with the company’s strategic intent. Big Data strategies provide input into roadmap development. The roadmap should include priorities and associated business values, actions required for implementation, and how to mitigate potential risks. It should also include a data strategy covering data governance, data privacy, and from a customer experience perspective, the need to incorporate opt-in/opt-out strategies. In a survey by European Communications, a group of industry executives found agreement across the board—91 percent believe that Big Data strategies should be a priority for every service provider. 5 Create a business case with measurable outcomes CSPs should always start on Big Data initiatives with measurable outcomes and senior management commitment. Before developing any business case, you should commence your Big Data journey with a proof-of-concept scale project and a small investment. Further, Big Data initiatives should be not viewed as an IT project unless the initiatives are purely IT related. It is recommended that a Big Data business case always have a business owner. The key success factor is for business functions to take the lead, “roll up their sleeves,” and refrain from being a back-seat driver giving IT instructions to produce analytical results. On the other hand, IT should refrain from the model of “build it and they will come.” Business and IT must partner for success. 4

F orbes, Big Data—Big Money Says It Is A Paradigm Buster, June 2012

5

 ig Data Survey: New Revenue Stream top B CEM as biggest Opportunity, European Communications, May 2012

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In fact, there is a trend for sales and marketing or operation/customer care functions to increase ownership. Gartner predicts that by 2017 the chief marketing officer (CMO) will spend more on IT than the chief information officer (CIO).6

HPE Industry Advisory Program The HPE Industry Advisory Program is a unique HPE Solution Consulting Services program that delivers innovative thought leadership to address our clients’ key business issues. The program is built on the global knowledge, expertise, and experience of our industry business consultants. It incorporates proven HPE methodologies, industry frameworks, and intellectual capital to deliver true business value through a collaborative, social media-based environment.

Develop a proof of concept To get started, pick a business function to be the testing ground. It should be analytics-friendly, backed with data scientist skills. Based on initiatives from the roadmap and committed initial efforts to customer-centric or customer experience management outcomes, these areas should provide value for the business to remain competitive and potentially generate additional revenue. Start small with simple analytics, measure the outcomes, and replicate the model across the enterprise quickly, increasing the analytic complexity gradually based on the Big Data strategy. Realign processes The challenge often lies not in the analytics, but in their effective integrations into organizational processes. Organizations must re-align the end-to-end process from what and how data is collected, to who analyzes them and the final decision-making process. If the process is not aligned, there’s a risk for analytics redundancy or data discrepancies, which undermines Big Data’s value. Manage talent Some of the most crucial Big Data success factors are the availability of data scientists and other professionals skilled at working with large quantities of information. The best data scientists have business domain expertise and are comfortable speaking the language of business and helping leaders formulate their challenges in ways that Big Data can tackle.7 They should have coding or technical skills to use the tools and be interested in finding hidden patterns in large data sets—internal and external. Data scientists understand how to fish out answers to important business questions from today’s tsunami of unstructured information. According to Gartner, by 2015 Big Data demand will reach 4.4 million jobs globally, but only onethird of those jobs will be filled.8 As companies rush to capitalize on the potential of Big Data, the largest constraint many will face is the scarcity of this special talent. Consolidate and remake architecture Many CSPs have overlap or redundancy in warehouses or data marts. This may be due to organizational differences or the result of mergers and acquisitions. Regardless, it increases costs and can contribute to management confusion if it results in “multiple versions of the truth” being reported. With Big Data deployment, IT organizations should leverage the Big Data strategy to consolidate data stores, clearing up confusion and remaking Big Data architecture and applications to ensure success. Use a third-party cloud platform A recent report from AnalysysMason claims that more than a quarter of CSPs have no strategy for Big Data analytics, and the vast amount of information that CSPs hold about their subscribers has been largely untapped.9

6

 artner, Forecast Analysis: Enterprise IT Spending G by Vertical Industry Market, Worldwide, 2Q13 Update

7

“ Data Scientists: The Sexiest Job of the 21st Century,” Thomas H. Davenport and D.J. Patil, HBR Oct 2012

8

 artner, Predicts 2013: Big Data and G Information Infrastructure, November 2012

9

 ig data analytics: How to generate B revenue and customer loyalty using realtime network data, January 2013

To address this shortcoming, you must define your Big Data strategy. For executing the strategy, some CSPs are turning to third-party cloud computing services to monitor their networks and software infrastructure and analyze customer behavior. This partnership achieves two key objectives: It identifies new patterns and opportunities to build customer loyalty and creates new revenue streams. And it enables you to move quickly by avoiding large investments and going through the learning process internally, leaving time to focus on the business strategic.

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Transform strategically Use data in decision-making One of the most critical aspects of Big Data is its impact on how decisions are made and who gets to make them.10 Although most companies today rely largely on data for their decisions, often, the most important decisions rely on HiPPO—the highest-paid person’s opinion. Most of time, the decision is made and then data is collected to support the decision. The first question that a data-driven organization asks itself is not “What do we think?,” but “What do we know?,” “What does the data say?,” and “Where does the data come from?” When it comes to what problem to tackle and questions to ask, of course, domain expertise remains critical. To nurture an analytical culture, organizations should also consider deploying simple and usable analytics tools to their front-end employees, for example, sales team, with proper training, where applicable. Big Data does not undermine the need for vision and human insight. The company who can combine vision and domain expertise together with data science will be ahead of their rivals. Seize this window of opportunity Big Data is no longer hype. The payoffs from joining the Big Data analytics management revolution are no longer in doubt. There are numerous success cases re-enforcing that companies who inject Big Data and analytics into their operations show productivity rates and profitability 5 percent to 6 percent higher than those of their peers.11 Consumer attitude is changing toward data privacy. OTT players have changed the attitude toward sharing personal information. Having said that, to capture Big Data opportunities, CSPs should have a data strategy addressing data privacy concerns, such as opt-in/opt-out measures. Data has become the new global currency. Ultimately, insights derived from Big Data strategies will increase your knowledge of your operations and customer base. This enables you to better direct network investment, improve the efficiency of operational processes, deepen knowledge of customers’ behaviors and needs, enhance customer experience, strengthen customer relationships over time, and create new business models. Seize this crucial window of opportunity to become a competitive new service provider. Use Big Data analytics to transform, monetize, and eventually become your most valuable core competency. While CSPs have similar data sets and thoughts, the real differentiators will be the “first mover” advantage, using holistic strategies from the beginning and superior execution, which needs to be supported by relevant skills and solutions.

Learn more at hp.com/go/scs

10

“ Big Data: The Management Revolution,” Andrew McAfee and Erik Brynjolfsson, HBR October 2012

11

Ibid

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About the author Helen Chu Helen Chu is a principal consultant from HPE Solution Consulting Services in the Asia Pacific region. With an extensive background in business consultancy and strong industry experience, Chu harnesses her capabilities in comprehending business challenges faced by individual clients and her deep technology knowledge to develop and execute effective strategies and transformation programs, while innovating and leveraging technology solutions that deliver business value to Hewlett Packard Enterprise (HPE) clients.

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© Copyright 2013, 2015 Hewlett Packard Enterprise Development LP. The information contained herein is subject to change without notice. The only warranties for HPE products and services are set forth in the express warranty statements accompanying such products and services. Nothing herein should be construed as constituting an additional warranty. HPE shall not be liable for technical or editorial errors or omissions contained herein. © 2012 Google Inc. All rights reserved. Google is a registered trademark of Google Inc. All other third-party trademarks are the property of their respective owner. 4AA4-8041ENW, November 2015, Rev. 3