Big game changers for telco


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Big game changers for telco

  1. 1. Big Game Changers for TelcoDisruptive Technologies for Changing the GameDr. Arvind SathiOctober 18, 2012 © 2012 IBM Corporation
  2. 2. Overview •  What is Big Data •  What is driving Big Data Tsunami •  Use Cases •  Advanced Analytics Platform •  Implementation of Big Data Analytics2 © 2012 IBM Corporation
  3. 3. Many of us are still struggling with what “Big Data” means……3 © 2012 IBM Corporation
  4. 4. What is Big data? •  Volume •  5 Exabytes every 10 minutes in 2013 •  5 Petabytes of location data every 100 days for a large CSP •  30+ Petabytes of user generated data in Facebook •  As of 2010, AT&T had 193 trillion CDRs •  Velocity •  Mobile data growth compounded 78%, projected to 10.8 Exabytes per month in 2016 •  Online advertisement bidding process in 80 milliseconds •  Variety •  Structured, unstructured text, voice, video, RFID tags, maps, seismic data, medical events •  Call center conversations and chat sessions in many languages •  Veracity •  Disgruntled ex-employees, competitors crowding public data on brands •  Deceptive data – service companies offering to4 © 2012 IBM Corporation “Like” a product
  5. 5. Veracity If you google “Tether Verizon iPhone to iPad” The responses have varying level of Veracity They include sales pitch for Verizon as well as Process for Jailbreaking iPhone How do we ingest this information, organize it, prioritize it, and make it available on customer touch points,5 © 2012 IBM Corporation
  6. 6. Overview •  What is Big Data •  What is driving Big Data Tsunami •  Use Cases •  Advanced Analytics Platform •  Implementation of Big Data Analytics6 © 2012 IBM Corporation
  7. 7. Today’s customer is more empowered than ever before Customers  now   The  Internet  and   Everyone  is  an   This  is  changing     have  unlimited   social  networking   influencer  –    driving   the  en,re  way     access  to   have  created  a   purchase  decisions   service  providers   informa,on  and   more  informed   and  brand   manage  their   can  instantly  share   buyer   percep5ons   commerce   it  with  the  world   regardless  of   processes  using  new   credibility   tools  to  drive   success   >25% 70% 64% 57%   of  the  global   of  customers  use   of  customers  rely   of  standout   popula,on  is  on   Internet  search  as   on   organiza,ons  are   the  internet   their  primary   recommenda,ons   more  likely  to  use   source   when  buying   social  tools  7 informa,on   © 2012 IBM Corporation
  8. 8. Resulting in changing relationship with service providers In  case  of  bad  experiences,  they  exchange  informa6on  with  their   friends/family  and  infrequently  engage  with  the  provider   Mature  Markets   Emerging  Markets   Attempt to re-dial/re-connect 45% 46% 9% 53% 43% 4% 78%  /  87%     Avoid providers friends/family Avoid  Providers  with   21% 57% 22% 31% 56% 13% poor  experience   have poor experience with 73%  /  85%  Tell friends /family about my poor 12% 61% 27% 24% 61% 15% Tell  friends/family   experience   about  their  poor   experience   Contact the customer service 6% 45% 49% 14% 59% 27% Switch providers – e.g.use 5% 31% 64% 6% 38% 56% different SIM My provider contacts me when I 5% 32% 63% 5% 28% 67% have a poor experience Always Most of the time/Sometimes Never Source: 2011 IBM Global Telecom Consumer Survey, Global N= 10177; Mature Countries N=7875 8 © 2012 IBM Corporation
  9. 9. Service Providers can find Social Network leaders Group with no leader§  Leaders are 1.2 times more likely to churn compared with non-leaders.§  There are two types of leaders: disseminating leaders and authority leaders. The former areclosely connected to their group using outgoin calls, while the latter are connected through alarger proportion of incoming calls.§  When a disseminating leader churned, additional churns were 28.5 times more likely. Whenan authority leader left the group, additional churns were 19.9 times more likely.§  Typically, there is a very limited time between leaders’ churn and the churn of the followers.9 © 2012 IBM Corporation
  10. 10. Automation is opening new opportunities for data collection and analytics Example: Wall Street Journal reported pilot programs to use smart phones to buy and bag grocery items. Smart phones can also deliver and apply coupons. Opportunity for analytics: •  Opportunity to analyze customer profile and coupon uptake. •  CSP customer profile can provide additional insights to the grocery store – internet viewing, mobility, TV viewing, habits, etc. – driving intelligent campaigns to deliver coupons. •  Grocery purchase behaviour, jointly with CSP profile can drive Television Advertising. 10 © 2012 IBM CorporationSource: Wall Street Journal and IBM Analysis
  11. 11. Monetization of data – emergence of a market place11, reprinted with permission © 2012 IBM Corporation
  12. 12. Overview •  What is Big Data •  What is driving Big Data Tsunami •  Use Cases •  Advanced Analytics Platform •  Implementation of Big Data Analytics12 © 2012 IBM Corporation
  13. 13. Getting closer to consumers with the Mission Control Center The room features: §  Social listening frameworks and protocols §  Social listening software §  Data integration software (“mash-up”) §  Data visualizations and dashboards The goal of the project is to “take the largest sports brand in the world and turn it into largest participatory brand in the world.”13 Also see © 2012 IBM Corporation
  14. 14. Product knowledge hub – faster product onboarding and central repository for product knowledge Call Center Web ChatProblem •  Data is fragmented across CSP intranet, manufacturer site and third parties •  None of them provide a complete recipe to a customer •  Customer needs a step by step process, some of which is manufacturer dependent and some CSP dependent. •  A plenty of information is available on third party sites – e.g., You Tube.Solution •  Search and locate all the data associated with tech support from all possible sources Product Knowledge Hub •  Normalize and index the data •  Parse the queries and use context specific search to locate relevant information •  Once the problem is understood, direct the customer to a web page which answers the question, including video and step-by-step tutorialResults Consumer •  Improved call center efficiencies CSP Data Feedback •  Calls can be diverted to web self service Manufacturer Third Party •  CSP seen as central repository for product knowledge Web Site Web Sites •  Improved product on-boarding14 © 2012 IBM Corporation
  15. 15. Network Analytics CSP network node topology mapped onto Google Maps reporting the current video traffic with associated KPIs (network errors ratio average, alerts for node errors exceeding threshold, etc...) Traffic audience per channel being multicasted onto the CSP network with associated KPIs (Packet Loss retransmission efficiency average, MPEG error ratio, etc…) 15 © 2012 IBM Corporation
  16. 16. Network Analytics Channel 1 Channel 2 2 Millions of Set-Top Boxes messages analyzed in real-time Broadcast TV KPIs to detect video degradation quality causes : KPIs - Network node (switch/router) Encoder - Set-Top Box firmware/hardware KPIs - Channel encoding errors Cognos dashboard Network Management Switches, CSP routers,…Network nodes Network topology Administrator DSLAM IBM Netezza Alerts on defect detection Marketing Home Gateway Statistics KPIs Home Network STB STB STB STB STB STB STB CRM Help Desk Ip=; MPEG error ratio=0.5; firmware 16 version=V2.1;model=XXX;MAC- IBM InfoSphere Stream © 2012 IBM Corporation Address=000430123456;LinkChain=Node1-Node12- 10 000 msg/s Node123-Node1234;Message=Statistic;PacketLoss=54
  17. 17. Are your campaigns location driven…………………17 © 2012 IBM Corporation
  18. 18. Social Media and CSP data can be aligned, and analyzed to create customerinsight which can be used both for CSP products as well as for third parties. CSP Products CSP Hosted B2B Business New Product Dev New Product Dev Marketing / Sales Marketing / Sales Customer Service Customer Service External Customer Insight Micro Purchase Sentiments Social Media Segments Intentions Network Behavior Event Internal Communities Patterns Triggers Data Social Media Location Usage Demographics Interactions18 © 2012 IBM Corporation
  19. 19. The Vision of Trigger-Based marketing with Location and full customer features captured and analyzed, allows for a Social CRM Retailer Fan Page Retailer Customer Product Catalog Profile Telco Customer Profile 1) Registers with Retailer, gives Permissions to Retailer and 2) Follows a friend’s Telco post on FB and clicks the Like button on a camera she likes 4) Receives a 3) Intelligent Advisor message with an platform processes offer reminding her Lisa’s activity for 6) Lisa to stop by if she’s relevant actions using uses promo in the area Intelligent Telco and code to Advisor Platform Retailer information purchase offer at POS 5) Receives promo code for offer while passing by the store Customer Action Telco / Retailer Action19 © 2012 IBM Corporation
  20. 20. Overview •  What is Big Data •  What is driving Big Data Tsunami •  Use Cases •  Advanced Analytics Platform •  Implementation of Big Data Analytics20 © 2012 IBM Corporation
  21. 21. Big Data Analytics Platform to Support Many Use CasesIndustry (1) Deliver smarter (2) Transform Operations (3) Build SmarterImperatives services that generate to Achieve Business & Networks new sources of revenue Service ExcellenceExecutiveStakeholders Chief Marketing Chief Operating Chief Network Officer Officer Officer •  Real Time CDR •  Real Time CDR Analytics •  Real Time CDR Analytics and Ingest for and Ingest for Analytics and Ingest for •  Intelligent Campaigns •  Revenue Leakage •  Network OptimizationBig Data •  Customer Profile/ PreventionBusiness •  Service QualityScenarios Location Monetization •  Fraud Detection Analytics •  Next Best Action •  Ad Effectiveness Analysis with Social Media 21 © 2012 IBM Corporation
  22. 22. Big Data Architecture using a Sports Television analogy. The commentators converse with the audience in real-time. They sense what is happening in the game, prioritize next Conversation layer best discussion, and keep the audience engaged. The directors orchestrate a number of inputs – cameras, stock photos, replays, Orchestration layer statistics, special appearances along with commentators to keep the production focused on the game. The editors and the statisticians work in the background to collate past statistics, Discovery layer game replays, constantly discovering interesting facts about the game.22 © 2012 IBM Corporation
  23. 23. Advanced Analytics Platform Act / Web / Cable Identify Assemble Score Respond Interactions Conversation Level Conversations Opt-in / Opt-out Obfuscation DMZ Model Management Location Identity Integration Resolution Engine Command Center CRM / POS Orchestration Level Orders Unstructured Structured Discovery Discovery Bills Discovery Level23 © 2012 IBM Corporation
  24. 24. Monitoring Customer CommentsTopics that customers are talking about; gleaned from all the CRs, Emails, and Social Mediacontent. Each layer is a topic, and the word-cluster within it represents the synonyms for thetopic24 © 2012 IBM Corporation
  25. 25. Big Data view of the Customer Personal Attributes • Demographics Timely Insights •  Intent to buy Life Events Social Media-based 360˚ • Life-changing event Consumer Profiles Products Interests • Personal preferences Relationships • Personal, business Monetizable intent to buy Life Events products •  College: Off to Stanford for my MBA! Bye Chicago! •  I need a new digital camera for my food pictures, •  Looks like well be moving to New Orleans sooner than I thought. any recommendations around 300? Intent to buy a house •  What should I buy?? A mini laptop with Windows 7 OR a Apple MacBook!??! •  Im thinking about buying a home in Buckingham Estates per a recommendation. Anyone have advice on that area? #atx Location announcements #austinrealestate #austin •  Im at Starbucks in Times Square25 © 2012 IBM Corporation
  26. 26. Identity Resolution Top 200 Customer Job Applicant Criminal Identity Thief Investigation26 © 2012 IBM Corporation
  27. 27. Real-time Adaptive Analytics High Velocity Sensor Scorer Predictive Modeler Analytics Engine High Volume27 © 2012 IBM Corporation
  28. 28. Overview •  What is Big Data •  What is driving Big Data Tsunami •  Use Cases •  Advanced Analytics Platform •  Implementation of Big Data Analytics28 © 2012 IBM Corporation
  29. 29. Traditional data warehousing has become too complex for many customers Nearly 70% of data warehouses experience performance constrained issues of various types §  Too complex an infrastructure §  Too inefficient at analytics §  Too complicated to deploy §  Too many people needed to maintain §  Too much tuning required §  Too costly to operate IT shops supporting business operations have to think about how to deliver more critical analytics for the enterprise with shorter time to value2929 10/30/12 © 2012 IBM Corporation
  30. 30. We are observing an evolution Where the industry has been Where the industry is going §  Monolithic EDW (data) §  “Smart Consolidation” §  Data and data mart sprawl §  Consolidate sprawl & reduce cost §  Lack of enterprise agility §  Analytics delivered via appliances & specialized systems (API’s) §  Complex structure, process & architecture – focused §  Time to value is paramount §  Governance: limited or lacking §  Centralized data governance program §  Everyone talking about Analytics §  Analytics integrated to real-time business operations 30 30 10/30/12 © 2012 IBM Corporation30 30
  31. 31. How to guide the animal spirit – Big Data Governance Data can be stolen, manufactured and misused! Where are the regulations §  Variations across the world §  Varying practices and back lashes §  Location data and your smart phone §  Driving data and your car §  Transaction data and your credit card Is it Big Data or Big Brother §  Opt-in, conditional Opt-in vs. Opt-out §  Generational divide §  Data corruption, vulnerability Bottom line §  Data privacy must be addressed to the satisfaction of the consumers §  Are there ways to adjust for data quality31 © 2012 IBM Corporation
  32. 32. Elaboration on Security – Business Problem •  Can Telco data be correlated with social media to get an improved profile of the customer? •  Can we use the resulting profile for use cases: •  Acquisition •  Product Introduction •  Campaigns / responses •  Care – assisted / self care •  Loyalty and churn management •  How about sharing these profiles with third parties? •  Could we buy third party data and correlate with CSP information? •  Under what condition can we interact with the customer and provided added value to improve product, promotion, price, care or policies •  How about Analytics in the Cloud? Can we ship CSP CRM data to a third party cloud? We are observing two extremes, both are bad for business: •  A conservative view that uses security to shut down any mingling of PII information with social media •  A liberal view of personalized communication with no regard to customer privacy preferences.32 © 2012 IBM Corporation
  33. 33. Elaboration on Security – Options and related capabilitiesAnonymous PersonalizedPII data is obfuscated PII data includes opt-inData is summarized Different forms of permission seeking / managementSocial media is correlated with masked data Insight created on a 1-to-1 basisInferences are projected to segments Trust and privacy is personalized and closely managedActions are broadcasted to segmentsData masking retains non PII content Rigorous management of privacy managementIdentification and categorization of PII data No contamination of anonymous and personalizedRigorous process for data masking Policies constantly managed and revised based on customer and regulator feedback Market experience is showing it is hard to manage information revealed selectively. See Geoffrey A Fowler, “When the Most Personal Secrets get Outed on Facebook”, Wall Street Journal October 13, 2012.33 © 2012 IBM Corporation
  34. 34. Maturity Levels and Business Value Analysis Breakaway – a company who’s generally considered to be best in class in their execution of key business strategies, thereby able to exhibit the characteristics of an agile, transformational and optimized organization. This classification excludes “bleeding edge” Breakaway   5   or pioneering aspects, however these may also be evident in such companies. Key predictive performance indicators are used, modeling for outcomes and information is utilized enterprise wide for multi-dimensional decision-making. Differentiating – a company who’s execution of key business strategies through utilization of information are viewed as generally better than most other companies, creating a degree Differentiating   4   of sustainable competitive advantage. Management has the ability to adapt to changes to the business to a degree and measure business performance. Business leaders and users have visibility to key information and metrics for effective decision-making. Competitive – a company who’s capabilities generally are in line with the majority of similar Competitive 3     companies, with growing ability to make decisions on how to create competitive advantage. It is also the starting point to establish some consistency in key business metrics across the enterprise. Foundational – a company who’s capabilities to gather key information generally lag behind the majority of peers, which could potentially result in a competitive disadvantage. Foundational   2   Information is not consistently available or utilized to make enterprise wide business decisions. Still have a degree of manual efforts to gather information. . Adhoc – a company who’s just starting to develop capability to gather consistent information in key functional areas, generally falling well behind other companies in the corresponding Adhoc   1   sector. Information beyond basic reporting is not available. Generally have time consuming, manual efforts to gather information needed for day to day business decisions. 34 © 2012 IBM Corporation
  35. 35. How is your experience with social media …………………35 © 2012 IBM Corporation
  36. 36. Information Agenda teams are conducting analytics workshops world wide across many industries. Inputs Activities Outputs Business Analyze current the assessment initiatives Scope and planned IT Current State Objectives & SOA Vision Prioritized Business Strategies Initiatives Understand Conduct diagnostic interviewsopportunities current business challenges / Existing Business & IT Understand business goals and SOA vision business Assess quality of information delivered to the Environment Assess current / desired Information Maturity level Analyze key business scenarios Assessment Existing Data Collect Data Environment Review Information requirements functional Delivery Capabilities Analyze non - Verify Synthesize Assess current / plannedGaps Identify the architecture using accelerators Develop Recommendations Provide Recommendations Prepare a final report Business & IT Current & Document and Present Information Mgt Planned Practices Services Develop Roadmap and Optimization Plan Prepare a final report Recommendations Information Agenda Accelerators Summary Details IA for Education IA Maturity Assessment IOD Reference Architecture36 © 2012 IBM Corporation
  37. 37. Social Media Maturity Model Ad hoc Foundational Competitive Differentiating BreakawayCapability: Marketing has Organizational Customer data Organization CustomerMonitor brand hired a set of accounts to from social engages in sentiment issentiment interns to collect media is social media integrated with monitor social sentiment data collected and conversation to product and media data on social media analyzed using influence marketing sites (FB, Yelp, analytical tools customer processes etc.) sentimentMeasurementsBrand Baseline Collected Measured Influenced to Influenced tosentiment positive positive direction directionIdentification of Baseline Low Medium High Highadvocates /ambassadorsImpact on Baseline Baseline Small Medium Largebrand / revenue37 © 2012 IBM Corporation
  38. 38. Conclusions Big Data Analytics is bringing unprecedented changes to organizations across industries. The presentation provided business solutions and provided a technical overview. Business solutions: •  Specific solutions – Network Analytics, Campaign Management, Profile Monetization •  Significant business value by tapping and conquering volume, velocity, variety, and veracity •  New applications, new business models, new partnerships Technical solutions: •  Overall architecture integrates with current DW platform using a three layer architecture – conversation, orchestration, discovery •  Significant technological gains in the last couple of years in each of these areas as well as their integration. Implementation: •  Establish a road map based on current and target maturity levels •  Big Data Governance an important issue to be addressed. •  Do not leave Data Security behind!38 © 2012 IBM Corporation
  39. 39. Big Data Analytics – New Book Launching at Information On Demand 2012! What’s the book about? This book examines the drivers behind big data, postulates a set of use cases, identifies a set of solution components enabled by big data, synthesizes a solution, and recommends implementation approaches. Who is this book for? Business and IT leaders who are looking for practical advice on how to drive immediate business results with analysis of big data Where can you get a copy? •  Information On Demand 2012 Book Store (Bayside Foyer, Mandalay Bay South Convention Center) •  Book Signing by Author, Dr. Arvind Sathi Ø Monday Oct 22 – 4:00 p.m.- 5:00 p.m. at conference Book Store •  Download e-book version at us at Information On Demand 2012 in Las Vegas! Oct 21 – 25, 2012 © 2012 IBM CorporationRegistration link -
  40. 40. Big Data Analytics Book Description SummaryThe Big Data tsunami is already hitting organizations - a set of disruptive technologies to drive gamechangers. Business leaders across the globe are seeking answers to the following questions: • What is Big Data and what are others doing with it? • How do we build a strategic plan for Big Data Analytics? • How does Big Data change our analytics architecture?Unlike many other Big Data Analytics blogs and books that cover the basics and technological underpinnings,this book brings a practitioner’s view to Big Data Analytics. The author has drawn the material from a largenumber of workshops and interviews with business and IT leaders. About Author Audience Next StepsDr. Arvind Sathi is the World Wide Communication •  mid to Sr. mgmt •  Get a complimentarySector architect for the Information Agenda team at executives in network copy of the book atIBM. His primary focus has been in creating visions operations, customer Information On Demandand roadmaps for Advanced Analytics at leading service, sales, marketing, 2012 Book Store orIBM clients in telecommunications, media and strategy or IT request the IBM salesentertainment, and energy and utilities organizations •  IT service & software rep to order one for youworldwide. He has conducted a number of provider community •  Request a briefing onworkshops on Big Data assessment and roadmap • Industries covered – Big Data Analytics fordevelopment. Financial services, Public key stakeholders from IT services, healthcare, retail, and Business in your telecom, energy & utilities, organization media & entertainment. © 2012 IBM Corporation
  41. 41. 41 © 2012 IBM Corporation