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© 2012 IBM Corporation
Big Game Changers for Telco Disruptive Technologies for Changing the Game Dr. Arvind Sathi October 18, 2012
© 2012 IBM Corporation 2
Overview
• What is Big Data
• What is driving Big Data Tsunami
• Use Cases • Advanced Analytics Platform
• Implementation of Big Data Analytics
© 2012 IBM Corporation 3
Many of us are still struggling with what “Big Data” means……
© 2012 IBM Corporation 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 to
“Like” a product
© 2012 IBM Corporation 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,
© 2012 IBM Corporation 6
Overview
• What is Big Data
• What is driving Big Data Tsunami
• Use Cases • Advanced Analytics Platform
• Implementation of Big Data Analytics
© 2012 IBM Corporation 7
Today’s customer is more empowered than ever before This is changing the en,re way service providers manage their commerce processes using new tools to drive success
Everyone is an influencer – driving purchase decisions and brand percep5ons regardless of credibility
Customers now have unlimited access to informa,on and can instantly share it with the world
The Internet and social networking have created a more informed buyer
>25% of the global popula,on is on the internet
57% of standout organiza,ons are more likely to use social tools
70% of customers use Internet search as their primary source informa,on
64% of customers rely on recommenda,ons when buying
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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
73% / 85% Tell friends/family about their poor experience
78% / 87% Avoid Providers with poor experience
Mature Markets Emerging Markets
45%
21%
6%
5%
46%
57%
61%
45%
31%
32%
9%
22%
27%
49%
64%
63%5%
12%
Attempt to re-dial/re-connect
Avoid providers friends/familyhave poor experience with
Tell friends /family about my poorexperience
Contact the customer service
Switch providers – e.g.usedifferent SIM
My provider contacts me when Ihave a poor experience
Always Most of the time/Sometimes Never
53%
31%
14%
6%
43%
56%
61%
59%
38%
28%
4%
13%
15%
27%
56%
67%
24%
5%
Source: 2011 IBM Global Telecom Consumer Survey, Global N= 10177; Mature Countries N=7875
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Service Providers can find Social Network leaders
§ 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 are closely connected to their group using outgoin calls, while the latter are connected through a larger proportion of incoming calls. § When a disseminating leader churned, additional churns were 28.5 times more likely. When an 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.
Group with no leader
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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.
Source: Wall Street Journal and IBM Analysis
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Monetization of data – emergence of a market place
www.lumapartners.com, reprinted with permission
© 2012 IBM Corporation 12
Overview
• What is Big Data
• What is driving Big Data Tsunami
• Use Cases • Advanced Analytics Platform
• Implementation of Big Data Analytics
© 2012 IBM Corporation 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.”
Also see http://www.youtube.com/watch?v=InrOvEE2v38
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Product knowledge hub – faster product onboarding and central repository for product knowledge
Problem • 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
• 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 tutorial
Results • Improved call center efficiencies • Calls can be diverted to web self service • CSP seen as central repository for product knowledge • Improved product on-boarding
Call Center Web Chat
Product Knowledge Hub
Manufacturer Web Site
Third Party Web Sites
Consumer Feedback CSP Data
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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…)
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STB Home Network
Home Gateway
STB STB
STB STB
Encoder
Broadcast TV
CSP Network nodes
topology
STB STB
STB
Ip=233.136.0.127; MPEG error ratio=0.5; firmware version=V2.1;model=XXX;MAC-Address=000430123456;LinkChain=Node1-Node12-Node123-Node1234;Message=Statistic;PacketLoss=54
DSLAM
Switches, routers,…
Netw
ork Managem
ent
Cognos dashboard
KPIs
KPIs KPIs
Channel 1 Channel 2
KPIs
Alerts on defect
detection
Statistics
Network Administrator
Help Desk CRM
Marketing
IBM InfoSphere Stream 10 000 msg/s
2 Millions of Set-Top Boxes messages analyzed in real-time to detect video degradation quality causes :
- Network node (switch/router)
- Set-Top Box firmware/hardware
- Channel encoding errors
IBM Netezza
Network Analytics
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Are your campaigns location driven…………………
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Social Media and CSP data can be aligned, and analyzed to create customer insight which can be used both for CSP products as well as for third parties.
New Product Dev
Location
Communities Behavior Patterns
Event Triggers
Micro Segments Sentiments Purchase
Intentions
Usage Demographics Interactions
External Social Media
Network Data Internal
Social Media
Customer Insight
Marketing / Sales
Customer Service
New Product Dev
Marketing / Sales
Customer Service
CSP Products CSP Hosted B2B Business
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The Vision of Trigger-Based marketing with Location and full customer features captured and analyzed, allows for a Social CRM
Customer Action Telco / Retailer Action
3) Intelligent Advisor platform processes Lisa’s activity for relevant actions using Telco and Retailer information
4) Receives a message with an offer reminding her to stop by if she’s in the area
6) Lisa uses promo code to purchase offer at POS
1) Registers with Retailer, gives Permissions to Retailer and Telco
Retailer Fan Page Retailer Customer
Profile
Intelligent Advisor Platform
5) Receives promo code for offer while passing by the store
Telco Customer Profile
Product Catalog
2) Follows a friend’s post on FB and clicks the Like button on a camera she likes
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Overview
• What is Big Data
• What is driving Big Data Tsunami
• Use Cases
• Advanced Analytics Platform
• Implementation of Big Data Analytics
© 2012 IBM Corporation 21
Big Data Analytics Platform to Support Many Use Cases
Big Data Business Scenarios
Industry Imperatives
(1) Deliver smarter services that generate new sources of revenue
• Real Time CDR Analytics and Ingest for
• Intelligent Campaigns
• Customer Profile/ Location Monetization
• Next Best Action
• Ad Effectiveness Analysis with Social Media
(2) Transform Operations to Achieve Business & Service Excellence
• Real Time CDR Analytics and Ingest for
• Revenue Leakage Prevention
• Fraud Detection
(3) Build Smarter Networks
• Real Time CDR Analytics and Ingest for
• Network Optimization
• Service Quality Analytics
Executive Stakeholders Chief Marketing
Officer Chief Operating Officer
Chief Network Officer
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Big Data Architecture using a Sports Television analogy.
Conversation layer
Orchestration layer
Discovery layer
The commentators converse with the audience in real-time. They sense what is happening in the game, prioritize next best discussion, and keep the audience engaged.
The directors orchestrate a number of inputs – cameras, stock photos, replays, 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, game replays, constantly discovering interesting facts about the game.
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Advanced Analytics Platform
Identify Assemble Score
Identity Resolution
Integration Engine
Command Center
Unstructured Discovery
Structured Discovery
Conversation Level
Orchestration Level
Discovery Level
DMZ Opt-in / Opt-out Obfuscation
Web / Cable Interactions
Conversations
Location
CRM / POS
Orders
Bills
Act / Respond
Model Management
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Monitoring Customer Comments Topics that customers are talking about; gleaned from all the CRs, Emails, and Social Media content. Each layer is a topic, and the word-cluster within it represents the synonyms for the topic
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Big Data view of the Customer
Monetizable intent to buy products • I need a new digital camera for my food pictures,
any recommendations around 300?
• What should I buy?? A mini laptop with Windows 7 OR a Apple MacBook!??!
Location announcements • I'm at Starbucks in Times Square
Life Events • College: Off to Stanford for my MBA! Bye Chicago!
• Looks like we'll be moving to New Orleans sooner than I thought.
Intent to buy a house • I'm thinking about buying a home in Buckingham Estates per a
recommendation. Anyone have advice on that area? #atx #austinrealestate #austin
Personal Attributes • Demographics
Life Events • Life-changing event
Relationships • Personal, business
Timely Insights • Intent to buy
Products Interests • Personal preferences
Social Media-based 360˚
Consumer Profiles
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Identity Resolution
scrila34@msn.com
Job Applicant
Identity Thief
Top 200 Customer
Criminal Investigation
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Sensor
Predictive Modeler
Scorer
Analytics Engine
High Velocity
High Volume
Real-time Adaptive Analytics
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Overview
• What is Big Data
• What is driving Big Data Tsunami
• Use Cases • Advanced Analytics Platform
• Implementation of Big Data Analytics
© 2012 IBM Corporation 29 10/30/12 29
§ Too complex an infrastructure
§ Too complicated to deploy § Too much tuning required
§ Too inefficient at analytics
§ Too many people needed to maintain § Too costly to operate
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Traditional data warehousing
IT shops supporting business operations have to think about how to deliver more critical analytics for the enterprise with shorter time to value
has become too complex for many customers
Nearly 70% of data warehouses experience performance constrained issues of various types
© 2012 IBM Corporation 30 10/30/12 30
We are observing an evolution
§ Monolithic EDW (data)
§ Data and data mart sprawl
§ Lack of enterprise agility § Complex structure, process &
architecture – focused
§ Governance: limited or lacking § Everyone talking about Analytics
§ “Smart Consolidation”
§ Consolidate sprawl & reduce cost
§ Analytics delivered via appliances & specialized systems (API’s)
§ Time to value is paramount
§ Centralized data governance program § Analytics integrated to real-time
business operations
Where the industry has been Where the industry is going
30 30
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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 quality
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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.
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Elaboration on Security – Options and related capabilities
Anonymous Personalized
PII data is obfuscated Data is summarized Social media is correlated with masked data Inferences are projected to segments Actions are broadcasted to segments
PII data includes opt-in Different forms of permission seeking / management Insight created on a 1-to-1 basis Trust and privacy is personalized and closely managed
Data masking retains non PII content Identification and categorization of PII data Rigorous process for data masking
Rigorous management of privacy management No contamination of anonymous and personalized 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.
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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” 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 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
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. 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 sector. Information beyond basic reporting is not available. Generally have time consuming, manual efforts to gather information needed for day to day business decisions.
1
2
4
5
3
Adhoc
Differentiating
Breakaway
Competitive
Foundational
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How is your experience with social media …………………
© 2012 IBM Corporation 36
Information Agenda teams are conducting analytics workshops world wide across many industries.
SOA Vision Business
Objectives & Strategies
Assess current / planned architecture using accelerators Identify the Gaps
Analyze non - functional requirements -
Prepare a final report Provide Recommendations
Understand business goals and SOA vision
Review Information Delivery Capabilities
Conduct diagnostic interviews Understand current business challenges / opportunities
Inputs Activities Outputs Current State
Prioritized Business Initiatives Existing Business & IT Environment
Assessment Collect Data Verify Synthesize
Recommendations Summary Details
Analyze current and planned IT initiatives Scope the assessment
Analyze key business scenarios Assess current / desired Information Maturity level
IA for Education IOD Reference Architecture
Current & Planned Services
Business & IT Information Mgt
Practices
Existing Data Environment
Prepare a final report Develop Roadmap and Optimization Plan
Develop Recommendations Document and Present
IA Maturity Assessment
Information Agenda Accelerators
Assess quality of information delivered to the business
© 2012 IBM Corporation 37
Social Media Maturity Model
Ad hoc Foundational Competitive Differentiating Breakaway
Capability: Monitor brand sentiment
Marketing has hired a set of interns to monitor social media data
Organizational accounts to collect sentiment data on social media sites (FB, Yelp, etc.)
Customer data from social media is collected and analyzed using analytical tools
Organization engages in social media conversation to influence customer sentiment
Customer sentiment is integrated with product and marketing processes
Measurements Brand sentiment
Baseline Collected Measured Influenced to positive direction
Influenced to positive direction
Identification of advocates / ambassadors
Baseline Low Medium High High
Impact on brand / revenue
Baseline Baseline Small Medium Large
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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!
© 2012 IBM Corporation
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.
What’s the book about?
Business and IT leaders who are looking for practical advice on how to drive immediate business results with analysis of big data
Who is this book for?
• 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 http://bit.ly/BigDataAnalyticsFlashbook
Where can you get a copy?
Big Data Analytics – New Book Launching at Information On Demand 2012!
Join us at Information On Demand 2012 in Las Vegas! Oct 21 – 25, 2012 Registration link - http://www-01.ibm.com/software/data/2012-conference/
© 2012 IBM Corporation
Big Data Analytics Book Description
Summary The Big Data tsunami is already hitting organizations - a set of disruptive technologies to drive game changers. 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 large number of workshops and interviews with business and IT leaders.
About Author
Dr. Arvind Sathi is the World Wide Communication Sector architect for the Information Agenda team at IBM. His primary focus has been in creating visions and roadmaps for Advanced Analytics at leading IBM clients in telecommunications, media and entertainment, and energy and utilities organizations worldwide. He has conducted a number of workshops on Big Data assessment and roadmap development.
Audience • mid to Sr. mgmt executives in network operations, customer service, sales, marketing, strategy or IT • IT service & software provider community • Industries covered – Financial services, Public services, healthcare, retail, telecom, energy & utilities, media & entertainment.
Next Steps
• Get a complimentary copy of the book at Information On Demand 2012 Book Store or request the IBM sales rep to order one for you • Request a briefing on Big Data Analytics for key stakeholders from IT and Business in your organization
© 2012 IBM Corporation 41