18. Introductions
• Spend 2 minutes to introduce yourself
o Name, current employer and job
o Let us know your favorite hobby
• For me its hiking with my family
o What you want to get out of this course
• What topics are most important to you?
18
(c) 2012 Alan Quayle Business and Service Development
92. Big Data is Getting Bigger
2.7 Zetabytes in 2012
Over 90% will be
unstructured
Data spread across a
wide array of silos
93. Why is Big Data Hard (and Getting Harder)?
Changing Data Requirements
Faster response time of fresher data
Sampling is not good enough & history is
important
Increasing complexity of analytics
Users demand inexpensive experimentation
94. Where is it Coming From?
Computer Human
Generated Generated
• Application server • Twitter “Fire Hose”
logs (web sites, 50m tweets/day
games) 1,400% growth per
• Sensor data (weather, year
water, smart grids) • Blogs/Reviews/Emails
• Images/videos /Pictures
(traffic, security • Social Graphs:
cameras) Facebook, Linked-in,
Contacts
95. Big Data Verticals
Social
Media/Ad Life Financial
Oil & Gas Retail Security Network/
vertising Sciences Services
Gaming
User
Anti-virus Demographi
Targeted Recommen Monte Carlo cs
Advertising d Simulations
Seismic Genome Fraud Usage
Analysis Analysis Detection analysis
Image and
Transaction Risk
Video
s Analysis Analysis Image In-game
Processing
Recognition metrics
96. Bank – Monte Carlo Simulations
“The AWS platform was a good fit for its
unlimited and flexible computational
power to our risk-simulation process
23 Hours requirements.
to With AWS, we now have the power to
decide how fast we want to obtain
simulation results, and, more importantly,
20 Minutes we have the ability to run simulations not
possible before due to the large amount of
infrastructure required.” – Castillo,
Director, Bankinter
103. Decision
Engineering
Adaptive
Analytics
Predictive Analytics
Reporting
Data Management (including data
migration, data quality, data
modeling)
104. Decision
Engineering
Adaptive
Analytics
Predictive Analytics
Reporting
Data Management (including data
migration, data quality, data
modeling)
105. Predictive/Adaptive Analytics on 1 slide
Will this customer churn?
Yes/No data: If customer has an open trouble ticket: Yes, otherwise: No
Real-Valued: If customer age < 30: Yes, otherwise: No Pattern
Combination: If customer age <30 AND has an open trouble ticket: Yes,
otherwise: No
Linear Combination: If 2.3 x Age + 4.4 x Income > 40: Yes, otherwise: No
Predictive Analytics: Obtain these numbers by analyzing historical data
Adaptive Analytics: Update your historical data, and re-derive the numbers
periodically to take changing situations into account.
Nonlinear Analytics:
Income vs. Income
age
age
106. Decision
Engineering
Adaptive
Analytics
Predictive Analytics
Reporting
Data Management (including data
migration, data quality, data
modeling)
107. Decision Model (part of Decision Engineering)
From: Agile Decision Making: Improving business results with analytics
TM Forum Quick Insight report, 2011. Source: Lorien Pratt
…Decision engineering places analytics in the larger business
context. Each “f” here is an analytic, or based on human
expertise
108. 1
Data used to
construct the
analytic
3
2 5
Sally Sally is likely
Operational If 2.3 x Age + 4.4 x
data Income > 40: Yes, enough to
otherwise: No churn that
we should
4
call her
109. Key Distinctions
• Automated versus human-in-the-loop while building
analytics
• Automated versus human-in-the-loop while using
analytics
• Strategic versus tactical goals
• One-size fits all versus demographic versus personalized
• Within-silo versus between-silo
• Cleansing for operational versus analytic purposes
110. Moving Analytics to the Center: Retailers face new competition that is driving
an advanced view of customers and interactions to the center of the business.
How to dynamically Multi-Channel Operations How do I leverage and
manage margin and operationalize customer
brand perception with insights and experience
the right mix of regular, data to drive personal,
promotional and timely, and relevant
Merchandising Marketing & Sales
markdown products interactions across all
across categories, Advanced channels?
channels, and formats? Customer
Intelligence How do I create a
Are inventory and responsive analytics
demand data leveraged capability, and
Supply Chain Operations
to optimize the customer governance relative to
experience and the right-time
effectively respond to application of analytic
changing marketing decision making?
Supplier/Partner Collaboration
conditions?
112. The New Analytical Competency
Focus of Efforts in the Past New Competency Requirements
Large-scale Integration of All Data Connected Information & Analytics
Sources Governance for the Enterprise
Central Control of Meta Data and Provisioning Information & Insights to
Information Usage Point of Leverage
Developing the Most Technically Agile Analytical Modeling Processes &
Correct Analytical Point Solution Rapid Evaluation of Business Lift
Possible
Example-
FROM: How can we use all possible customer dimensions to predict
customer churn?
TO: What is the optimum behavior modeling framework to rapidly build
and deploy models applicable to multiple business objectives that change
over time?
113. Predictive Analytics
Historical Future Needs
Approaches Rely on Require a More
Static Data Dynamic Approach
• Propensity to Churn • Ability to intervene
• Propensity to Buy in customer
• Propensity to Pay interactions to
create desired
• Customer Lifetime
outcomes
Value
114. Problem Statements
Telcos are not traditionally nimble
Telcos look at customers in groups, not individually.
Telcos have very little idea what drives customer
behavior
Telcos have no idea how to influence customer behavior
Even if they knew how to influence customer behavior,
Telcos do not have the nimble decisioning tools required
to impact customer behavior in real time.
117. provides integrated solutions to enable
rapid decisions on big data for CSPs
Guavus delivers Unique ability to Patent pending Current
big data rapidly fuse huge streaming customers include
solutions, quantities of analytics leading wireless,
not just data from technology IP, and video
technology diverse sources proven over service
components 10+ years providers
118. Guavus at a Glance
Silicon Valley
Venture Backed • US HQ in San Mateo, CA, R&D Offices in India
Company • Raised $48 Million, 350 employees worldwide
• 3 of the top 5 NA mobile operators, 3 of the top
Tier-1 CSP 5 IP / MPLS backbone carriers, & CDN Networks
Customers &
• 4 of the top 6 largest global communications
Partnerships
infrastructure equipment vendors
• Mature (10+ years) patent-pending technology
Industry Proven
& Recognized
119. Guavus Empowers LOB to Make Decisions
Information Systems Devices & Networks
Enterprise
Apps
Networks
Databases
Data at Rest Data in Motion
Views Flows
Data
Warehouses
Finance & Network & Customer
Marketing Executives
Regulatory Operations Care & Sales
• Profitability • Traffic • Customer • Continuous • Churn Prediction
Analysis Engineering Segmentation Business • Focused
• Tiered Pricing • Capacity • Campaign Optimization Prospecting
Optimization Planning management • Predictive • Targeted Up-Sell
• Contract/SLA • Peering Planning & Cross-Sell
Enforcement Optimization
Data Collection, Fusion and Mining Across Disparate Data
Sources
120. Operator Challenges in a Big Data World
EXPONENTIAL DATA TIMELY DISTRIBUTED
[ STREAMING ] SITTING INSIGHTS NETWORK
DATA GROWTH IN SILOS GENERATION
121. Key Data Sources & Insights
CONTENT INTERNET CDN Streaming
PROVIDERS Analytics
Insights
Content trending
& consumption
Fused network
events
Subscriber
EDGE ACCESS CPE OR dynamic usage
NETWORK NETWORK END DEVICE profiles
Network usage
patterns
Policy control
functions
122. Transforming the Big Data Analytics Economic Model
Traditional Streaming Centric
Centralized, Store-First Distributed, Compute-First
Architecture Architecture
TRANSPORT
STORAGE
TRANSORT STORAGE COMPUTE COMPUTE
[ Insights ] [ Insights ]
RESOURCES & TIME RESOURCES & TIME
• Move processing to data edge
• Consolidate data in a repository
• Focus spend on analytics first
• Transport and store data- Transport
• Continuous processing yields timely and
and storage costs alone may put it
actionable insights
over budget
• Reduce overall spend per new analytics
• Project may not even get started
questions
• Leverage off the shelf low cost processing
and storage
123. Big Data Streaming Analytics Architecture
Analytics Applications Examples 3rd Party Feeds & Customer Tools
Data Market Capacity
Broadban Ad
Mobility Digital Warehouse Research Planning
d Targeting
Media s
Centralized Clustering
Master Master Business Machine
Compute & &
Fusion Aggregation Logic Learning
Classifying
Analyze
Distributed Site 1 Distributed Site 2 Distributed Site 3
Aggregation Aggregation Aggregation
Data Fusion Data Fusion Data Fusion
Streaming / Batch Local Streaming / Batch Local Streaming / Batch Local
Ingest Ingest Ingest
Data Store Data Store Data Store
Media Service
DPI PDN Flow & AAA Web Web Advertisin
Type Meta Consumption
Data Flows Routing Data Activity Taxonomy g Traffic
Data Traffic
Data Sources
124. Guavus Analytics Platform Details
Guavus Applications Customer UI Portals Insight Discovery 3rd Party System Support
Mobility Reflex Consumer Guavus External API Network Management,
Reporting & POC Sandbox Field Inventory, etc.
IP Reflex
Enterprise
CDN Reflex Reporting
Ad Reflex API
Data Stores (IT, DWH, Cloud)
Cube API HBASE API
SQL SQL/Hive Ingest Export
Processing Pipeline
Caching Compute Nodes … XDR
Guavus Stream
( Bus Cubes, Machine Learning Caching )
Analysis Store Traditional
ETL Layers
Central Compute
( Fusion, Aggregation & Compute )
Data Store
Distributed Data Distributed Data Distributed Data
Collectors Collectors Collectors
Inventor
DPI PCMD IPDR NetFlow RADIUS DNS … PM / FM CRM
y
Streaming Data Feeds