inform innovate accelerate optimize how telcos can … · inform innovate accelerate optimize how...

© 2013 TM Forum | 1

V2013.4

inform innovate accelerate optimize

How telcos can

benefit from

streaming big data

analytics

Sponored by:

#streamingbigdataanalytics

https://twitter.com/hashtag/streamingbigdataanalytics?src=hash

© 2013 TM Forum | 2

V2013.4

Today’s Speakers

Adrian PasciutaDirector of Industry Solutions

Rebecca SendelSenior Director, Business Assurance Program

© 2014 TM Forum | 3

V2013.5


Data Analytics

Best Practices

TM Forum WebinarSeptember 2014

© 2014 TM Forum | 4

V2013.5

Big Data Analytics

Identification,

design and

deployment of

strategies,

processes,

skills, systems

and data that

can provide

actionable

intelligence

resulting in

business value

VarietyVolume

Velocity

VALUE

© 2014 TM Forum | 5

V2013.5

Biggest Challenges for Success

in Data Analytics

© 2014 TM Forum | 6

V2013.5

Planned/Expected Investment

Areas for Analytics

© 2014 TM Forum | 7

V2013.5

Data Analytics in TM Forum

Common Language

© 2014 TM Forum | 8

V2013.5

Big Data Analytics Guidebook

© 2014 TM Forum | 9

V2013.5

Data SourcesNetwork, OSS, BSS, Social Networks, …

Data

Repository

Str

uctu

red

Data

, U

nstr

uctu

red

Da

ta, S

em

i-str

uctu

red D

ata

CAPEX Reduction Applications

OPEX Reduction Applications

CEMApplications

Revenue Generating Applications

Other Applications …

Data

Govern

ance

Priva

cy,

Se

cu

rity

, a

nd

Com

plia

nce

Data AnalysisData Modeling, Metrics, Reports

Batch Streaming

Data IngestionIntegration, Import, Format

Data AnalysisData Modeling, Complex Event Processing, Alerts & Triggers, Reports

Data ManagementTransformation, Correlation, Enrichment, Manipulation, Retention

GB979: Big Data Analytics

Reference Model

© 2014 TM Forum | 10

V2013.5

Big Data Analytics Use Cases

Use Case ID Use Case

S-MOM-T1 Real-time Personalized Offers while Browsing

S-MOM-T2 Real-time Personalized Offers during Checkout

S-MOM-T3 Real-time Personalized Offers during a Live Interaction

S-MOM-T4 Real-time Personalized Offers Based on Location

S-MOM-T5 Real-time Personalized Offers Based on Usage

S-MOM-T6 Real-time Personalized Offers Based on Device

S-MOM-T7 Intelligent Advertising Based on Browsing History

S-MOM-T8 Stimulating Prepaid to Postpaid Conversion

S-MOM-T9 Enticing Usage from Roaming Customers

S-MOM-O1 Product Definition and Development

S-MOM-O2 Product Introduction Analytics

S-MOM-O3 Product Performance Optimization

S-MOM-O4 Purchase Propensity Analytics for Enhanced Targeting

S-SDM1 CSP Data Monetization

S-SDM2 MVNO Data Monetization

S-RDM1 Value-based Network Planning

S-RDM2 New Enterprise Order Impact Analysis

S-RDM3 Policy-based Capacity Management

O-CRM-CC1 Personalization of Real-Time Interaction in Assisted Care

O-CRM-CC2 Increase Effectiveness of Customer Self Service

O-CRM-CC3 Improving Assisted Care with Network Experience Analytics

O-CRM-PC1 Proactive Care

O-CRM-PC2 Right Proactive Care Channel and Time

O-CRM-PC3 Proactive Care Based on Poor Care Experience

O-CRM-PC4 Proactive Care During or After Network Fault

O-CRM-PC5 Proactive Care Based on Absence of Usage

O-CRM-PC6 Proactive Care Based on Network Experience Analytics

Use Case ID Use Case

O-CRM-PC7 Roaming Customer Onboarding

O-CRM-CR1 Churn Risk Prediction for Customer Retention

O-CRM-CR2 Churn Motivation Prediction for Customer

Retention

O-CRM-CR3 Personalized Offers for Customer Retention

O-CRM-CR4 Retention Offer Acceptance Propensity Analytics

O-RMO1 Network Fault Location and Recovery

O-RMO2 Real-time Value-based Congestion Management

O-RMO3 Real-Time Customer Offload Management

O-RMO4 Proactive Experience Driven Network Repair

O-SPRM1 Partner Value Optimization

O-FUL-O1 Increasing Conversion in the Ordering Process

O-FUL-O2 Reduction of Errors in the Ordering Process

O-FUL-I1 Optimization of Customer Self-Installation

O-FUL-I2 Field Technician Assignment Optimization

O-FUL-I3 Field Technician Arrival Optimization

O-BRM1 Revenue Assurance

O-BRM2 Personalized Collections Treatment Plan

E-SEP1 Market Watch

E-EEM1 Business Process Optimization

E-FAM1 Fraud Management

© 2014 TM Forum | 11

V2013.5

500+ Pre-defined Metrics

www.tmforum.org

Standards Menu

Tools

Frameworx

Metrics

Repository

© 2014 TM Forum | 12

V2013.5


Thank You!

© 2014. VITRIA TECHNOLOGY, INC. All rights reserved.

Streaming Analytics for Big Data:

What’s In It for CSPs?

Adrian PasciutaDirector, Industry Solutions

17th September 2014

© 2014 | www.vitria.com | 14

Topics

The TMF Big Data Reference Model

What is Streaming Analytics?

From Streaming Analytics to Operational Intelligence

Streaming Analytics in the Big Data Ecosystem

What Problems Does It Solve?

Customer Use Cases

Summary and Q&A


GB979: TMF Big Data Analytics Reference

Model

Data SourcesNetwork, OSS, BSS, Social Networks, …

Da

ta R

ep

osi

tory

Stru

ctu

red

Da

ta,

Un

stru

ctu

red

Da

ta, S

em

i-st

ruct

ure

d D

ata

CAPEX Reduction Applications

OPEX Reduction Applications

CEMApplications

Revenue Generating Applications

Other Applications …

Da

ta G

ove

rna

nce

Pri

vacy

, Se

curi

ty, a

nd

Co

mp

lia

nce

Data AnalysisData Modeling, Metrics, Reports

Batch Streaming

Data IngestionIntegration, Import, Format

Data AnalysisData Modeling, Complex Event Processing, Alerts & Triggers, Reports

Data ManagementTransformation, Correlation, Enrichment, Manipulation, Retention


Request-based (when you ask)

One-time evaluation

Bulk algorithms

Disk-based (usually)

Batch vs. Streaming Analytics

Event-based (when something happens)

Continuous evaluation

Incremental algorithms

In-memory (by design)

On-Demand Streaming

In memory

Prospective

Predictive

Proactive

Investigative

Retrospective

Reactive

Retrospective

Investigative

Reactive


Streaming

Analytics

Streaming Analytics

Required capabilities

• Correlate and Enrich- across diverse sources

• Correlate across Time- Track and trace

• Detect Patterns & Trends

• Advanced Real-time Analytics

• Predictive Analytics


Real-Time

VISIBILITY

Immediate

ACTION

Vitria Operational Intelligence

Powered by Streaming Analytics

• Respond quickly using Automated Processes& Guided Workflows

• Location and Situation Awareness

• Real-time Information in context

• Rich Dashboards

• User Empowerment

Streaming

Analytics

• Correlate and Enrich- across diverse sources

• Correlate across Time- Track and trace

• Detect Patterns & Trends

• Advanced Real-time Analytics

• Predictive Analytics


Vitria Operational Intelligence Architecture

Databases

& Files

Networks /

Sensors /

Devices

Social /

Weather /

Traffic

Systems &

Applications

In Memory Streaming Analytics

Share

d S

erv

ice

s &

RE

ST

AP

Is

Ad

min

To

ols

& U

tilit

y A

pp

s

Feed Layer

Visualization Layer

Action Layer

Output

Stream(s)Continuous

Queries

Input

Stream(s)

Data Integration LayerEvent Sources / Processing / Event Targets

ESB

Web

Services/

Messaging

Co

llab

ora

tive

De

ve

lop

me

nt To

ols

De

ve

lop

er

an

d B

usin

ess A

na

lyst


Streaming Analytics in Action:

A Network Event Processing Pipeline

Network Signalling EventsExample 250,000+ EPS

EPS = Events Per Second Events of Interest~2,500 EPS

Significant Events

~250 EPS

Stage 1: Filter & Enrich• Event Filtering• Duplicate removals • Event enrichment• Raw event statistics• Storage of events

enriched w/ context for look back/hypothesis analysis

Event Filtration

Enrich & Store

Stage 2: Use Cases• Complex Patterns and

Rules • KPI Computation• SLA measurement• Frequencies & scoring• Multidimensional

Analytics

Probable Match

Stage 3: Action• Confirmed trend to

predictive pattern• Process driven actions• Updates and Alerts• Guided workflows• Automated multi-step

actions• Visualizations• Stored calculations for

analysis

Prediction or Trigger

Network & IoT Data - Examples• Network signalling• CDRs• Telemetry / smart meter data feeds• Streaming data from weather / traffic

sources

Customer, Network

& Device Reference Data


Elastic Scalability

Scale out on commodity

hardware an elastic grid of

compute servers

Each use case defined as a

multi-step “Event

Processing Network” (EPN)

Each EPN contains multiple

“Projects” pipelined together

A Project uses Map-Reduce

to scale

Contextual (business) data

is preloaded into memory

Intelligent BPM enables

immediate action

AnalyticServer

AnalyticServer

AnalyticServer

iBPM

iBPM

AnalyticServer

AnalyticServer

AnalyticServer

AnalyticServer

AnalyticServer

AnalyticServer

Detect anomalous events

Correlate Customer data

Multidimensional analysis

Automated Actions,

Alerts, Workflows &

visualizations

Asset & Customer

Context

iBPM

Network events


Automated Triggers

• Matched patterns initiate intelligent alerts

Intelligent Actions

• Automated processes

• Guided human workflows

Advanced Analytics

• Multidimensional, duration, outcome…

• Predictive Analytics & Trending

• Prescriptive Analytics – Next best action

Intelligent Processes

• Adaptive process behavior

• Based on situational and social awareness, location awareness

Predictive Analytics & Intelligent Action



Big Data ‘In Motion’ & ‘At Rest’



Big Data ‘In Motion’ & ‘At Rest’

Event capture

Queries

Data & Result

Streaming



Complementary Big Data ‘In Motion’ & ‘At Rest’

Vitria OI + Hadoop Lambda Architecture

Raw events are sent to both Vitria OI and Hadoop

Vitria OI for streaming analytics (Speed Layer)

Hadoop provides historical storage and historical analytics (Batch layer)

Vitria OI can also query Hadoop to provide real-time insight with historical context

Event capture

Queries

Data & Result

Streaming

Batch Streaming


What Problems Can I Solve with Streaming

Analytics?

Real-time Network Optimisation

Real-time performance of all network cells

Real time dropped call detection

Adjacent cell performance (error perturbation)

Cells not reporting data

Predictive failure analysis

Real-time Customer Experience

Real-time dropped call detection and resolution for

VIP & Corporate customers

VIP ‘track and trace’ across the network

Real-time experience for roaming VIPs

Real-time Fraud & Security

Mobile originated spam detection

Mobile wallet fraud detection


What Problems Can I Solve with Streaming

Analytics?

Real-time Marketing

Real-time 1-to-1 marketing based on where the

customer has been, where the customer is, and a

prediction of future behaviour

Travel-related roaming offers

Real-time Revenue Optimisation

Real-time mobile data pricing

Analytics supporting new account / service

propositions such as Joint Accounts

Dynamic top-ups and dynamic charging based on

usage

Real-time Analytics for Internet-of-Things/M2M

Operational analytics for Smart Metering, Asset

Management, eHealth, Connected Car


Case Study: Telefonica O2 UKOperational Intelligence for Real Time Customer Experience

Competitive pressures make providing the best

customer service essential

Demands a shift from traditional service assurance to

real-time, 1-to-1 customer focus

Requires ability to process huge volumes of data from

the network in real time

Filter, correlate and enrich events of interest, visualise

and act on them in real time

Vitria OI provides real-time visibility, insight and action

across network events correlated with customer, network

and device reference data

Problem: Maintain Competitiveness by Maximising Customer Experience

Solution: Vitria Operational Intelligence Platform Big Data in Motion:250,000 events/sec.

~10 billion events/day

Largest Carrier in UK

Subsidiary of

Telefonica

7th Largest Telco WW

>320 Million

Customers


Network Visibility vs. Customer Insight: How to ensure the best service for VIP Customers?

Source: O2


Operational Intelligence at O2

#1: Real-time Network Situational Awareness

Real-time monitoring of network performance and faults

Worst performing cells (dropped calls)

Corporate and in-building cell monitoring

Cell “cluster” monitoring

Adjacent cell performance

Cells under detailed investigation

#2: Real-time VIP Customer Experience Monitoring

Real time Customer Experience for VIPs and High Value Accounts

VIP dropped call detection

“Tracked customers”—detailed customer experience tracking

Inbound roaming VIP customer experience

Automated escalation

#3: Real-time, Predictive 1-1 Marketing

Real-time, relevant offers based on where the customer has been, where they are now, and where you “predict” they are going

O2 Travel related products to customers about to roam off the UK network (Eurostar, UK airports)

Big Data in Motion:250,000 events/sec.

~10 billion events/day

Largest Carrier in UK

Subsidiary of

Telefonica

7th Largest Telco WW

>320 Million

Customers


Real Time Customer ExperienceHow to ensure the best service for VIP Customers?

Volume of DataVelocity of Data

250,000 events

per secondCellular

Network

CRM

Continuous Monitoring for anomalous events (Dropped calls)

Correlate among disparate sourcesCRM, network data, device DB, …

Continuous Real-time Analytics# VIPs affected per cell

Automated Actions Immediate action based on analytics

< 1 sec


Geo-map of call

failures by VIP group

Categorisation and

cause code of call

failure(s)

Call performance for

VIP group

List of call failures for

VIP group

VIP / HVA Call Failure Monitoring


Real-time, Situational 1:1 Marketing

10 million passengers/year travel on

Eurostar trains via the Channel Tunnel

between UK and Europe.

Many are O2 customers.

Most turn off data roaming just before

leaving the UK.

Opportunity:

Text them a great data roaming offer just

before leaving UK.

Challenge:

How to detect customers on the train?

Local Javelin trains share same routes

Highways next to train routes & stationsProblem:

Javelin Trains

share UK routes

Eurostar



Volume of DataVelocity of Data

250,000 events

per secondCellular

Network

CRMCorrelate among disparate sourcesCRM, …

Correlate location with train routeGeospatial (location) context

Track & trace passenger over timeEnsure that this is a train passenger

Correlate with train scheduleOnly track Eurostar passengers

Automate actions Text the roaming offer

In-Time – between Ashford & Tunnel

Route



Individual customer

journey drill-down

Route over time

visualisation

Customer detection

alerts

SMS notification

status


Real-time Analytics for IoT:

UK Smart Meter Implementation Programme

Largest M2M project in the world

23 million customer hubs, 53 million smart meters across the

southern half of the UK by 2020

Vitria OI will provide real-time operational management Power outage management, fraud detection, device troubleshooting

Coordination of process-based automated responses

Operational analytics


Summary

Vitria Operational Intelligence is a

streaming analytics platform

consistent with TMF Big Data

reference architecture

Complementary with Big Data at Rest

in Lambda Architecture

Combines real-time streaming,

discovery, analysis, visualisation and

action

Scalable, high performance at

extreme event rates

Enables innovation across multiple

operator business silos

STREAM

Continuously ingest massive

volumes of events and data

DISCOVER

Discover exceptions, patterns

and trends

ANALYZE

Correlate, analyze, and

predict outcomes

ACT

Respond proactively.

Seize Opportunities. Squash threats.


Operational IntelligencePowered by Streaming Analytics

Try our Interactive Streaming Big Data Demosvitria.com/big-data-demo

www.vitria.com

© 2013 TM Forum | 39

V2013.4


Q & A

Webinar sponsored by:

#streamingbigdataanalytics

https://twitter.com/hashtag/streamingbigdataanalytics?src=hash

inform innovate accelerate optimize how telcos can … · inform innovate accelerate optimize how...

Documents