HP CMS CTO View on big data and analytics 2012

©2011Copyright Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice ©2011 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice

CME Industry Directions Aligning solutions to Megatrends

Jeff Edlund

CTO Communications & Media Solutions

©2011Copyright Hewlett-Packard Development Company, L.P.

Agenda

2

• Overview of CMS Portfolio

• Megatrends in the CSP Market

• Big Data / Analytics Intersection

• HP CMS Response

• New Innovations

– Live Customer Intelligence

– Personal Profile


CMS Portfolio

3

Megatrends in the CSP Market


Megatrends

5

• Analytics / Big Data – Movement from Systems of Record to System of Engagement

– Transforming data into insight enabling enhanced service experiences

• Globalization – Everything is becoming connected, always on and mobile

– Systems & service must move and interplay the way same as their users

• Social Networking / Communications – Rapidly replacing email and SMS as a preferred form of Communications

– Entirely new business models emerging as the Communications channel is now public

• Ecosystem Players – The pre-Smartphone ecosystem used to be the device + CSP

– New Ecosystems include: Device, Communications, Content, Applications, Mobility & Managed Experience

– Apple & Android currently dominate. Amazon will emerge in 2012

• Machine to Machine (M2M) – Not a new phenomenon but mobility opens up new opportunities

– Represents one of the clearest paths for new CSP revenue

©2011Copyright Hewlett-Packard Development Company, L.P. 6

HP observed investment directions

Investment PoR

New Capabilities

7

Big Data:

It’s more than just storing

bits


Everything is becoming

INSTRUMENTED

We now have the ability to measure, sense and

see the exact condition of practically everything.

INTERCONNECTED

People, systems and objects can communicate

and interact with each other in entirely new ways.

INTELLIGENT

We can respond to changes quickly and accurately, and get better results by predicting and

optimizing for future events.

WORKFORCE

MANUFACTURING

SUPPLY CHAIN

CUSTOMERS

TRANSPORTATION FACILITIES

IT


Data growth is massive

Volume of Digital Data Every day, 15 petabytes of new information are being generated. This is 8x more than the information in all U.S. libraries. In 2010, the codified information base of the world was doubling 11 hours.

Importance of Decision Making 70% of executives believe that poor decision making has had a degrading impact on their companies’ performance Only 9% of CFOs believe they excel at interpreting data for senior management

Analytics, modeling, and visualization of this data can help to run our systems more effectively


HP View of Big Data

10

• Velocity

– Moves at very high rates (think sensor-driven systems)

– Valuable in its temporal, high velocity state

• Volume

– Fast-moving data creates massive historical archives

– Valuable for mining patterns, trends and relationships

• Variety

– Structured (logs, business transactions)

– Semi-structured and unstructured


Big Data and the CSP

11

• Incoming CSP data streams are different

than traditional business apps

– Need to write data quickly & reliably, but …

• It’s not just about high speed writes

– Need to validate in real-time

– Need to count and aggregate

– Opportunity to analyze in real-time

– Need to scale on demand

– May need to transact


High velocity DBMS requirements

12

• Ingest at very high speeds and rates

• Scale easily to meet growth and

demand peaks

• Support integrated fault tolerance

• Support a wide range of real-time (or

“near-time”) analytics

• Integrate easily with high volume

analytic data stores

• Support millions of write operations per

second at scale

• Read and write latencies below 50

milliseconds

• Provide ACID-level consistency

guarantees (maybe)

• Support one or more well-known

application interfaces

– SQL

– Key/Value

– Document


Traditional Big Data use cases

13


Big Data Management Infrastructure Online

gaming

Ad serving

Sensor data

Internet commerce

SaaS, Web 2.0

Mobile platforms

Financial trade

Structured data ACID guarantees Relational/SQL Real-time analytics

NewSQL

Unstructured data Eventual consistency No Schema KV, document

NoSQL

Analytic Data stores


- moving from storing data to putting information into action Systems of Record vs. Systems of Engagement

• Systems of Record create efficiency

• Impossible to transact commerce without SoR

• Focus’ on cost, quality and contractual obligations

• Systems of Engagement create effectiveness

• Address the complexities of business relationships

• Create compelling customer interactions on-line in real-time

• What’s the correct architecture

• SoE’s operate on top of and in touch with SoR’s

• This requires an evolutionary infrastructure not a wholesale revolution

“Systems of Engagement” is a phrase coined by Geoff Moore


- what’s the big change Implications of SoE’s for the CSP

Systems of Record

• Command & control

• Transaction oriented

• Data centric

• Users learn the System

• Navigation, value, etc…

• Very safe & secure

Systems of Engagement

– Collaborative model

– Interaction oriented

– User experience centric

– Systems learn users:

• Wants, needs, desires

– Very baller & hipster

17

Transforming Big Data

through Analytics:

Customer Experience

Assurance


Real-time Analytics requirements

18

• Database should support a wide variety of high performance reads

– High-frequency single-partition

– Lower-frequency multi-partition

• Common analytic queries should be optimized in the database

– Multi-partition aggregations, limits, etc.

• Database should accommodate a flexible range of relational data operations

– Particularly relevant to structured data


Integrating with Analytical data stores

19

• Database should offer high performance, transactional export

• Export should allow a wide variety of common data enrichment operations

– Normalize and de-normalize

– De-duplicate

– Aggregate

• Architecture should support loosely-coupled integrations

– Impedance mismatches

– Durability


Based on: Competing on Analytics, Davenport and Harris, 2007

Degre

e o

f C

om

ple

xity

Standard Reporting

Ad hoc reporting

Query/drill down

Alerts

Forecasting

Simulation

Predictive modeling

Optimization

What exactly is the problem?

What will happen next if ?

What could happen … ?

What if these trends continue?

What actions are needed?

How many, how often, where?

What happened?

Stochastic Optimization

Descriptive

Prescriptive

Predictive

How can we achieve the best outcome?

How can we achieve the best outcome including the effects of variability?

Types of Analytics

- making use of the data sources available today The new face of Information Mgmt

Time shifted

Historical

Reporting

Elitist IM

Timely

Contextual

Relevant

Democratic

EaaS Mobility Social Networking

Usage patterns

Profile

Yesterday Today


Perhaps it is the Insights… Where’s the value - Data or Information?


… Perhaps it is the Insights Where’s the value - Data or Information?

cartridge paper tray price printer scanner software

0 -1 0 +1 0 +1

0.00

1.00

1 3 5 7 9 11 13 15 17 19 21 23 25

All HP Printers

% pos % neg

copy, fax, feature, photo, price, print, quality cartridge, driver, ink, installation, paper, software, usb

I feel Obligated to counter the bad reviews. The printer is just fine. I don’t know what people are complaining about regarding the software but it installed seamlessly and is intuitive in its operation. Even though the paper tray jams sometimes I am happy I bought this wonderful printer.

- transforming data into meaningful results

•Capture

• Integrate

•Classify

Information

•Analyze

•Process

•Govern

Insight •Predict

•Publish

•Personalize

Action

Point of view • Avoid boiling the ocean, progress steadily • Most CTO’s / CIO’s tell us they are stuck in the first stage • Unstructured data & rich content are huge problems to solve • Predictive analytics transforming Information -> Insight -> Action = $$$ • Feedback and measurement on Results is critical

Content Results

Business Value

Information Management Value Chain

- the data flood is here and begs the question: What do you analyze?

– XDR

– OM

– DPI

– SBC

– PCMD

– MDM

– HLR

– HSS

– Location

– LERG

– Billing

– Marketing

– PCRF

New analytical models & technology can provide usable advantage

What’s most important: Network data, Subscriber data, Application data, Market Information

– Performance

– Fault

– Probes

– Timers

– Topology

CSP Data Sources of today


•CDR • PCMD • xDR

• PM • FM

Sources Include: • LERG •Demographics • Segmentation

Sources Include: •HSS • Provisioning •Billing

Sources Include: •DPI • SBC • Social Network

Customer Centric

Sources Include:


Delighted Customers:

– services that work & play the way that they do

– billing plans that fit a variable lifestyle

– self customization as personal needs dictate

Customer Insights:

– that drive great experiences

– help you customize your offers

– allow you to massively personalize service delivery

as the CSP you have far more information at your disposal than OTT providers

Assuring the Customer Experience


HP CMS Big Data / Analytics Platform

28

Profile Data Device Data Network Data Usage Data Subscriber Data

UDR Broker Subscriber Data Mgr

Policy Marketing Charging

CEP – Data Exposure

IUM

IDOL 10 / Vertica

Solutions

Service Personalization Mobile Experience Personalization

Personalized Advertising

Real-time Profile Analysis & Exchange

Network Intelligence

Actionable Customer Experience

Management

Service Intelligence

Tethering / Usage Analysis


Pote

ntial Busi

ness

Valu

e

Business Event

Result Measured

Root Cause Determined

Corrective Decision Made

Action Taken

Action time

Effective Real-time Decisions

Dynamic Business Conditions

Point of Transaction Response to Dynamic Conditions

Business Automation Enables Fast Response

Real-time analytics = effective decisions

Innovations in Analytics

Social Analytics

HP Cloud Platform

IDOL Vertica

LCI Explore

Service Platform (API)

Offerings

http://awardsmeter.com/

Chameleon Personal Profile • Comprehensive and ubiquitous cloud Personality Profile

• Network element that collects, stores and analyzes personal data

• Gather data across multitude of user devices

• Builds individual opt-in Personality Profiles based upon Consumer behavior

• Algorithmically translate real-world data into effective actionable models

Open Eco-system Collect

Store

Analyze

Profile

Report

Charge

Secure

THANK YOU

HP CMS CTO View on big data and analytics 2012

Technology

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