how first to value beats first to market: case studies of fast data success

page

HOW FIRST TO VALUE BEATS FIRST TO MARKET: CASE STUDIES OF FAST DATA SUCCESS

Executive Webinar Series on Fast Data

page© 2016 VoltDB

EXECUTIVE WEBINAR SERIES: FAST DATA STRATEGY

1.  Fast Data for Competitive Advantage: 4 Steps to Expand your Opportunity

2.  How First to Value Beats First to Market: Case Studies of Fast Data Success

3.  Fast Data Choices: Strategies for Evaluating Alternative Business and Technology Options

2

page© 2016 VoltDB

OUR SPEAKERS

3

Peter VescusoCMOVoltDB

Niall NortonCEOOpenet

page© 2016 VoltDB

DATA IS TRANSFORMING BUSINESS

4

Broad content targeting to generic viewers Smarter, more individualized customer experiences

AUDIENCES

Content Metrics

INDIVIDUALS

Consumer Centric

From: AUDIENCES To: INDIVIDUALS

page

Big Data

“Perishable insights can have exponentially more value than after-the-fact traditional historical analytics.”

Mike Gual2eri, Principal Analyst, Forrester Research

Fast Data

DATA IS TRANSFORMING BUSINESS

page

FAST = ADVANTAGE

6© 2016 VoltDB

page© 2016 VoltDB

•  Forrester’s findings:•  Businesses can’t get the data they need fast enough

•  Data volume and variety are crushing business systems

•  The mobile mind shift hinges on data, e.g., metadata that data needs to be classified, linked, and exposed to create “mobile moments”.

7

Niall Norton, CEO, Openet

9 © Copyright 2016 Openet – Company Confiden=al For Use Under Non-‐Disclosure Only

•  Openet has always been about real-‐=me

•  Background is large scale transac=on processing, control and mone=za=on of data for communica=on service providers

•  Wanted to take it to the next level to enable smarter engagement for our customers

•  This helps communica=on service providers grow to be able to work and beOer compete with OTT and content providers (Google, Facebook, Amazon, Skype, NeTlix, Skype, Spo=fy, etc)

•  Enables communica=on companies transform to be Digital Service Providers

Openet – Why Fast and Smart Data is Crucial


Openet – Mee=ng the Needs of The Digital Service Provider


•  Advanced PCC -‐ The world's most advanced Policy and Charging suite

•  Big Data Prepara2on -‐ Turn big data into smart data that delivers real business benefits

•  NFV -‐ Openet’s solu=ons are all fully virtualized providing the founda=on for faster =me to market, reduced implementa=on and upgrade =me

•  CEM -‐ Having smart data available to provide a holis=c view of all customers as well as understanding customer context in real-‐=me enables personalized marke=ng offers

•  Network Op2miza2on -‐ Improve quality of experience, reduce cost and maximize revenue through efficient and proac=ve management of network resources

Openet Exper=se


Openet Enables Smarter Engagement


•  A higher performance, in-‐memory database that could combine the capabili=es of an opera=onal database, real-‐=me analy=cs, and stream processing in one easy-‐to-‐use plaTorm.

•  An in-‐memory database that could handle fast data

•  Database technology that would be complimentary to our innova=ve soaware solu=ons and suitable for virtualized deployments.

•  A database that was elas=cally scalable and could grow and contract as needed.

•  The result – Openet is now rolling enabling smarter engagement at many of the most innova=ve service providers in world.

To Deliver Smarter Engagement Openet Worked with VoltDB to Deliver:


•  Smarter Engagement with Customers – use smart data and enable a beOer customer experience and enable service providers to compete for a bigger share of customers’ digital spend.

Smarter Engagement with Customers

How do you become more relevant to your customers?


• Smarter Engagement with Real-‐2me Data – understand customer context in real-‐=me. Use this to push personalized, contextually aware offers.

Smarter Engagement with Fast Data


•  Smarter Engagement with Technology – using NFV to run smarter systems, including real-‐=me charging and policy

Smarter Engagement with Technology


• Smarter Engagement with Exis2ng Systems -‐ reconfigure legacy/diverse networks and systems

Smarter Engagement with Exis=ng Systems

Be Digital Ready -‐ ‘Best of Breed ‘ adjunct approach enables fast track system transforma2on


Sample Use Cases: Used by Many of the World’s Most Innova=ve Service Providers

Shared Data -‐ Enterprise

Video Op=miza=on

Access type Policy

IN Replacement

Audience Measurement

Tradi=onal Media=on

Time of Day Pricing

Conges=on Management

VoLTE Service Enablement

Spend No=fica=ons and Bill Shock Control

Device Type Policy

Bandwidth on Demand

Fair Usage

Service Tiers

Time-‐based Service Pass

Parental Controls

Dual Persona (BYOD)

Data Volume / Speed Tiers

Data Roaming Service Pass

Data Roaming No=fica=ons

Content Bundles with OTT Services Applica=on Service Pass

Fast Device / Service Rollout

Device Tethering

Real-‐Time Contextual Offers

Shared Data -‐ Mul= Device

Network Selec=on Intelligence

Shared Data -‐ Mul= User Account

Data Giaing

Sponsored Data


Chosen by Leading Service Providers


Ope

net

Best of breed

Cross functional for growth & innovation

“Big stack” guys Quality

Applicability

Flexibility

Compatibility

Expansibility

Performance

Quickly redesign services for a dynamic market

Gets along well with other systems

Cloud ready, hardware agnostic

Industry leading

Afterthought or offloading altogether (e.g. NSN)

Closed silo designed for yesterday

Submit change request. Cross fingers.

Vendor lock in

Proprietary

Demand Overwhelms

Why We’re Different


•  Telecoms is transforming

•  Everyone had a strategy but need the flexibility to adapt in =mes of change

•  Those who don’t best adapt to change will be lea behind

•  Legacy way of doing business and systems will soon be obsolete

•  Not just about big data. It’s using data in a fast and smart way to drive change and open new revenue streams

•  It’s about enabling change

Summing Up – Openet and VoltDB

page© 2016 VoltDB 22

From Development to Release

First to Market First to Value

From Development to hitting Sales and Profitability Goals

When First to Market doesn’t lead to First to Value, it’s due to either the wrong solu7on or the wrong technology pla;orm.

versus

page© 2016 VoltDB

FIRST TO VALUE WITH FAST DATA – THE CHALLENGES

•  Fast data applications have different technology requirements

•  Early adoption of technology doesn’t guarantee success

•  Many technology options

•  Need to pick the business and technology strategy that’s right for you

23

page© 2016 VoltDB

Batch/IterativeAnalytics+

Big DataFast Data

Rapid Data Ingestionand

TransformationStreamingAnalytics

Operational Interaction/ Transactions

COMPARISON OF FAST AND BIG

page© 2016 VoltDB

COMPETITIVE STRATEGY DRIVES TECHNOLOGY AND DATA MANAGEMENT REQUIREMENTS

25

Hyper Personalization

Real-Time Resource Management

Real-time Policy Enforcement

IoT & Sensor Data

page

WHAT’S YOUR CORE COMPETENCY?

-  CUSTOMERS AND APPLICATIONS

-  DISTRIBUTED SYSTEM INFRASTRUCTURE

26© 2016 VoltDB

page© 2016 VoltDB

EVALUATION CRITERIA

Criteria Considera2ons

Data Volume & Velocity Capacity to ingest, process and export at speed of data

Response speed, Performance Need for interac=ve, real-‐=me

Personaliza=on Batch vs con=nuous event processing

Accuracy, Data Consistency Is data high value, cri=cal?

Scalability Accommodate rapid growth. Cloud-‐ready

Standards SQL for data abstrac=on vs Applica=on heroics

Skill Set Specialty open source skills, e.g., Cassandra

27

page

SOME TECHNOLOGY OPTIONS…

28© 2016 VoltDB

page© 2016 VoltDB

THE “DIY” DATA INFRASTRUCTURE

29

GlueCode

GlueCode

Community Supplied You write this

Zookeeper

page© 2016 VoltDB

THE “DIY” DATA INFRASTRUCTURE

30

GlueCode

GlueCode

Community Supplied You write this

Zookeeper

Implications-  Need a specialized skill set-  Development: more work to write glue code, test and QA system for potential failure modes-  Support: test and maintain “glue” code with each component release

Bottom line: -  More $ invested in developing data infrastructure-  Longer time to value

page© 2016 VoltDB

THE “DIY” DATA INFRASTRUCTURE VS VOLTDB

•  Rigorous testing and QA•  1/4th of the components•  Simpler, Faster•  SQL and Java•  Easier to test, maintain applications

GlueCode

GlueCode

Zookeeper

page© 2016 VoltDB

BATCH PROCESSING VERSUS CONTINUOUS EVENT PROCESSING

•  Batch processing is an efficient way of processing large volumes of data•  Collect – Process – Report

•  Fast data processing involves a continuous process; each event is treated individually•  Ingest - Analyze - Act

32

page

BATCH PROCESSING

33

Event OccursAnalyze, Gain Insight

Take Action

Collect Data Process Data Act on the Data

TimeNow Later

page

CONTINUOUS EVENT PROCESSING

Analyze, Gain Insight

Take ActionEvent Occurs

34

TimeNow Later

page© 2016 VoltDB

SQL VERSUS NOSQL

35

•  SQL (structured query language) is for relational databases

•  Powerful query language

•  Standard and widely adopted

•  Flexibility - abstracts application from the data

•  ACID transactions – ensures immediate data consistency, reliability

•  NoSQL

•  Analytics are difficult/painful due to ridged data model

•  Non-standard programming interface (each product is different)

•  Lack of SQL and ACID transaction guarantees drives complexity to the Application

Ø Data integrity becomes the job of the Application developer

page© 2016 VoltDB

Personalized trade recommendations

Business challenges:-  “Interactive” speed-  Personalized offers-  Data accuracy,

integrity (compliance)-  Multiple data sources

CASE STUDY: FINANCIAL SERVICES

page© 2016 VoltDB


38

Data Sources

Rules Engine

In-Memory Grid

AppApp App

•  Event data from multiple sources

•  Each application database replicates to Cassandra and Hadoop

•  In-memory grid used to maintain logic and publish ‘state’ back and forth

•  Rules engine with fast access to Cassandra

•  MySQL used for slow-changing data

page© 2016 VoltDB


Resultsü  Simplified system architecture

ü  Immediate data consistency

ü  Real-time recommendations

ü  Faster time to value

41

page© 2016 VoltDB

CASE STUDY: MEDIA AND ENTERTAINMENT

Content Delivery Network Service Provider

Business challenges:-  Real-time analytics for customers

-  Data accuracy: over/under billing

-  Scalability

42

page© 2016 VoltDB


44

Resultsü  Simplified system architecture

ü  1/10th the compute resources

ü  100% budget accuracy, eliminated $$$ under/over spending

ü  Faster time to value

“We chose to go with VoltDB over other streaming aggregate solu2ons (like Trident) for its SQL interface, real-‐2me Ad-‐Hoc queries over our raw data, and simpler overall design” Behzad Pirvali, Architect, MaxCDN

page© 2016 VoltDB

CASE STUDY: INTERNET OF THINGS

IoT Device Manufacturer Platform-  Smart devices, appliances

Business challenges:-  High volume and velocity of data from

smart devices-  Complexity (multiple ingest points, apps,

databases) -  Performance – need to automate action on

inbound data at the velocity of the feeds

page© 2016 VoltDB


46

Device Data

Rules Engine

In-Memory Grid

AppApp App

•  Device data flows from cloud from multiple devices, appliances

•  Each application database replicates to Cassandra and Hadoop

•  In-memory grid used to maintain logic and publish ‘state’ back and forth

•  Rules engine for intra-day data to trigger actions (e.g., ‘turn lights on’)

•  PostgreSQL used for dimension data

page© 2016 VoltDB


Resultsü Simplified system

architecture

ü Single ingest point for high-velocity feeds of inbound data

ü Faster time to value

49

page© 2016 VoltDB

QUESTIONS?

•  Use the chat window to type in your questions

•  Try VoltDB yourself:

Ø  Free trial of the Enterprise Edition:

•  www.voltdb.com/Download

•  Email us at: [email protected]

51

how first to value beats first to market: case studies of fast data success

Software