Slides: EMC Data Value Tutorial

1© Copyright 2016 EMC Corporation. All rights reserved.

INTRODUCTION TO DATA VALUESTEVE TODD, EMC FELLOW, VP OF STRATEGY AND INNOVATIONJULY 6, 2016, BIS 2016 LEIPZIG GERMANY


1. Introduction to Data Value2. Data Value IT Architectures

SESSIONS


1. Introduction to Data Value- Introductions: What is Data Value?- San Diego Supercomputer Center- Product to Data Value- Industry Challenge- Emerging Use Cases- Audience Discussion

2. Data Value IT Architectures

SESSION1


DR. JIM SHORT, SAN DIEGO SUPERCOMPUTER CENTERARCHITECTING FOR VALUE

Valuation IT ArchitecturesValuation Business Processes


Analysis of the vast collections of GIS data we have or have access to is not merely generating new exploration, it has become a salable service on its own right.

— Australia Oil & Gas

CAPGEMINI EMC BIG DATA REPORTFROM PRODUCT VALUE TO DATA VALUE

Source: Forbeshttps://www.capgemini.com/news/new-global-study-by-capgemini-and-emc-shows-big-data-driving-market-disruption-leaving-many

Among our respondents, 63% consider that the monetization of data could eventually become as valuable to their organizations as their existing products and services.

— CapGemini EMC Big Data Report


BABOLATEXAMPLE: PRODUCT TO DATA VALUE


ADIDAS SMART BALL EXAMPLE: PRODUCT TO DATA VALUE


LANTMATERIETEXAMPLE: PRODUCT TO DATA VALUE


Source: WSJhttp://www.wsj.com/article_email/whats-all-that-data-worth-1413157156-lMyQjAxMTE1OTE1NDUxMDQ5Wj

companies have better accounting for their office furniture than their information assets

ARE WE GETTING A GOOD DEAL?


VALUATION BUSINESS PROCESSESM&A

CREDITORVALUATION

DATA INSURANCE DATAMONETIZATION

DATA SALE


DATA VALUE AND ACQUISITIONS

Source: https://press.linkedin.com/site-resources/news-releases/2015/linkedin-to-acquire-lyndacom

CALCULATIONS $1,500,000,000 90% $1,350,000,000Acquisition cost per tutorial $9,269.24Acquisition cost per GB (uncompressed) $112.88

Lynda.com’s extensive library of premium video content helps empower people to develop the skills needed to accelerate their careers.

— Jeff Weiner, CEO of LinkedIn

http://www.wsj.com/articles/in-caesars-fight-data-on-players-is-real-prize-1426800166


UPDATE: MICROSOFT ACQUISITION

Source: http://www.wsj.com/articles/microsoft-to-acquire-linkedin-in-deal-valued-at-26-2-billion-1465821523

Sales representatives using Microsoft’s Dynamics software for managing customer relationships could pick up useful tidbits of background on potential customers from LinkedIn data. Microsoft also sees opportunities in Lynda.com, a channel for training videos that LinkedIn bought for $1.5 billion last year. Microsoft will be able to offer Lynda’s videos inside its own software, such as Excel spreadsheets.

— Wall Street Journal, June 14 2016


http://www.wsj.com/articles/microsoft-to-acquire-linkedin-in-deal-valued-at-26-2-billion-1465821523

http://www.wsj.com/articles/microsoft-to-acquire-linkedin-in-deal-valued-at-26-2-billion-1465821523


BANKRUPTCY

Source: http://www.wsj.com/articles/in-caesars-fight-data-on-players-is-real-prize-1426800166

DEFENDANT’S LOOTING OF CEOC’S VALUABLE OPERATING ASSETS

Date of Transfer

Asset Transferred

Conservative Estimated Equity Value

Equity Value Attributed

Equity Valuation Shortfall - $

Equity Valuation Shortfall - %

May 2014 Total Rewards $1.0BN None $1.0BN 100%Total $5.9BN $2.4BN $3.6BN 60%



• 23andMe– 800,000 Customer DNA Kits since 2006– $99 per test ($79.2 million)

• Genentech– Upfront payment of $10 million– Further milestones as much as $50 million

DATA MONETIZATION

Source: Forbeshttp://www.forbes.com/sites/matthewherper/2015/01/06/surprise-with-60-million-genentech-deal-23andme-has-a-business-plan/

…this single deal with one large drug company could generate almost as much revenue as doubling 23andMe’s customer base.

— Forbes Article


DATA SALE

http://adexchanger.com/ecommerce-2/tesco-eyes-sale-of-dunnhumby-its-nearly-1-billion-shopper-data-business/

Tesco said it has appointed Goldman Sachs as its adviser to explore “strategic options” for the US$756 million business

[dunnhumby]…has a unique frame of reference on the purchase habits of 770 million shoppers


DATA INSURANCE

Liberty Mutual• 30% increase in primary data insurance

policies between 2013-2014

Source: Boston Globe http://www.bostonglobe.com/business/2014/02/17/more-companies-buying-insurance-against-hackers-and-privacy-breaches/9qYrvlhskcoPEs5b4ch3PP/story.html

TJX• 46 million credit/debit cards• Estimated cost $180 million


• What data-related business processes add revenue to the bottom line?

• What data-related business processes subtract costs from the bottom line?

• What characteristics of data can be used to calculate value?

AUDIENCE DISCUSSION


• Sell• Rent (or provide data services)• Monetize (analyze to increase revenue or cut costs)• Data/Cyber Insurance claim

COMPARE YOUR ANSWERS

• Cost to process data• Cost to store data• Cost of premiums for data/cyber insurance• Cost to purchase a data asset• Cost to acquire a company’s data• Regulatory fines for data violations• Data science staff

PLUS

MINUS


1. Introduction to Data Value2. Data Value IT Architectures

– Introduction to Data Lakes• Data Lake Architecture• Data Lake Industry use cases

– Adding Valuation to Data Lake Architectures

SESSION2


MORE DATA MEANS MORE COMPLEX RELATIONSHIPSTO ANALYZE IN REAL-TIME AT A LARGE SCALE

Amount of Data

4.4 Zb

2013 2020Source: IDC 2014

16+ ZbHot Data

44 Zb

The Data Multiplier Effect

Business

Human

Machine

1X 10X 100XDatabase Data

VOLUMEVARIETYVOLUME

VARIETYVOLUMEVELOCITY

Enterprise/External Data

Sensor/External Data

Satellite Imaging

Sensors

Video RecordingM2M Log

Files

Bio-Informatics

Email Documents

Web Logs Social

• More Data Needs To Be Captured Faster

• Real-time Analytics For Business Insights

• Existing Applications Are Taxed

• Evolving New Applications & Architectures


BRING TOGETHER DATA, ANALYTICS, & APPS

ANALYZE ANYTHINGAll of the dataMore sophisticated analysesNew combinations and correlations

STORE EVERYTHINGStructured, unstructured, darkGenerated by the enterprise, imported from outsideHistoric & real-time

SPEEDANALYTICS

APPS

DATA

BUILD THE RIGHT THINGDeliver data consistently & in a standardized way Get at the data quicklyBuild views and applications each user really needs


INGEST STORE ANALYZE SURFACE ACT

Ingest data in real-time,

near real-time batch/micro-

batch.

Open HDFS storage allows

access from the full-stack of analytics

tools.

Apply the latest

machine learning and data science techniques.

An open platform for visualization

of results and data products.

And an application

development platform to

act on findings.

DATA LAKE - ATTRIBUTES



Data Streaming Reference ArchitectureData Feeds Transactional Apps Analytic Apps

Data Stream Pipeline

DistributedComputing Real-Time Data Expert Systems &

Machine LearningAdvancedAnalytics

HDFSData Lake


FEDERATION BUSINESS DATA LAKE PLATFORMDATA & ANALYTICS CATALOG

(THIRD PARTY APPLICATIONS)

HADOOP

OPEN DATA PLATFORM

PIVOTAL BIG DATA SUITEADVANCED ANALYTICS

DATA PROCESSING

APPS AT SCALEGREENPLUM DATABASE HAWQ

PIVOTAL HDSPARKSPRING XD

DATA SERVICES MANAGEMENT

ANALYTICS TOOLBOX

REDIS

RABBITMQ

GEMFIRE

BDS ON PIVOTAL

DATAMANAGER

DATAGOVERNOR

INGEST

INDEX & SEARCH

POLICYMGMT

SECURITY & ACCESS

CONTROL

VIRTUALIZATION PIVOTAL CLOUD FOUNDRY

EMC II STORAGEDATA LAKE FOUNDATION: ISILON | ECS

VCE VBLOCK | XTREMIO


DATA LAKE ACCESS METHODS

FILE

HPC

Backup/Archive

Analytics

Mobile

File Shares

Cloud Apps

FILE



DATA: ANALYTICS-READY STORAGE CHOICES

ISILON, ECS Scale compute & storage independently HDFS-enable existing data No single point of failure Easily import & export via next-gen communication;

including HDFS, S3, Swift and Atmos API support Fault-tolerant, end-to-end data protection Self-service provisioning Storage hardware choice: enterprise, commodity

CONSOLIDATE DATA STORAGE THROUGH MULTI-PROTOCOL ACCESS

Real

tim

eBa

tch

Hadoop

Analytics

Surface

ActCloud

Archive

Mobile

HPC

Shares


DATA LAKE ARCHITECTURE/USE CASES

……

.

8 PetabytesGenome

Sequenzed Data

• 20.000 Oncology Samples per Year• 40-50GB per Sequence Session• Historical Data for Cancer Analysis


Mission statement

“To provide knowledge, services and solutions to fulfil Radboudumc research ICT needs, in a way that fits the individual study. The solutions includes a Digital Research Environment ( DRE) which allows researchers to import, merge, optimize, store, analyse, archive and share data from various sources (local and (inter)national) in a single scalable digital environment per study. The use of this digital environment increases research study efficiency and output, thereby increasing scientific impact of the Radboudumc. The sustainable, secure, law compliant infrastructure places the Radboudumc in a key position as scientific partner.”

The mission


DRE positioning

Education

Digital Learning Environment (DLE)

Care

Electronic Medical Record (EMR)

Research

Digital Research Environment (DRE)


DRE-mandate: valorize the new EMR ‘Epic’

EMR

DRE

‘Paddy Field’


DRE

Import

Merge

Optimize

StoreAnaly

se

Archive

Share

DRE-mandate: increase efficiency and output


Datamanagementat this moment

© Caspar Terheggen, Radboud Universiteit

& LAW

DRE-mandate: modernize


© Caspar Terheggen, Radboud Universiteit

Virtual research workspace

DRE-mandate: modernize


Local key management

Uitwisseling collega’sSharing

High performance computing

Data ponds

Secure Research Environment

Standards

Analysis & Reporting

Archiving

Multi centersources

Source disclosure

Pseudonymisation Merging

DRE: study example


Hybrid CloudPrivate Public

Platform as a ServiceLight Opensource Frameworks, Services, Data and Analytics

Orchestration Automated Provisioning and IT Infrastructure Portal

Converged Infrastructure IT Transformations to Service Delivery

Standardize, Virtualize, Automate

Research as a ServiceBuilding new apps for competitive advantage in market, the

new business

Research Self Service PortalEnd User Self Service with Measured SLA’s

Vendor inventory: architecture


BITBW (State SP for State of Baden-Württemberg) – EMC ECS-Electronic Record Archiving in Criminal Justice

Department– Cooperation with ISV and their SW (PDV Systeme)– Starting 1st Jan 2018 Electronic Comm. between Lawyers and Justice Courts will be fully electronical without any Media Break– Justice Courts are fully digital


I. Traditional / Horizontal Use Cases– Email Archives– File Shares / Home Directories– VDI (user data)– vCAD Workstation Virtualization– Backup + Archive– Video Surveillance

II. Engineering Use Cases– Computer-Aided Design– Computer-Aided Engineering– Advanced Driver Assistance Systems, ADAS

III. Emerging Use Cases– Hardware in the Loop / Simulation– Connected Cars– Analytics

IT IN AUTOMOTIVE

Source: Pictures by Bosch


Car Supplier - Typical Workflow

Labeling

SIL-HIL computing

Data Ingest

Label-data

Validation

Developer

Tape library

Import,Cut&Compress

144*

X400

SIL = Software in the LoopHIL = Hardware in the Loop


CAR SUPPLIER INFRASTRUCTURE (GLOBAL)

144*X400

3*X400

Plymouth

Germany

IP

California

Replication of selected data over Aspera *3*X400

Replication of selected data over Aspera *


Typical HiL Environment for adas develoment

SMB / NFS

Write (Ingest)

Read (simulation)

2PB – 20 PB HiL Server


VALUATION APPROACHES

ApplicationAgility

Content Workflow

ContentProcessing

Data Protection Ecosystem

ContentIngest


CONTENT PROCESSING

NLP, Translation, Stemming, Tokenization

Domain A Domain B Domain C Domain D

Valuation Algorithms


DATA PROTECTION ECOSYSTEM

Backup Schedule/Catalog

Backup Data

Valuation Algorithms

Mappings Between Primary/Protection

System

P1 B1

P2 B2

Schedule Num Copies

(x)

Catalog V1

V2

V3


CONTENT INGESTSpout

1Spout

2

Bolt 2

Bolt 1

Bolt 3

Bolt 4


APPLICATION AGILITY

APPS

DATA

SPEED

ANALYTICS

Zero Downtime Upgrade to Production

Commit Code

Change

1

Automate Build & Test

(Unit Test, Static Code Analysis)

2

Store Binaries &

Build Artifacts

3

Automated Integration

Testing

4

Acceptance,Performance

& Load

5 6


CONTENT WORKFLOW

End User End User End User

Driver

Source

Driver DriverDriver

Driver DriverDriver

Source Source Source Source Source

Driver DriverDriver

Driver Driver

Driver Driver


DATA SCIENCE EXAMPLE

Final Report & Business Recommendation$29M

Labeled DiskArrayData

Labeled Disk ArrayDataDisk array data where each disk drive is being assigned to a label indicating failure or activity

DiskArrayData

Product_Tables

Customer History Table

Geneaology

GPO GeneaologyProvides a listing of all items in venwith tracking of their

TCE History TableProvides history tracking for drives & other parts in vendor equipment

Product TablesThe product table contains information at the part item number level, i.e., configuration parameters & meta data

Disk Array DataData collected from arrays installed at customer sitesContains many places of information regarding configuration & parts as well as error information for disk drives

DiskArray

Enriched w/EMC

SN

Data Scientist: John SmithTool: Greenplum DBActions: Identify failed from non-failed drives

Disk Array DataEnriched with EMC SNDisk array data where each row disk drive serial number is mapped to WMC internal disk drive serial number

Data Scientist: John SmithTool: Greenplum DBActions: Join the product meta data with the drive data

Data Scientist: John SmithTool: Greenplum DBActions: Map serial numbers

Data Scientist: John SmithTool: Greenplum DBActions: Identify disk drives & map raw drive serial numbers to serial numbers


• Async replication is never perfect– Inherent data lag between production and replica – This is unprotected data; will be lost in disaster– Data loss drives monetary loss– We aim to minimize monetary loss in a case of disaster

• Optimize replication resources for minimize data loss’s costs

VALUE-DRIVEN DISASTER RECOVERYMINIMIZE MONETARY LOSS IN DISASTER

3 111 219 327 435 543 651 759 867 975 1083 1191 1299 1407 1515 1623 1731 1839 1947 2055 2163 2271 2379 2487 2595 2703 2811 2919

ASSUME DATA A IS X TIMES MORE IMPORTANT THAN APP B

Non optimized Business optimized (*2) Business optimized (*4) Business optimized (*10)

Time (secs)

BusinessDamage in

Disaster

(x=2)(x=4)

(x=10)Peleg Yiftachel, Udi Shemer, Omer Sagi


Learn about the value, opportunity, and insights that Big Data provides. Get introduced to the Federation Business Data Lake solution to leverage the full power of big data to drive major business strategies.https://educast.emc.com/learn/data-lakes-for-big-data-archive-2015 http://stevetodd.typepad.com

EMC HELPS WITH MOOC, BLOGMASSIVE OPEN ONLINE CURRICULUM

https://educast.emc.com/learn/data-lakes-for-big-data-archive-2015




http://stevetodd.typepad.com/

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Slides: EMC Data Value Tutorial

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