TAKING A QUANTUM LEAP IN DIGITAL TRANSFORMATION · Digitalized Work Orders Machine vibration data...

© Copyright 2016 EMC Corporation. All rights reserved. EMC CONFIDENTIAL—INTERNAL USE ONLY EMC CONFIDENTIAL—INTERNAL USE ONLY

TAKING A QUANTUM LEAP IN DIGITAL TRANSFORMATION MARK SEAR, BIG & FAST DATA CTO, EMC GLOBAL SERVICES EMEA

EMC CONFIDENTIAL—INTERNAL USE ONLY EMC CONFIDENTIAL—INTERNAL USE ONLY

© Copyright 2016 EMC Corporation. All rights reserved.

THERE ARE THREE TYPES OF COMPANY

© Copyright 2016 EMC Corporation. All rights reserved. 3 © Copyright 2015 EMC Corporation. All rights reserved.

Digital Exclusive


Digital Exploit


Company 2003 Value (in Billions)

Amazon $23.90

Apple $8.90

Facebook Not yet founded

Google Private, four years old

Pandora Private, three years old

Netflix $1.5

Total $34.30

SOURCE: CODE HALOS

2013 Value (in Billions)

$180.20

$515.40

$132.00

$355.20

$6.00

$21.70

$1,210.50

Industrial Model Competitor

Borders

Nokia

MySpace

Yahoo

HMV

Blockbuster

Total


$1.78

$87.50

$0.58

$29.60

$1.35

$4.00

$124.71


Bankrupt

$30.30

$0.04

$37.50

Bankrupt

Bankrupt

$67.84


EVERY BUSINESS IS BECOMING A DIGITAL BUSINESS


HOW NEW IS BIG DATA & ANALYTICS?


9 © Copyright 2016 EMC Corporation. All rights reserved.

HOW DO WE ADDRESS MODERN DAY DIMENSIONAL PROBLEMS?


MapMyRun.com

AND NEW SOURCES OF CUSTOMER, PRODUCT AND MARKET INSIGHTS ARE POPPING UP EVERY DAY

Sporting Goods Manufacturers

Sporting Goods Retailers Insurance

Companies

Healthcare Provider

Sports Nutrition

First Aid Products


TRADITIONAL EDW / BI ARCHITECTURES

•  Summarized data (small percentage)

•  Descriptive

•  Design up front

•  Long Refresh Time

•  Expensive

•  Very Accurate!!


BIG DATA BUSINESS DRIVERS

Real Time Disparate Granular Predictive

The Economic shift: We can now store 20-50x more data for the same cost with new tools!


VALUATION OF DATA HOW ARE BUSINESSES USING DATA TO OBTAIN VALUE?

$80 if predictive, $400k if reactive Shopycat – Gift Recommendations


INSURANCE COMPANY:

•  Insurance company leverages analytics in new innovative ways.

•  The company studies social networks to detect potential cases of medical fraud and identity theft.

•  It looks at speech-to-text call center data to find likely attrition candidates — who sound different than happy customers — and propose remedies.

•  It predicts the likelihood that certain disease management programs will succeed, since patients respond to treatment plans differently.

DETECT FRAUD, TREAT DISEASE, MAKE CUSTOMERS HAPPY


BIG DATA PROJECTS FAIL


BIG DATA PROJECTS FAIL


ITS ALL ABOUT THE STRATEGY

“Companies need a business

strategy that leverages Big

Data…


Let EMC Help You Discover

YOUR KILLER USE-CASE with a Big Data Vision Workshop


Demonstrate the potential value using data science techniques

BIG DATA VISION WORKSHOP

Align business and IT goals around big

data

Identify strategic

opportunities for big data

analytics

Prioritize key use cases by

assessing feasibility and

ROI

Recommend the appropriate

analytics engagement

and deployment

roadmap

Workshop Objectives

1 2 3 4 5

IDENTIFY WHERE AND HOW TO LEVERAGE BIG DATA ANALYTICS


• Present final Priority Matrix

• Present Data Assessments

• Present Use Case Roadmap

• Present POV proposal

• Prepare and explore the data

• Integrate external and “small” data sources

• Perform data science work

• Develop visualizations and build analytic models

• Develop mockups to show the “Art of the Possible”

Workshop

Explore

Next Steps Research Interview

• Determine target business initiative

• Identify Business & IT participants

• Identify candidate data sets

• Schedule interviews

• Secure sample data (5-6GB)

• Conduct Business & IT interviews and document findings

• Collect supporting data materials

• Identify other potential data sources (internal and external)

• Review targeted business initiative, interview findings, analytics, visualizations, and mockups

• Brainstorm decisions & questions

• Group into use cases and prioritize

TIMELINE


USING THE GRAPH THEORY: THE ENTWINED COMPLEXITIES OF DIAGNOSES’ PROCEDURES COULD BE CLASSIFIED AND GROUPED USING THE NETWORK COMMUNITIES


H

B

D

F

A C

E

N

J

Top Priority Use Cases: (B) Game mix rationalization (A) New player activation (C) Reinvestments optimization (D) Campaign effectiveness

Other Use Cases E. Game / floor optimization F. Credit decisions G. New player targeting H. Reactivation I. Floor operations J. Customer service K. Team members L. Cohorts / influence M. Social analysis N. Competitive analysis O. Cross-sell

G

I K

L

M O

CASINO USE CASE PRIORITIZATION


DATA SOURCES VALUE ASSESSMENT Cardmember LTV Calcula/on

Cardmember Gaming Profile

Game Mix Ra/onaliza/on

New Member Ac/va/on

Campaign Effec/veness

Reinvestment Op/miza/on

Internal Data Sources Slots 4 4 4 4 4 4 Tables 4 4 4 4 4 4 Guest Demographics 4 4 2 4 3 3 Guest Spend History 4 4 4 2 2 4 Ra/ng History 4 4 1 1 1 4 Hotel Spend 2 2 1 2 1 4 Show Spend 2 2 0 2 1 4 Floor Map 1 2 4 1 1 2 Promo/on Calendar 0 2 0 1 4 2

External Data Sources USPS 1 2 0 3 1 1 Zillow 3 3 1 4 1 1 Census 2 2 0 1 1 0 American Comm Survey 0 0 0 1 1 1 Facebook 2 2 1 2 1 2 TwiNer 1 1 1 2 1 2 Yelp 1 1 0 1 1 1 Twellow/Twellowhood 3 0 0 1 1 1 Unitedstatezipcode.org 1 0 0 1 1 1 Facebook (Ad op/on) 1 0 0 1 1 1


Data Feasibility Ease of Acquiring Completeness Accuracy Granularity Current Cost

Internal Data Sources Slots 4 4 3 3 4 4 Tables 4 4 3 3 4 4 Guest Demographics 4 4 4 3 4 4 Guest Spend History 4 4 4 3 3 4 Ra/ng History 4 4 3 4 3 4 Hotel Spend 2 2 2 4 3 4 Show Spend 2 2 2 3 3 4 Floor Map 4 2 3 3 3 4 Promo/on Calendar 2 2 3 3 3 4

External Data Sources USPS 4 4 4 3 3 4 Zillow 3 4 4 3 3 3 Census 3 3 3 3 3 4 American Comm Survey 4 4 4 4 3 3 Facebook 3 2 2 4 4 3 TwiNer 3 2 3 4 4 3 Yelp 3 4 4 4 4 3 Twellow/Twellowhood 4 2 3 2 2 4 Unitedstatezipcode.org 4 4 4 4 3 4 Facebook (Ad op/on) 3 3 3 3 4 3

DATA SOURCES FEASIBILITY ASSESSMENT


+ -‐

Winnings Traffic Fraud

ACTIONABLE DASHBOARD

①  Clark Kent is experiencing substan/ally larger than normal losses [More]

②  Lois Lane is si^ng at the same game and has not played in over 15 minutes [More]

③  Bruce Wayne is experiencing substan/ally larger than normal winnings [More]

④ Homer and Marge Simpson are here on 20th wedding anniversary; comp a one-‐night suite stay [More]

⑤ Diane Prince is visi/ng in first /me in >6 months; comp a meal at Blazing Noodles [More]

⑥  Tony Stark is here with friends for his 40th birthday; comp $100 in chips [More]

Recommenda:ons

4

2

3

5

6

7

+ -‐

+ -‐

+ -‐

+ -‐

+ -‐

Help Hostesses manage player base with player-‐specific engagement

recommenda/ons

Provide addi/onal revenue and mone/za/on opportuni/es

Visually track “important” clients

1


Observations •  Students 4A1, 4A4 and 4B6 are

40% behind class norm [More] •  87% of students in Class Omega

are 20% or more behind equivalency levels [More]

•  Student 8B12’s math proficiency dropped from 20 to 33% behind personal target [More]

Recommendations •  Students 4A1, 4A4 and 4B6

should be moved to Group A2 [More]

•  Student 4B9 should be moved to Group A1 [More]

•  Schedule Chapter Fractions Basics review for Class Omega [More]

TEACHER WORKBENCH

Act

Act

Act

Grade: 7 Subject: ELA Level: Advanced

What if we added Teacher Recommendations: •  What are the usage patterns of the most “successful” students? •  What are the behavioral patterns that indicate a student is struggling? •  How can successful students benchmarks drive recommendations? •  How can I share teacher best practices with other teachers?


Engagement: Identify the optimal times, places and offerings with which to engage Creators in order to influence their product and project buying decisions

Communication: Leverage marketing and social media to influence and measure Creators and their “spheres of influence”, advocacy and net promoter scores

Product/Project: Identify, influence and predict buying and project planning decisions, and understand the effectiveness of marketing and merchandizing in reaching them at decision time

Financial: Identify the drivers of decisions and measuring the effectiveness of influencing those drivers

Identify/Targeting: Provide a predictive environment for identifying the Creator’s lifecycle

Loyalty: Create and monitor a customer loyalty index that can guide customer acquisition, growth and retention marketing campaigns

KEY STEPS ü Research and select strategic business initiative

(objectives, success metrics, timeframe)

ü Leverage group dynamics with Business and IT stakeholders to extract big data use cases

ü  Identify and secure key data sets (typically 5-6GB)

ü Perform data science work to determine which data sources provide the most value

ü Envisioning exercise to help convey the “realm of the possible” with big data analytics

ü Prioritize big data use cases, feasibility and ROI

ü Review, recommend, and plan a course of action for implementation

Lo Hi Feasibility

Bus

ines

s Va

lue

F E C

A

B D

Hi

Capture Creator Segment

F

E

C

A

B

D


Recommendation

Improve predictive models

Ease of data Acquisition

Cost of Acquisition

Data Management /

Preparation

Machine sensor logs / error codes

Digitalized Work Orders

Machine vibration data

Manufacturer Performance History

Omega machine maintenance data

Other providers maintenance data

Location-based data

Customer 214 Transaction Behavior - Airlines •  For Customer 214, there are average

183 flights per year (2011, 2012).

•  Base on the time series decomposing, there average 4 trips per every 5 work days.

•  On second and forth weeks in a month, Cardmember 214 has 3 days + weekend without airline traveling. Those days are Day 10-14 and Day 25-28 within that month.

•  Trips cover 35 out of 50 states in a year.

(Travel Patterns) What If… Deliver Real-time, Personal Offers Integrating Customers’ Shopping Propensities And Current Location?

XXXX.XX

XXXX.XX

XXXX.XX

Shop Hot Offer! >

What are the usage patterns of my most “valuable” card members?

What are the usage patterns that indicate someone may churn?

How do I gain insights into cardmember’s interests, passions, affiliations and associations?

How do I leverage personalized offers to increase cardmember engagement and usage?

What additional insights would my Merchants value?

Hi Lo

Hi

Implementation Feasibility

Bu

sin

ess V

alu

e

Churn: Leverage customer usage data to improve Churn Predictive Model Effectiveness Product Performance: Change network bandwidth based upon customer’s usage patterns Network Optimization: Optimize Network investments using customers apps usage patterns Standardization: Standardize tools, processes, analytic models and hiring profiles across teams

Recommendations: Create product recommendations based upon usage behaviors

Monetization: Leverage/package customer usage data to drive new monetization opportunities

A

B

C

D

E

F

F

E

C

A

B

D

Monetize Customer Usage Behaviors

KEY DELIVERABLES

1.  Big Data Business Opportunities

2.  Business Value and Feasibility Assessment

3.  Advanced Analytic Illustrations

4.  User Experience Mockups

5.  Business Opportunities Prioritization

1

2

3

4

5

1

2

3

4

5


WHAT IS A FEDERATION BUSINESS DATA LAKE

•  The complete way to bring data, applications, and analytics together

INGEST STORE ANALYZE SURFACE ACT

The Key Characteristics of our Business Data Lake •  Data is ingested, catalogued, inventoried, and controlled regardless of source or destination •  The raw data is never lost, it is stored in its original format for later analysis and evaluation •  It delivers analytics capability where needed; down to the point of data inception/ingest •  The data is presented when and where it is needed and not before (or after) •  The data lake is self referential and continually improves and tunes itself


PIVOTAL BIG DATA SUITE

EMC DATA LAKE SOLUTIONS

VMWARE VCLOUD SUITE

EMC II STORAGE DATA LAKE FOUNDATION: ISILON

VCE VBLOCK | XTREMIO | DATA DOMAIN

OPEN

ANALYTICS

TOOLBOX

DATA AND ANALYTICS CATALOG

ADVANCED ANALYTICS APPLICATIONS AT SCALE

DATA PROCESSING

GREENPLUM DATABASE HAWQ

SPRING XD PIVOTAL HD SPARK

REDIS

RABBITMQ

GEMFIRE

BDS ON PIVOTAL CLOUD FOUNDRY

HADOOP

PLAT

FORM

MAN

AG

ER

DATA

GO

VER

NO

R

DAT

A M

AN

AG

ER

ING

EST

MAN

AG

ER

AN

ALY

TICS

MAN

AG

ER


DRONES FOR AIRCRAFT INSPECTION + PREDICTIVE ANALYTICS FOR MAINTENANCE

32 EMC CONFIDENTIAL—INTERNAL USE ONLY EMC CONFIDENTIAL—INTERNAL USE ONLY

BRINGING IT TOGETHER DATA, ANALYTICS, & APPS

ANALYZE ANYTHING All of the data

More sophisticated analyses New combinations and correlations

STORE EVERYTHING Structured, unstructured, dark

Generated by the enterprise, imported from outside Historic & real-time

SPEED

ANALYTICS

APPS

DATA

BUILD THE RIGHT THING Deliver data consistently & in a standardized way

Get at the data quickly Build views and applications each user really needs


THANK YOU

Mark Sear, Big & Fast Data CTO EMC Global Services EMEA Mobile: +44.788.1511607 Email: [email protected] LinkedIn: https://uk.linkedin.com/in/mark-sear-64436018

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TAKING A QUANTUM LEAP IN DIGITAL TRANSFORMATION · Digitalized Work Orders Machine vibration data...

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