Converging your data landscape

© 2017 MapR Technologies 1

+

John Myers, Managing Research Director, EMA

Jack Norris, SVP Data and Applications, MapR

Converging your Data Landscape:

How Data-driven Approaches are

Changing Data Management


Featured Speakers

Jack Norris, SVP Data and Applications, MapR

Jack drives understanding and adoption of new applications enabled by data

convergence. With over 25 years of enterprise software marketing experience, he

has demonstrated success from defining new markets for small companies to

launching and establishing analytic, virtualization, and storage products, to

increasing sales of new products for large public companies.

John Myers, Managing Research Director, EMA

John has nearly 20 years of experience in areas related to business analytics and

business intelligence in professional services, sales consulting, product

management, industry analysis, and research. He helped organizations to solve

their analytics problems whether they related to operational platforms like

customer care, billing, or applied analytical applications, such as revenue

assurance or fraud management. John established thought leadership in

emerging data management paradigms such as big data (combination of multi-

structured and relational data sets) applications and NoSQL access data stores.


Logistics for Today’s Webinar

An archived version of the event recording will be

emailed to you following the event

• Log questions in the chat panel located on the lower

left-hand corner of your screen

• Questions will be addressed during the Q&A session

of the event

QUESTIONS

EVENT RECORDING

A link to the slides in slideshare will be emailed to you

following the event

SLIDES


• Data-driven cultures are disrupting the business landscape

• Drivers of change to the data management landscape

• Best practices

• Customer example in action

• Question and Answer

Agenda


Topic #1:

Data-driven cultures are disrupting the business landscape

IT & DATA MANAGEMENT RESEARCH,

INDUSTRY ANALYSIS & CONSULTING

Data-Driven Cultures and Strategies

Slide 6 © 2017 Enterprise Management Associates, Inc.



Data-driven Application Usage Scenarios


• Multiple usage scenarios

require multiple workloads and

multiple datasets

• Exploratory

• Operational

• Analytics

• Operational Analytics



Pace of Industry Disruption…



American Express Leveraging Machine Learning

• Big Data Platform Protects over $1T in spending each year

• Redefining Fraud Approach for the next decade

• Over 120 Separate Applications running on a single cluster

104 Million Card Members


Real-time Integrated Pub/Sub Streams to capture• Stocking changes

• Orders

• Cancellations from various systems

• Process events in real-time for latest stock availability

Critical consistent SLAs for stock availability checks • < 50 ms at any Large Scale

Schema-flexibility with Native JSON document data model

Rich queries via secondary indexes to search products on any dimension

Global Multi-Master replication for serving users across the world

System of record reliability

NO downtime

Lower TCO: Migrate from expensive RDBMS (DB2)

Transforming the Online Digital Shopping Experience

Modernize online digital shopping experiences for customers through robust acquisition, processing and

serving data to customers in real-time while reducing costs

LARGE EUROPEAN

RETAILER


Topic #2:

Drivers of Change in Data Management



Disjointed Components




Drivers for Implementation



Data Growth Puts Premium On Efficient Leverage

Source: McKinsey Global Institute: “The Age of Analytics”, Dec. 2016

THE AMOUNT OF DATA

IS PREDICTED TO

DOUBLE EVERY

THREE YEARS

DATA DIVERSITY

EmailsCall Detail

Records

Click

stream

CSV DocumentsData

PDFBilling Data Meta

Data

JSON Network

Data

Mobile

Data

XMLProduct

Catalog

Medical

RecordsText Files VideoText

Messages

Merchant

Listings

Sensor

Data

Server

Logs

Set Top

Box

Social

Media

Audio

4 Zettabytes

of Data

20111986

300 Exabytes

of Data

3 Exabytes

of Data

20192016

2 Zettabytes

of Data


90+% of Machine Learning

Success is Data Logistics,

Not Learning

Ted Dunning & Ellen Friedman

Model Management in the Real World

Machine Learning Logistics

https://mapr.com/ebook/machine-learning-logistics


Topic #3:

Best Practices for Data-driven Applications



Focus on the End Goals…Not the Implementation

Details…




Drivers for Data-driven Applications




Access to Data Needs to be Proactive and Centralized




EMA’s Hybrid Data Ecosystem:

Logical Framework for Next Generation Data


• Integrated logical architecture

• Dependent on access to and

the sharing of technical and

business metadata across

platforms

• Understands that business

stakeholders are the ones

driving data adoption





Variety

VolumeVelocity

Vicinity

Location Awareness

Visibility

Global Namespace

Veracity

Consistency and Integrity

Requirements for a Data Fabric


Topic #4:

Data Fabric in Action


High Frequency

Decisioning Use Cases

• Advanced Driver

Assistance Systems

(ADAS)

• Computer Aided Driving

• Vehicle Healthcare

• Fleet Management

By 2020, more than 250 million

vehicles will be connected globally—Gartner

Connected Car


Source

1

Source

2

Source

1000

Time to insight (24 hrs)

Manual, Scripts,

Req High Bandwidth Network

Source

1

Source

2

Source

1000

Time to insight (< min)

Automated Process1000s Test Cars

running in various

Conditions 24/7

Can do Pre Processing locally + at Core

(Custom App + Test Feedback Loop)

Car Test Analysis App

Before Edge After Edge

All Analysis only

done at Core

1-5TB/Day

Connected Car


MapR Converged Data Platform


JSON DB

(MapR-DB)

Graph DB

(Titan on

MapR-DB)

Search Engine

(Elastic-Search)

Transforming the Health Care Ecosystem

Electronic Medical Records

“The Stream is the

System of Record”–



Where to go From Here?

• Keep up or… get

left behind

• Moving at the

speed of

business

• Coordinated

environment


Questions and Answers


ENGAGE WITH US

Contact us at:

855-NOW-MAPR

Or

[email protected]

https://twitter.com/mapr

https://www.linkedin.com/company/mapr-

technologies

Follow us at:

https://twitter.com/mapr


Additional Resources

O'Reilly Book: "Machine Learning Logistics: Model Management in the Real World"

Get more information on the HDE and Data-driven applications at:

http://research.enterprisemanagement.com/hybridDataEcosystem.html

https://mapr.com/ebook/machine-learning-logistics/

http://research.enterprisemanagement.com/hybridDataEcosystem.html

Date post:	21-Jan-2018
Category:	Data & Analytics
Upload:	mapr-data-technologies
View:	109 times
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Converging your data landscape

Data & Analytics