Data-driven Approaches Changing Data Management

© 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An archived version of the event recording will be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
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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
Records
Text 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 theReal World
MachineLearning
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
Volume
Velocity
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
maprisr@mapr.com
https://twitter.com/mapr
https://www.linkedin.com/company/mapr-
technologies
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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

Data-driven Approaches Changing Data Management

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