Modernizing Architecture for a Complete Data Strategy

1© Cloudera, Inc. All rights reserved.
Modernizing Architecture
for a Complete Data
Strategy
August 2nd, 2016

Today’s Speakers
Tony Baer
Ovum – Principal Analyst
Information Management
@tonybaer
Clarke Patterson
Sr. Director – Product &
Solutions Marketing
@clarkepatterson

Agenda
• Our relationship with data is changing
• Success getting harder to ignore
• How to build a Modern Data Strategy

Let’s go to the polls!
• How would you rate your experience with Hadoop today?
a. We’re thinking about it, haven’t started yet
b. We just got going, not yet in production
c. We’re in production for 1 or 2 projects within 1 or 2 departments
d. We are advanced, running many use cases across the organization in
production

“Something interesting is happening”
The world’s largest
taxi company owns
ZERO vehicles.
The world’s largest
accommodation provider
owns ZERO real estate.
The world’s most
popular media owner
creates ZERO content.
The world’s leading
music platform owns
no music.

Our relationship with data
is changing.

“80% of CEOs cite data mining
and analytics as strategically
important.”
-2015 PWC CEO Survey

Big data has made it to the boardroom
DRIVE CUSTOMER
INSIGHTS
IMPROVE PRODUCT &
SERVICES EFFICIENCY LOWER BUSINESS RISK
MODERNIZE ARCHITECTURE

• What key business initiative are your big data project(s) aligned to?
a. Driving customer insight (eg. Single view of customer, next best offer)
b. Improving product & services efficiency (eg. Better product quality, faster
product innovation)
c. Lowering business risks (eg. Cybersecurity, fraud)
d. All of the above
e. None of the above/I don’t know

Ovum | TMT intelligence | informa10 Copyright © Informa PLC
Why Big Data?
 Full fidelity data access
 Start with data in its native format, without losing context to fit
rigid database schema
 Make the window larger
 Greater breadth & depth provides the big picture
 Analytic flexibility
 Work with the data format and analytic path of choice
 Deliver tangible results
 Improved Business Forecasting based on all of the data, not just a
sampling
 Better Customer 360 views, through all of the channels where your
customers are active – not just your CRM or call center system
 Improved security/threat detection via advanced techniques such as
machine learning for ID’ing signals from data sets too large for humans to
digest unaided

Perception:
Big Data is only for elite practitioners

Success
getting harder
to ignore

Many Businesses Using AI Without Realizing It
Information Week 7/21/2016
88% of those who denied using AI solutions reported that they rely on services or products powered by AI techniques
Finding ROI in a swirl of data analytics
Computerworld 9/2/2015
Integrating health records with geospatial data helps clinicians target
safety campaigns. "What GIS mapping allowed us to do is identify the
hotspots where injuries were occurring and map them out."
Genomic research and precision medicine are on the leading edge of
cancer research and treatment, but the huge size of the data sets and
the complexity of sharing those data sets among researchers thwarts
progress. Here's one project that is aiming to remove those obstacles
Can Big Data help cure cancer?
Information Week 7/19/2016

Changing the manufacturing business
 From manufacturing to services.
Ford transforming itself to “transportation
services company” that may change the car buyer
relationship
GE bets future on the industrial internet with
predictive analytics & optimization software
helping customers increase yields
From buying a product …
To buying the service that the product delivers

Optimizing healthcare delivery
 Large regional healthcare system uses big data analytics
to segment 150,000-patient population
 Building data lake comprising EHRs, clinical data, third
party syndicated data leveraging Hadoop’s schema-on-
read flexibility
 Schema on read speeds data exploration, open new
analytics paths
 Clinical segmentation initiative based on age, gender,
disease, care delivery, payer type, geography, and
healthcare utilization patterns to drive care management
 With precise segmentation, clinical resources are focused
on highest risk patients
Know Your customer initiative to improve patient care

Retailer tunes into social media to gain customer visibility
 Listening to social media to understand customer perceptions,
yield actionable insights
 Can customers find the products they need?
 Are promotions effective?
 What store policies are effective?
 Challenge: cutting through “the noise” –
 80% of tweets not relevant to the retailer
 Results:
 Reduced social media noise to 20%
 Improved tracing of trending topics
 Improved decision productivity and ability to listen in real time

How to isolate signal from the noise?
Here’s what 29 million tweets can
teach us about Brexit
Washington Post 7/20/2016
Technology is
monitoring the
Urban Landscape
NY Times 7/20/2016
In uncertain times, CEOs Lose Faith in
Forecasts
With Brexit to the rise of Donald Trump, business leaders have
made have made them wary of expert predictions
Wall Street Journal 7/12/2016
Trump’s Big Data Gamble
While he tweets to the masses, Hillary will be
precisely targeting persuadable voters.
Wall Street Journal 7/25/2016

“Big Data Must become a First
Class Citizen in the enterprise”
“Big Data cannot exist
on its own island”
Where we started…

The Big Data conversation has changed
 Big Data & Hadoop Adoption:
Moving from Early Adopters to Early Majority
 Use cases
Familiar business problems – Moving from clickstream
optimization to Customer 360, Fraud Prevention,
Operational Efficiency
 Hadoop maturing
Becoming more diverse, mature, well-rounded & joining the
data platform ecosystem
From batch MapReduce to SQL, streaming, machine
learning, graph analytics

The Big Data conversation has changed
 Big Data & Hadoop Adoption:
Moving from Early Adopters to Early Majority
 Use cases
Familiar business problems – Moving from clickstream
optimization to Customer 360, Fraud Prevention,
Operational Efficiency
 Hadoop maturing
Becoming more diverse, mature, well-rounded & joining the
data platform ecosystem
From batch MapReduce to SQL, streaming, machine
learning, graph analytics
Build a
Modern Data
Strategy

How to build a Modern Data Strategy?

What is a Modern Data
Strategy?
How to get there?

People, Process &
Technology

People
Put the business first
It’s a team sport
Manage:
 Plan for making the data accessible –
and secure
 Plan for making analytics pervasive
Explore:
 Iterate, share & collaborate
Apply
 Make discoveries & insights
operational
IT
Ops, InfoSec, DBA, ETL
Data Team
BI, Analysts, Data Scientists
“Business” Users
Anyone
Manage
Explore
Apply

Process
Be agile – implementation will
be a learning experience
 Form hypotheses, but go where the
data leads
Plan for governance
 Data & analytics will be shared
resources
Governance is an iterative
process
 ID the starting point that is right for
your organization
Collect, Create, Manage
unlimited data
Explore, Analyze
data in many ways
Operationalize
insights to drive action
Gather, Imagine
Discover, Query
Make actionable

Technology
Keep your options open
 Add new data sources, more complex analytics,
real-time ingest & consume over time
 Fail fast… is good!
Preserve your tracks…. Keep your metadata!
 Data lineage is critical!
Start small, build on success
 Allow the learning process to go viral
 Collaborate, communicate success… and failure
 Land & expand
Collect, Create,
Manage
unlimited data
Explore, Analyze
data in many ways
Operationalize
insights to drive action

Start small, build on success
Group Multi-department Enterprise
Log analytics
Sentiment Analysis
DW offload
Data Lake
Exploratory Analytics
Line of business analytic applications
Operational analytics
A Data Lake is the
culmination of your
journey, not the start

Evolve the use cases
Land & Expand
Start with high-
impact, line-of-
business
applications….
Group Multi-department Enterprise
Customer 360
Intrusion Detection
Ad optimization
Cross-
departmental
applications
Customer segmentation
Cybersecurity
Operations Management

Modern Data Strategy –
Where to go from here?
Assess your business needs
Where are the points of pain?
Bottom line or top line?
Where are the missing signals?
Do you have the right data?
Assess where you are
Skills readiness – data, technology, ability to
communicate
Infrastructure readiness
Who are the change agents?
 Where to start?
ID “low hanging fruit” where you can get tangible, high
impact benefits
Group
Multi-department
Enterprise

• What are you most concerned about when it comes to your readiness for big data
and Hadoop?
a. People
b. Process
c. Technology
d. All of the above
e. None of the above

Three core enterprise data applications
Operational Efficiency New Business Value
Programmatic data
processing and data pipeline
creation.
Data
Engineering
Explore, analyze, and
understand all your data.
Analytic
Database
Data-driven applications
to deliver real-time insights.
Operational
Database

Data
Engineering
Build the pipelines to feed your
decision making and harness
real-time and interactive data
with ease.
Get access to data faster to
build and refine analytic models
in real-time. Implement
complex machine learning
functionality.
Easy cross segment
collaboration with active
auditing and lineage tracking
throughout the platform.
Programmatic Pipeline Creation
Exploratory Data Science
Rich Developer Experience

RelayHealth builds
predictive models for
when payments to
healthcare providers
will be received,
improving their
cash flow.

Analytic
Database
Web, social, and external data
continues to be collected but
out of reach for analysis. Tap
into the value of all data
Democratization of Data
New requirements require
analytics on streaming, rapidly
changing, and real-time data
Real-time Analysis
Optimize your data warehouse
infrastructure, enabling more
data to more users than ever
before, across environments
Modernize Data Warehouse

Re-Platformed 2,000 Systems
onto Cloudera for Centralized
View
Challenge:
• Improve ETL processing, extend data
retention, and enable new use cases
Solution:
• Centralized, multi-tenant environment
for unified customer view across
business units
• Increased data velocity for ETL by 15x
(process 5x the data in 1/3 the time)
• 200-250% ROI & take on new projects
quicker, at a lower cost

Operational
Database
Self Service BI and analytics
capabilities delivered directly
to end users.
Identifying meaningful events
based on multiple data streams
and taking action.
Determine credit worthiness
and optimize bad debt
collection activities based on
data.
Embedded Applications
Complex Event Processing
Model Scoring

Western Union reveals
correlations between
exchange rates and
transaction volumes in
specific communities.

Cloud growth fueling Hadoop use cases
Requirements to move from POCs
to enterprise use cases
- Security
- Portability
- Flexibility
- Ecosystem support

What’s driving Hadoop to the cloud?
Data Gravity Flexible Resourcing Enterprise Acceptance

Common workloads in the cloud
Only pay for what you need,
when you need it
▪ Transient clusters
▪ Elastic workload
▪ Object storage centric
▪ Cloud-native deployment
ETL/Modeling
(Data Engineering)
App Delivery
(Operational
Database)
Reduce Operating Costs New Insights, New Revenue Run Without Risk
BI/Analytics
(Analytic Database)
Explore and analyze all data,
wherever it lives
▪ Long-running clusters
▪ Sized to demand
▪ HDFS or object storage
▪ Lift-and-shift deployment
Enterprise-grade to protect
your business, no matter what
▪ Fixed clusters
▪ Periodic sync
▪ All HDFS storage
▪ Lift-and-shift deployment

• To what degree are you using Hadoop in the cloud?
a. Not using nor any plans at this time
b. Planning on it in the next 6 months
c. We’re already running some test projects in the cloud
d. Some of our workloads are in the cloud
e. All of our workloads are in the cloud

Key Takeaways
• Big Data analytics is the new table stakes… no longer limited to early
adopters
• Build a Modern Data Strategy that focuses on people & process… not only
technology!
• Focus on tangible business cases… you’re not conducting data science
experiments!
• Assemble the right team
• Stay agile… Rome was not built in a day
• Plan for making data accessible, governable, and keep your metadata!
• Plan for making analytics pervasive

Thank you!

Modernizing Architecture for a Complete Data Strategy

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