Getting Started with Big Data for Business Managers

How to Get Started on Hadoop
for Business Managers"
© 2014 Datameer, Inc. All rights reserved.

Audio"
▪ Audio will be streamed over
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host a chat message through
the chat window

Tony Baer @Ovum
Principal Analyst
Tony Baer leads Ovum’s Big Data research area.
Over his 25 years in the industry, he has studied
issues of data integration, software and data
architecture, middleware, and application
development. Having tracked the emergence of BI
and data warehousing back in the 1990s, Baer
sees similar parallels emerging in the world of Big
Data today. His coverage focuses on how Big Data
must become a first-class citizen in the data center,
IT organization, and the business.
@TonyBaer
About Our Speaker"

Azita Martin @datameer
CMO
Azita Martin is Chief Marketing Officer at Datameer
with extensive marketing leadership experience at
high-growth start-ups and category-creating public
companies like Salesforce and Siebel.
Azita has global responsibility for scaling all
aspects of Datameer’s product and corporate
marketing, including defining go-to-market strategy,
driving thought leadership, and increasing brand
awareness and customer acquisition.
Prior to Datameer, Azita built and led marketing
teams for both fast-growing start-ups and major
public companies, including Get Satisfaction, Moxie
Software, LiveOps, Salesforce, Siebel and SGI.
#datameer @datameer
About Our Speaker"

www.ovum.com
How to get started on Hadoop
for Business Managers"
Tony Baer, Principal Analyst
tony.baer@ovum.com
© Copyright Ovum 2014. All rights reserved.

Agenda"
§ Why Big Data & Hadoop?
§ Making the business case
§ When to use Hadoop
§ How to make it happen?
§ What’s the End Game
© Copyright Ovum 2014. All rights reserved. 7

Making the business case"
§ Address documented
business challenges
§ Choose high-impact
problem & solution that
delivers actionable
results
§ Determine whether the
solution absolutely
requires Big Data
This is not a data science exercise!

When to use Hadoop"
New data
sources
Schema on
read
New analytic
approaches
beyond SQL
Inexpensive
compute
cycles
• Structured
data
• Text
• Social
networks
• Mobile data
• Machine data
• Rich media
• Let the data
drive you to
the problem
or insight
• Path analytics
• Cluster
analytics
• Graph
analytics
• Streaming
analytics
• Move
compute
loads from
DW to
Hadoop

Use case –
general themes"
New perspectives
to addressing
existing problems
New data
generates new
revenue streams

Agenda"
§ Making Big Data & Hadoop first class citizens
§ Getting the right people
§ Walk before you run
§ What’s the end game?

Big data must become a 1st class citizen
in the enterprise"
No separate Big
Data silos!
“Big Data
cannot exist on
its own island”
• IT: Map to existing staff & skills
• Data Center: Map to existing
policies & rules
• The Business: Map to existing
business challenges

Get the right people"
Technical Analytics
team
Business
Platform Specialists/
Cluster architect
Java/Python
Developers
Data Architect
DBA
Domain/subject matter
experts
Statistical experts
Mgmt. champion Evangelist
Business
owner/sponsor
(Supplemented by
applications or tools)
Data steward/
Data curator
Some will play dual roles

Do you really need a data scientist?"
§ Creative, investigative mind
§ Statistical programming skills
§ Domain/industry awareness
§ A “nose” for data sets
§ Some database skills/awareness
§ Ability to communicate &
evangelize
Applications & tools may embed
data science!
Look for a team, not a rock star!

Getting there with the army you have"
§ Hadoop platform training is critical
§ Big Data analytics training is
critical
§ Greater variety of data
§ Different analytics methods (beyond
traditional SQL)
§ Cloud
§ Reduces technology skills
requirements, depending on type of
cloud service
§ Different architecture than on premises
cluster deployments
§ Requires retraining if cloud used for
jumpstart to on-premises

Walk before you run"
Low Road
High Road
Higher Road
Big Data on Hadoop use cases
Clickstream/log analytics
DW optimization
Customer optimization
Risk Management
Anti-fraud
Operational Efficiency
Create new business
services
Business
transformation

Agenda"
§ What’s the end game?

Set realistic goals"
• “Hard” ROI numbers from
• DW optimization
• Operational efficiency
• “Soft numbers” from
• Benefits that are directly
attributable to using Big
Data analytics
• New business opportunities
from Big Data
• New capabilities for sensing
& responding
Challenges are similar to any analytics project

Embrace & Extend"
IT organization
Data Center
Enterprise
Embrace Extend
Existing SQL, Java, other
language skills
Mgmt for bigger, more
variable data sets & use
new analytic methods
beyond SQL
Existing data stewardship,
resource mgmt, security,
perf mgmt practices
Practices to support
different workload
characteristics & active
archiving
Existing competitive
problems
Problem solving by
using new data types &
analytic methods to
boost understanding
How to make Hadoop & Big Data 1st class citizens

Summary: The elements for getting
started with Hadoop"
1. Problem
2. People
3. Training
4. Technol.
5. Document
results
• Start simple
• Choose high-impact problem & solution that delivers actionable
results
• Determine whether the solution absolutely requires Big Data
• Extend DW teams with greater roles for developers, statistical analysts
• Don’t expect to find a “data scientist.”
• Management champion is critical
• Get data center practitioners up to speed with Hadoop platform
• Develop understanding of how to work with new data types +
analytics methods beyond traditional SQL
• Start small.
• If cloud used for jumpstart, architectural migration will be required for
moving on premises
• Unless used for operational efficiency or DW optimization, benefits will
rely on “soft numbers”
• Identify business benefits that are directly attributable to using Big Data
analytics

Combining More Data for New Insights"
Social Media
Mobile
Ads
Web Logs
CRM
Product Logs
Transaction
Call Center
Are keywords related to
customer segments?
Which campaign
combinations accelerate
conversion?
Which product
features drive
adoption?
Which features do users
struggle with?
What behavior
signals churn?
Where do hack attempts
originate?
How do we determine
which cell towers to
upgrade?
Can we predict production
failure?

Decrease Customer Acquisition Cost
30% Lower Customer
Acquisition Costs

Internet of Things"
Connected
Home
Energy
Consumption
Data
Reduced False Alarms
User Behavior
Improved Customer Experience

New Data-Driven Service
Server Logs
Product Catalog
CRM
Predictive Maintenance
New Revenue Stream

For the webinar:
http://bit.ly/1DcEdeu"

Getting Started with Big Data for Business Managers

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