Big Data has become critical to the enterprise because of the massive amount of untapped data sources, and the potential to gain new insights that were previously not possible. So, how to get started with Big Data and Hadoop becomes a question more pertinent than ever before.
Listen to leading analyst at Ovum, Tony Baer, as he discusses answers to the key questions around how to:
Approach Big Data and associated business challenges
-- Identify what types of new insights can be revealed by Big Data
-- Staff for this undertaking and implement the technology necessary to be successful
-- Take the first steps toward getting started with Big Data on Hadoop
2. How to Get Started on Hadoop
for Business Managers"
Š 2014 Datameer, Inc. All rights reserved.
3. Audio"
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4. Tony Baer @Ovum
Principal Analyst
Tony Baer leads Ovumâs 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"
5. 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 Datameerâs 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"
6. www.ovum.com
How to get started on Hadoop
for Business Managers"
Tony Baer, Principal Analyst
tony.baer@ovum.com
Š Copyright Ovum 2014. All rights reserved.
7. Agenda"
Â§ď§ Why Big Data & Hadoop?
Â§ď§ Making the business case
Â§ď§ When to use Hadoop
Â§ď§ How to make it happen?
Â§ď§ Whatâs the End Game
Š Copyright Ovum 2014. All rights reserved. 7
8. Making the business case"
Â§ď§ Address documented
business challenges
Â§ď§ Choose high-impact
problem & solution that
delivers actionable
results
Â§ď§ Determine whether the
solution absolutely
requires Big Data
This is not a data science exercise!
Š Copyright Ovum 2014. All rights reserved. 8
9. 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
Š Copyright Ovum 2014. All rights reserved. 9
10. Use case â
general themes"
New perspectives
to addressing
existing problems
New data
generates new
revenue streams
Š Copyright Ovum 2014. All rights reserved. 10
11. Agenda"
Â§ď§ Why Big Data & Hadoop?
Â§ď§ How to make it happen?
Â§ď§ Making Big Data & Hadoop first class citizens
Â§ď§ Getting the right people
Â§ď§ Walk before you run
Â§ď§ Whatâs the end game?
Š Copyright Ovum 2014. All rights reserved. 11
12. Big data must become a 1st class citizen
in the enterprise"
No separate Big
Data silos!
âBig Data
cannot exist on
its own islandâ
⢠IT: Map to existing staff & skills
⢠Data Center: Map to existing
policies & rules
⢠The Business: Map to existing
business challenges
Š Copyright Ovum 2014. All rights reserved. 12
13. 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
Š Copyright Ovum 2014. All rights reserved. 13
14. Do you really need a data scientist?"
Â§ď§ Creative, investigative mind
Â§ď§ Statistical programming skills
Â§ď§ Domain/industry awareness
Â§ď§ A ânoseâ for data sets
Â§ď§ Some database skills/awareness
Â§ď§ Ability to communicate &
evangelize
Applications & tools may embed
data science!
Look for a team, not a rock star!
Š Copyright Ovum 2014. All rights reserved. 14
15. Getting there with the army you have"
Â§ď§ Hadoop platform training is critical
Â§ď§ Big Data analytics training is
critical
Â§ď§ Greater variety of data
Â§ď§ Different analytics methods (beyond
traditional SQL)
Â§ď§ Cloud
Â§ď§ Reduces technology skills
requirements, depending on type of
cloud service
Â§ď§ Different architecture than on premises
cluster deployments
Â§ď§ Requires retraining if cloud used for
jumpstart to on-premises
Š Copyright Ovum 2014. All rights reserved. 15
16. 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
Š Copyright Ovum 2014. All rights reserved. 16
17. Agenda"
Â§ď§ Why Big Data & Hadoop?
Â§ď§ How to make it happen?
Â§ď§ Whatâs the end game?
Š Copyright Ovum 2014. All rights reserved. 17
18. Set realistic goals"
⢠âHardâ ROI numbers from
⢠DW optimization
⢠Operational efficiency
⢠âSoft numbersâ 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
Š Copyright Ovum 2014. All rights reserved. 18
19. 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
Š Copyright Ovum 2014. All rights reserved. 19
20. 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
⢠Donât expect to find a âdata scientist.â
⢠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 âsoft numbersâ
⢠Identify business benefits that are directly attributable to using Big Data
analytics
Š Copyright Ovum 2014. All rights reserved. 20
21. www.ovum.com
Thank you"
Tony Baer
Ovum
(646) 546-5330 @TonyBaer tony.baer@ovum.com
Š Copyright Ovum 2014. All rights reserved.
25. 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?
But as we said up front, appealing to the enterprise changes the script for Hadoop â
When Hadoop was invented by Yahoo, Facebook, and others, it was for solving highly specialized problems. And as a new technology that they open sourced to the Apache Foundation, the practitioners were a small, select, highly elite group. Hadoop clusters were their own specialized environments set apart from the operational systems, and run by their own dedicated teams.
Hadoop emerged as an island of its own. That model will not be sustainable in the enterprise!
Thatâs why we at Ovum firmly believe that for Big Data â and Hadoop â to gain traction with the enterprise, it must get off the island. It must become a first class citizen in the enterprise.
Updated Nov. 1 (Non-NDA)
This use case study is a major credit card company (AMEX) that spend millions of dollars in both digital and non digital advertisement. This credit card company used Datameer to correlate purchase history, profile information and behavior of their customers on social media sites. For example, they collected customer profile data on their platinum customers. Then theyâd correlate this data with transaction history and things the customers âlikedâ on Facebook.
From those findings the company targeted their advertisement on TV channels their high value customers like to watch (food network) and offered promotions at an organic foods store, where their customers frequently shop at. As a result of using Datameer, they were able to decrease their customer acquisition costs by 30% . For a major credit card company this represents millions of dollars in annual savings.
Additional detail:
This financial services company gathers data from Facebook and generate profiles of what people likes. They gather and correlate this data to check for patterns. With these patterns, they can set their marketing strategy to target people of certain profiles with certain advertisements. As a result of using Datameer, they were able to decrease their customer acquisition costs by 30% and the time it took to create these reports down to 1 day from 16 weeks.
The average customer acquisition cost is about $60 per customer for credit cards. For first 3 months of 2013, new cards increased 3% to 42.5M for this company. So that is 1.275M new customers. If we reduced customer acquisition costs by 30%, that is $18 per customer, or $23M.
http://about.americanexpress.com/news/pr/2012/earnings-preannouncement.aspx
http://www.nbcnews.com/id/50496724/ns/business-small_business/t/how-much-did-new-customer-cost-you/#.UYPBwSuAe24
http://www.rtefs.com/images/Forrestor_20Financial_20Services_20Firms_20Open_20Up_20About_20Customer_20Acquisition_20Costs.pdf
http://seekingalpha.com/article/1351731-american-express-gains-on-consumer-spending-and-international-growth
Vivint
â Serves 800,000+ homes, Vivintâs touchscreen panel (their hub) creates a streamlined network that connects all of the homeâs smart systems (security, HVAC, lighting, small appliances, video, etc).
â Uses Datameer to:
â integrate and analyze not just row data but also streaming data, which is a key component to their smart home analytics solution.
â to parse, join and sessionize various complex data streams to determine occupancy and vacancy patterns in an effort to reduce the number of false alarms and improve the overall efficiency of their devices.
â sessionize data based on events and looking at log windows before and after a selected event.
âEnsures a better customer experience by improving the understanding of the customers behavioral patters.
â Time savings due to rapid implementation of the analytical solution for sensor data
â Cost savings due to minimal investment in skilled Hadoop resources
â Can compare to other to others homes and learn behavior
â Based on outlier detection - e.g. unusual movements within house and detect thieves, improve products from this insights)
NetApp
This enterprise hardware company was generating and collecting data that was doubling every 15 months. In addition to the rapidly growing data volumes, there were hundreds of different semi-structured and unstructured log formats. Before Datameer, analysts were forced to write ad hoc Perl code to parse a subset of the log files and store data locally. By using Datameer, the company was able to derive valuable insights that helped virtually every group --   Support, Development, Marketing, Services. They also use their data-driven intelligence to offer âPremium Supportâ as a new revenue service offering.
For example, Support was able to send out a replacement part before the component actually failed. Sales was able to look at usage patters to improve forecasting and renewal negotiations.Â
Additional detail:
http://documentation.datameer.com/documentation/display/SALES/Device+Analytics
QUESTIONS?
Well, that ends our webcast today. Thank you to Tony and Azita for talking with us today as we wrap up 2014.
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A reminder that we have other webcast recordings and resources on our website at http://www.datameer.com/learn/index.html
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We encourage you to visit our website to learn more, request a trial download at datameer.com or follow us on twitter @datameer.
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This concludes the audio portion of our webcast today. Â Thank you and Happy Holidays. See you in 2015.
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