This document discusses building a Hadoop capability within an organization. It recommends creating a sense of urgency around opportunities not currently possible, building a guiding coalition of potential users and partners, and formalizing use cases. It also suggests enlisting an excited volunteer team, starting small to remove barriers, generating short-term wins, sustaining acceleration through communication of successes, and instituting Hadoop as the default platform. Examples of proof of concepts include search, archival, real-time analytics, and log analysis. Challenges discussed include leveraging other teams, documenting work, building reusable solutions, using the full Hadoop stack, failing quickly and learning, and gaining developer skills.

1. A Big Data Journey
Growing a Hadoop-based Capability
Paul Boal – VP Delivery - Amitech Solutions
January 7, 2016
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2. Big Data Momentum
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Create a Sense of
Urgency:
What can’t we do
today? Are we
missing key
opportunities?
* The model here is from Kotter International – The 8-Step Process for Leading Change
• Experiencing pain from existing infrastructure
• Cost of growing and upgrading
• Addressing “real-time” demands

3. Big Data Momentum
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Build a Guiding
Coalition:
Who are potential
users and
partners?
• Build demo and do a road show – get people thinking
• Ask others who have done it to speak
• Remain open to potential partners; but…
• Be discerning about who you pick

4. Big Data Momentum
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Form Strategic
Vision and
Initiatives:
Formalize the use
cases and interest
you hear.
• Paint the big picture and show people what might be
• Lay out a potential growth plan based on use cases
• Highlight business value and immediate wins
• Make “step 1” very easy

5. Big Data Momentum
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Enlist a
Volunteer Army:
Identify customer
and IT teams who
are excited by
change.
• Find a customer who is excited by doing things in new ways
• Leverage IT relationships to move new technology smoothly
• Find IT teams and individuals are excited about something new

6. Big Data Momentum
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Enable Action
by Removing
Barriers:
Start small and
align growth to
business needs.
• Start as simply and cheaply as you can for the first POC or use case
• Leverage non-IT dollars when possible
• Align investment to specific business needs
• Build incrementally

7. Big Data Momentum
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Generate Short-
Term Wins:
Execute quickly
and repeatedly
• Leverage an Agile approach
• Deliver small but valuable features quickly and frequently
• Focus on what users need, what you want them to need

8. Big Data Momentum
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Sustain
Acceleration:
Share success and
keep selling
internally
• Develop a communication plan that includes sharing the quick wins
to a broad mid-level and executive leadership audience
• Don’t drop out of sales and communication mode once the first
implementation starts… keep shelling future projects

9. Big Data Momentum
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Institute Change:
Let Hadoop be
your default
platform.
• Switch from “we’ll use Hadoop if we have to” to “we’ll use Hadoop
unless we can’t do it there.”
• Take on some small and simple projects. They can be quick wins,
and they’re good opportunities for new developers to learn, too.

10. Example Solutions
• Chart Search (POC) – search has “wow factor”
• System Archival – Simple process to archive Omnicell data and expose for
reporting with SAP Business Objects
• Real-Time Clinical Analytics – Documentation Improvement and the big
vision of what Hadoop could do based on Epic data
• Lab Text Search – Easier way to dig through Epic lab notes using Hive, Solr,
some custom code and various integration pieces, and reporting via SAP
Business Objects
• Epic Access Log – Got those billions of rows of Epic access log data out of
our reporting database, compressed, and easier to report on
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11. Challenges and Lessons
• Leverage other departments and
projects, including their funding
• Keep sharing what Hadoop can do, and
write down everything you do
• Build solutions and tools that are
reusable and scalable
• Leverage the entire Hadoop stack of
related tools
• Try to fail as quickly as possible, and
then try something else
• Build an approach / methodology that
scales (e.g. Data Lake)
• Don’t underestimate the learning
curve
• Leverage polyglot developers
• Spend extra time with traditional data
warehouse and ETL developers
• Pay attention to versions and learn
how to upgrade quickly
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