Big Data expert and Infochimps Product Manager Tim Gasper shares insights and explains how to effectively execute your Big Data project while avoiding the most common pitfalls.

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top strategies
for successful big data projects
Tim Gasper
Product Manager
Infochimps, Inc.
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#1 big data platform for the cloud
some of our customers our partners
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big data
data science
big data
application
development
big data
business
intelligence
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agenda
• why you care about big data
• too many big data projects fail
• 7 strategies for success
• q&a
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poll
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BIG DATA
• volume • scalable
• velocity • intelligent
• variety • agnostic
• variability • holistic
• veracity
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use cases
oldies but goodies new school
• website and systems • brand and sentiment analysis
log analysis • targeted marketing and
• large scale transaction personalization
analysis • customer insights / behavior
• trade performance • big data business intelligence
analytics • predictive modeling
• risk analysis and fraud • intelligent systems
detection • large-scale data mining and
• geo/spatial analysis cleansing
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44% of big data
projects are cancelled
versus 25% for IT projects in general
and many more fail to
achieve project objectives
Infochimps/SSWUG Enterprise Big Data Survey 2012
Dynamic Markets Enterprise IT Survey 2008
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poll
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why do projects fail?
• business
• inaccurate scope
• not enough time / deadlines busted
• non-cooperation between departments
• having the right talent / lack of expertise
• technical
• technical or roll-out roadblocks
• gathering data from different sources
• finding and understanding tools, platforms,
technologies
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how do I prevent failure?
mapping common challenges
to 7 simple strategies for
big data project success
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define the problem
• what are your business goals
• identify the greatest
opportunities and challenges
• eat a frog
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think BIG and be SPECIFIC
• what big goal are you tackling
• don’t waste your time
• is it specific enough
• is there a project
• you can’t eat an elephant in
one bite
• get to something you can
measure
• set your priorities
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identifying projects
• new business initiatives
• enhancements to current
operations, systems, and
processes
• replacements for legacy
operations, systems and
processes
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map priorities to technologies
• business value  technology
• think high-level because the tech
is changing rapidly
• impala vs hive vs hadapt vs
vertica vs …
• batch vs real-time
• proprietary vs open
• DIY vs IaaS vs PaaS
vs SaaS
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poll
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hit the ground running
• avoid analysis paralysis
• take a phased approach
• test and iterate as you go
• you’ve done your
homework, you have
your compass
• know what to do with the
data
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manage scope creep
• manage requirements and
expectations
• manage complexity
• manage people creep
• manage technology creep
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embrace the cross-organizational
nature of big data
• who are your initial users
• who else can benefit
• other departments
• other use cases
• other data sources,
systems, and tools
• what are the next steps
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drive business value
• don’t be driven by technology, be
driven by business needs
• what is the point of big data
• NOT a cost center
• time to business value
• speed to insight
• data driven decisions
• go FAST and do AWESOME
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Let’s discuss your big data projects and ideas.
infochimps.com/free-big-data-consultation
sales@infochimps.com
follow us: @infochimps
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