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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 • volume
• velocity
• variety
• variability
• veracity
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DATA • scalable
• intelligent
• agnostic
• holistic
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oldies but goodies • website and systems
log analysis
• large scale transaction
analysis
• trade performance
analytics
• risk analysis and fraud
detection
• geo/spatial analysis
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new school • brand and sentiment analysis
• targeted marketing and
personalization
• customer insights / behavior
• big data business intelligence
• predictive modeling
• intelligent systems
• large-scale data mining and
cleansing
use cases
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44% of big data
projects are cancelled
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Infochimps/SSWUG Enterprise Big Data Survey 2012
Dynamic Markets Enterprise IT Survey 2008
and many more fail to
achieve project objectives
versus 25% for IT projects in general
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poll
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why do projects fail?
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• 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?
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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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