playing nice in the product playground #stratahadoop
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1 #AJLPlayNice
Playing Nice in the Product Playground
data scientists, engineers, and product managers working together to create innovative data products
Jonathan Goldman @jrgoldman
Anu Tewary @anutewary
Lucian Lita @datariver
Intuit Data Engineering & Analytics
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product vision business impact success measures
effective architecture scalability & robustness metrics & monitoring
not sure what this product does, but look at the 2% lift I can get from this model... ooh, ooh, a Dirichlet prior is what this needs!! is this good for an ICML or KDD paper?
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product vision business impact success measures
effective architecture scalability & robustness metrics & monitoring
not sure what this product does, but look at the 2% lift I can get from this model... ooh, ooh, a Dirichlet prior is what this needs!! is this good for an ICML or KDD paper?
Data scientists navel gazing in a corner?!
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product vision business impact success measures
rapid experimentation simple models first right metrics
let’s write a new streaming framework for the weekly dashboard! we’re not meeting our SLAs, let’s write a faster json parser! let’s write an optimized distributed graph database for our data scientist.
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product vision business impact success measures
rapid experimentation simple models first right metrics
let’s write a new streaming framework for the weekly dashboard! let’s write a faster json parser in Clojure! silver bullet: graph database, fp, lambda arch
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Engineers reinventing the tech wheel?!
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rapid experimentation simple models first right metrics
forget A/B testing, my gut tells me this is the way to go... revenue impact? Who cares! Build it anyway! no time to instrument! Let’s go to market and we’ll do that later - I’m sure that the numbers will look good!
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effective architecture scalability & robustness metrics & monitoring
8 #AJLPlayNice
rapid experimentation simple models first right metrics
forget A/B testing, my gut tells me this is the way to go... revenue impact? Who cares! Build it anyway! no time to instrument! Let’s go to market and we’ll do that later - I’m sure that the numbers will look good!
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effective architecture scalability & robustness metrics & monitoring
Product in a bubble?!
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find the right mix
minimum
prod
ds
eng
good
good
good
target
great
good
good
prod
ds
eng great
good
good
prod
ds
enggreat
good
great
prod
ds
eng
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form pods around product
personalization & reco pod
real time data capture &
stream proc. pod
business search pod
real time commerce graph pod
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Three Steps to Risa**
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1 awesome team (pods)
solve a big problem (pods)
get out of the way (pods)
** Risa is to Nirvana as Spark is to Hadoop
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Example 1: Multinational banking and financial services company
Took a “technology first” approach: wanted to build a hadoop cluster, because they had heard they should No product vision, but tremendous (!) possibilities Not connected closely with business needs No data science
build an awesome team solve a big problem engage
prod
ds
enggood
tiny tiny
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Example 2: Large media company
Excellent engineering team Good product team, but not data driven Good metrics and beginning data science. Did not iterate quickly; data and product were too decoupled
build an awesome team solve a big problem engage
?
prod
ds
engamazing
tiny good
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Example 3: Large advertising firm
Data-driven product team, but limited vision Engineering team not product focused. Could not iterate quickly Non-existent data science
build an awesome team solve a big problem engage
good
tiny
ok
prodds
eng
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Example 4: Attempt at Introspection
An awesome team with data, product and engineering working together Solving hard problems – for individuals and small businesses Good metrics in place, but not there yet – more work to be 100% eyes on, hands off.
build an awesome team solve a big problem engage