playing nice in the product playground

2015

Playing Nice in the Product Playground

Building Data Teams:data scientists, engineers, and product managers

working together to create innovative data products

Anu TewaryOctober 15, 2015

#GHC15

2015 Anu Tewary@anutewary

2015

Pop quiz …

2015

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?

[ 1 ]

2015

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?!

[ 1 ]

2015

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.

[ 2 ]

2015

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

[ 2 ]

Engineers reinventing the tech wheel?!

2015

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!

[ 3 ]

effective architecture

scalability & robustness

metrics & monitoring

2015

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!

[ 3 ]

effective architecture

scalability & robustness

metrics & monitoring

Product in a bubble?!

2015

Data Science

data product

Product

Engineering

2015

3

Three Steps to Risa**

21

** Risa is to Nirvana as Spark is to Hadoop

2015

321

Build an Awesome Team

2015

awesome team

2015

never settle

2015

find the right mix

minimum

prodds

eng

good

good

good

target

great

good

good

prodds

eng great

good

good

prodds

enggreat

good

great

prodds

eng

2015

form pods around product

personalization & reco pod

real time data capture &

stream proc. pod

businesssearch pod

real timecommercegraph pod

2015

blur the boundaries

2015

321

Solve a Big Problem

2015

Solve a big problemidentify big problem

2015

keep score

2015

change, pivot, iterate

2015

321

Get Out of the Way

2015

time

trust the team to become experts

2015

anyone can represent the team

2015

your role as a coach?

2015

engage!

2015

Three Steps to Risa**

3

2

1 awesome team (pods)

solve a big problem (pods)

get out of the way (pods)

** Risa is to Nirvana as Spark is to Hadoop

2015

Most companies are not there yet

2015

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

prodds

enggood

tinytiny

2015

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

?

prodds

engamazing

tinygood

2015

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

2015

Example 4: Attempt at Introspection

An awesome team with data, product and engineering working together

Solving hard problems – for individuals and small businesses

Need to do a lot more work to get the right metrics in place – need more work to be 100% eyes on, hands off.

build an awesome team

solve a big problem

engage

2015

3

2

1 awesome team (pods)

solve a big problem (pods)

get out of the way (pods)

2015

Anu Tewary@anutewary

2015

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