yhat - applied data science - feb 2016

Applied Data ScienceMaking insights accessible and actionable

PRESENTED BY

Colin RistigProduct Managercolin@yhathq.com

Austin OgilvieFounder & CEOa@yhathq.com

Agenda

Quick Intro to Data Science

Understanding the Value Chain

Designing Your Data Science Process

About Us

We help data scientists build & deploy apps

Founded 2013Headquarters in NYC

You may know us from

Data Sciencein 30 seconds

Data Science in 30 Seconds

Broadly…

A multidisciplinary field concerning

problem solving using data,

statistics & software.

“ What distinguishes data science itself from

the tools and techniques is the central goal

of deploying effective decision-making

models to a production environment. ”

Data Science is not “Interesting Research”

~ Nina Zumel & John Mount, Practical Data Science with R

It’s about day-to-day problems

Carl wants to watch a good movie.

And practical, real-world solutions

Carl wants to watch a good movie.

Hey, Carl. Check these out!

Explanation isn’t always important

Carl wants to watch a good movie.

Carl

Cindy

http://courses.washington.edu/css490/2012.Winter/lecture_slides/08b_collaborative_filtering_1_r1.pdf

Carl would like Frozen because Cindy liked it.

Data ScienceChallenges

30%

Why?

Key obstacles data science teams face

Lack of Understanding

Key obstacles data science teams face

Difficulty of Experimentation

Hey, Trey. Online sales are down. What can we do to keep users engaged and shopping carts full?

Trey is asked to “look into something”

I’ll look into it.

Hm...cool. Can you talk to the

dev team?

Here’s what we should do:

Trey uncovers a bunch of things we didn’t know

Trey hands his work to deployment engineers

“Throw it over the wall” projects

Execs Data Science Application Developers

Common reasons these types of projects stall

- Unclear benefits- Skepticism about effectiveness- Too complex to operationalize- Too time-consuming- Unclear how to measure ROI

Data ScienceValue Chain

Making data valuable

Collect and display individual records

Structure, link, metadata, interact, share

Understand, infer, learn

Drive value,

change

Clean, aggregate, visualize

Actions

Predictions

Reports

Charts

Records

Extracting value from data is like any other value chain.

Value

Like a raw material, data has no obvious utility to start out.

Collect and display individual records

Structure, link, metadata, interact, share

Understand, infer, learn

Drive value,

change

Clean, aggregate, visualize

Actions

Predictions

Reports

Charts

Records

Value

Making data valuable

We make it valuable through sequential refinement.

Collect and display individual records

Structure, link, metadata, interact, share

Understand, infer, learn

Drive value,

change

Clean, aggregate, visualize

Actions

Predictions

Reports

Charts

Records

Value

Making data valuable

Cost of Creating that Value

Building data products requires lots of work

Cost of Creating that Value

But most of the value is generated at the end

Cost of Creating that Value

Data Teams

Managers

Customers

Everyone has to see past a lot of challenges

DataScienceCustomers

- Consumers

Several types of customers

Carl wants to watch a good movie.

- Consumers- App Developers

Cambria needs to call credit models from Salesforce.

Several types of customers

Douglas needs 3 AM server outages to stop.

Several types of customers

- Consumers- App Developers- Infrastructure Admins

Gordon wants sales reps calling the hottest leads.

Several types of customers

- Consumers- App Developers- Infrastructure Admins- Sales & Marketing

DataScience5 Attributes for Success

1. Focus on the customer

5 Attributes of Successful Data Science Teams

1. Focus on the customer2. Identify practical constraints

5 Attributes of Successful Data Science Teams

1. Focus on the customer2. Identify practical constraints3. Start small but ship quickly

5 Attributes of Successful Data Science Teams

1. Focus on the customer2. Identify practical constraints3. Start small but ship quickly4. Measure the impact

5 Attributes of Successful Data Science Teams

1. Focus on the customer2. Identify practical constraints3. Start small but ship quickly4. Measure the impact5. Relentless iteration

5 Attributes of Successful Data Science Teams

1. Focus on the customer2. Identify practical constraints3. Start small but ship quickly4. Measure the impact5. Relentless iteration

5 Attributes of Successful Data Science Teams

Demo

Hm...cool. Can you talk to the

dev team?

Here’s what we should do:

Trey uncovers a bunch of things we didn’t know

Trey hands his work to deployment engineers

“Throw it over the wall” projects

Data Science Application Developers

Deploy Models Faster

Data Science Application Developers