The document presents an overview of applied data science, emphasizing its role in solving practical problems and delivering actionable insights. It outlines the challenges faced by data science teams, such as complex operationalization and ROI measurement, while detailing the value chain of extracting insights from data. Additionally, it highlights five key attributes for successful data science teams, including a customer focus and the importance of iterative improvements.

1. Applied Data Science
Making insights accessible and actionable
PRESENTED BY
Colin Ristig
Product Manager
colin@yhathq.com
Austin Ogilvie
Founder & CEO
a@yhathq.com

2. Agenda
Quick Intro to Data Science
Understanding the Value Chain
Designing Your Data Science Process

3. About Us

4. We help data scientists
build & deploy apps

5. Founded 2013
Headquarters in NYC

6. You may know us from

7. Data
Science
in 30 seconds

8. Data Science in 30 Seconds
Broadly…
A multidisciplinary field concerning
problem solving using data,
statistics & software.

9. “ 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

10. It’s about day-to-day problems
Carl wants to watch
a good movie.

11. And practical, real-world solutions
Carl wants to watch
a good movie.
Hey, Carl.
Check these out!

12. 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.

13. Data
Science
Challenges

14. 30%

15. Why?

16. Key obstacles data science teams face
Lack of Understanding

17. Key obstacles data science teams face
Difficulty of Experimentation

18. 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.

19. 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

20. Trey hands his work to deployment engineers

21. “Throw it over the wall” projects
Execs Data Science Application Developers

22. Common reasons these types of projects stall
- Unclear benefits
- Skepticism about effectiveness
- Too complex to operationalize
- Too time-consuming
- Unclear how to measure ROI

23. Data
Science
Value Chain

24. 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

25. 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

26. 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

27. Cost of Creating that Value
Building data products requires lots of work

28. Cost of Creating that Value
But most of the value is generated at the end

29. Cost of Creating that Value
Data Teams
Managers
Customers
Everyone has to see past a lot of challenges

30. Data
Science
Customers

31. - Consumers
Several types of customers
Carl wants to watch a good movie.

32. - Consumers
- App Developers
Cambria needs to call credit models from Salesforce.
Several types of customers

33. Douglas needs 3 AM server outages to stop.
Several types of customers
- Consumers
- App Developers
- Infrastructure Admins

34. Gordon wants sales reps calling the hottest leads.
Several types of customers
- Consumers
- App Developers
- Infrastructure Admins
- Sales & Marketing

35. Data
Science
5 Attributes
for Success

36. 1. Focus on the customer
5 Attributes of Successful Data Science Teams

37. 1. Focus on the customer
2. Identify practical constraints
5 Attributes of Successful Data Science Teams

38. 1. Focus on the customer
2. Identify practical constraints
3. Start small but ship quickly
5 Attributes of Successful Data Science Teams

39. 1. Focus on the customer
2. Identify practical constraints
3. Start small but ship quickly
4. Measure the impact
5 Attributes of Successful Data Science Teams

40. 1. Focus on the customer
2. Identify practical constraints
3. Start small but ship quickly
4. Measure the impact
5. Relentless iteration
5 Attributes of Successful Data Science Teams

41. 1. Focus on the customer
2. Identify practical constraints
3. Start small but ship quickly
4. Measure the impact
5. Relentless iteration
5 Attributes of Successful Data Science Teams

42. Demo

43. 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

44. Trey hands his work to deployment engineers

45. “Throw it over the wall” projects
Data Science Application Developers

46. Deploy Models Faster
Data Science Application Developers