Data is no longer for nerds only! Computers follow people around all day, and everytime we interact with them, that’s a data point captured. How that data is handled makes a big impact on the user experience. Every industry works with informatics. Fundamental concepts can be elusive for those who don’t have a background in it. This deck was presented at Big Design 2017 by Alexander Sicular and Alyssa Gruen. They explore concepts in data storage, retrieval, and processing and how it impacts user experience and business. Dive into a deeper understanding of how data is leveraged in both startup and enterprise environments. Learn key concepts of how data is processed behind the scenes. Walk away with talking points to facilitate conversations with technologists in your organization.

1. BENEATH THE SURFACE
IMPACT OF DATA ON UX
Big Design Conference 2017
Alexander Sicular
@siculars
Alyssa Gruen
@alyssa_g

2. AGENDA
• Data: Why
• Key Concepts
• Microservices
• Database
• Data Science

3. DO YOU BELIEVE IN MAGIC?

4. WHAT THEY SAID
TECHNICAL CONSTRAINTS & CAPABILITIES
Developers Designers

5. WHAT THEY SAID
NEED FOR UNDERSTANDING
Designers
Developers

6. WHAT THEY SAID
PERCEPTION OF UNDERSTANDING
Designers
Developers

7. CAN I GET SOME (MICRO)SERVICE AROUND HERE?

8. KEY CONCEPT
MICROSERVICES
ARCHITECTURE
Interoperable
Well Defined APIs
Modular
Decoupled
A method for architecting
large applications by
loosely connecting
smaller self-contained
components.

9. RETAIL APPLICATION
THE MONOLITH
Payment
Processing
Session
Management
Shopping Cart
Returns
Product
Catalogue
User Profiles
Inventory
Authentication
Reports
Checkout
Message
Queue

10. The most damaging
phrase in language
is ‘It’s always been
done that way.’”
- Grace Hopper

11. RETAIL APPLICATION
MICROSERVICES ARCHITECTURE
Payment
Processing
Session
Management
Shopping Cart
Returns
Product
Catalogue
User Profiles
Inventory
Authentication Reports
Checkout
Message
Queue

12. ENABLING TECH
MICROSERVICES ARCHITECTURE
GET /statuses/show/:id
https://dev.twitter.com/rest/
reference/get/statuses/show/id
POST /statuses/update
https://dev.twitter.com/rest/
reference/post/statuses/update

13. RETAIL APPLICATION
MICROSERVICES ARCHITECTURE

14. RETAIL APPLICATION
MICROSERVICES ARCHITECTURE
Product
Catalogue

15. RETAIL APPLICATION
MICROSERVICES ARCHITECTURE
Shopping Cart

16. RETAIL APPLICATION
MICROSERVICES ARCHITECTURE
Payment
Processing

17. RETAIL APPLICATION
MICROSERVICES ARCHITECTURE
Authentication

18. RETAIL APPLICATION
MICROSERVICES ARCHITECTURE
User Profile

19. RETAIL APPLICATION
MICROSERVICES ARCHITECTURE
Reporting

20. CHALLENGES
MICROSERVICES
ARCHITECTURE
1. Increased
Complexity
2.Location Services
3.More Surface Area

21. Share your designs early
and often.
UX KEY TAKEAWAYS
MICROSERVICES ARCHITECTURE

22. Microservice architecture
lends itself to iterative ux
design.
UX KEY TAKEAWAYS
MICROSERVICES ARCHITECTURE

23. DATA, DATA, EVERYWHERE

24. KEY CONCEPT
DATABASE
Speed
Availability
Data Model
Scalability

25. Transactional
Expressive query language
Rigid, relational, normalized
data model
Limited availability and
scalability
MAJOR TYPES
DATABASE
1. Relational
2. Non-Relational
Legacy
Emerging
Non - Transactional
Limited query language
Flexible, non-relational, de-
normalized data model
High availability and
scalability

26. RELATIONAL
DATABASE

27. NON-RELATIONAL
DATABASE

28. Know thy database.
UX KEY TAKEAWAYS
DATABASE

29. Flexible data models (non-
relational) support iterative
design and development.
UX KEY TAKEAWAYS
DATABASE

30. Legacy products that are
info-centric, rather than
user-centric often depend
on a normalized data-
model (relational).
UX KEY TAKEAWAYS
DATABASE

31. Include a scheduled
maintenance user story in
your design if the product
uses a relational database.
UX KEY TAKEAWAYS
DATABASE

32. DATA SCIENCE…OH YEAH.

33. KEY CONCEPT
DATA SCIENCE
Machine Learning
Recommender System
Analytics

34. CANONICAL DATA SCIENCE VENNY’D
DATA SCIENCE

35. AND ANOTHER ONE
DATA SCIENCE
http://www.prooffreader.com/2016/09/battle-of-data-science-venn-diagrams.html

36. MACHINE LEARNING: RECOMMENDER SYSTEM
DATA SCIENCE

37. MACHINE LEARNING: RECOMMENDER ENGINES
DATA SCIENCE

38. AMAZON: IF YOU LIKE THIS THEN YOU’LL LIKE THAT
DATA SCIENCE

39. User “Likes” something
Group based on content/product
similarity
Ex. Songs, movies, books
MODEL TYPES
RECOMMENDER
SYSTEMS
1. CONTENT
2. COLLABORATIVE
User activity comparisons
Group Based on vast amounts of
data
Ex. Likes, search history,
purchase history, AB testing

40. CONTENT MODEL V. COLLABORATIVE MODEL
RECOMMENDATION ENGINE
Content Collaborative

41. Recommender Systems
streamline information
architecture.
UX KEY TAKEAWAYS
DATA SCIENCE

42. User Interface abstracts
the complexity of data
science.
UX KEY TAKEAWAYS
DATA SCIENCE

43. User experience
professionals define
metrics that matter to
users.
UX KEY TAKEAWAYS
DATA SCIENCE

44. It takes a village.
UX KEY TAKEAWAYS
DATA SCIENCE

45. Our technology, our
machines, is part of our
humanity. We created
them to extend ourselves,
and that is what is unique
about human beings.
- Ray Kurtzweil

46. THANK YOU!
Alexander Sicular
@siculars
Alyssa Gruen
@alyssa_g