This document discusses several topics related to big data and data science over time, including: 1. In the past, competition and control were key issues in big data, while cloud computing and crowdsourcing became popular. 2. More recently, topics like curation, culture, and containers have become important in data science, as context and the container may be more important than just content alone. 3. Looking to the future, predictive analytics, smart data, and making data actionable will be important trends, as will balancing accuracy and real-time capabilities in big data applications.

1. unexperienced
Jeong, Buhwan

2. Past Present and Future 2010, & 3 yrs later

3. ~2010

4. Competition
Context
Control Cloud

5. Consumption
Crowds
Convenience
Connection

6. 2013~

7. Curation

11. We purchase convenience.

12. Container

13. I want to travel Jeju, too.

14. Content is king.
Context is anther content.

15. NEXT?
Media is message.
Is container the next?

16. Content
Context
Container
Topic
Contents Perspective
Style/Nuance Container is brand.
Situation Time & Space
Context Relation
Person
History/Interest
Expertise
Container Reputation
Influence

17. What do people think about us?
Container is the first.

18. Culture

19. Cultural Creativity vs Creative Culture

22. Daum Culture?

23. Data-driven execution

24. Community of Practice (CoP)
Spotify

25. DevDay/Hackertone
Imagine impossible, do possible.

26. THEN NOW
Context Container 5 Internet Trends
Competition Convolution 1. Structured/Linked Data
Control (un-)Certainty 2. Real-time Internet
Cloud C
3. Personalization
Crowds Curation
4. Mobile & Augmented Reality
Consumption Culture
Convenience Control 5. Internet of Things
Connection Continuity
ir ls?
o tg
h
are
here
w

28. Data Science

29. We’ve faced challenges to dealing with
Large-Scale Data
Time-Series Data
in Real-Time
+ Big Data
Unstructured Data
Persuasion
with
Interpretation
&
Visualization

30. Big Data
Volume Platform Problem
Variety
o rk
Velocity W
+ a ta
D
Value People Productivity
Platform
Monitoring/ Decision
Execution
Sensing Making
Intelligent System

31. Smart Data
- Moving from big data to smart data
- 2013: The year of smart data
- Big data spotlight: Big data is dead! Long live smart data!
- Big data won’t save pharma, but smart data might
So Much Data, So Little Impact.
- Big data? Try smart data
- Investing in big data’s second act

32. Make Big Data work.
Big Data + Analytics + Visualization
Actionable Insight

33. If you can predict the future, you also change it.
Predictive Analytics

34. Prediction
Model
Evaluation
Model Predictive Control

36. Realtime
- Simple algorithms
Counting / Random Forest / Regression
Accurate vs Real-time
- Feature selection & summary
- Streaming processing and analytics
- In-memory storage
Data Science = Data + Analytics + Action

37. So, what?
Those who cannot remember the past are condemned to repeat it.
We live in pasts we’ve never lived.

38. No prepare, just repair.
Make you survive by yourself.