Big Data and HR - some thoughts

1. Laboratory for Web Science
1
University of Applied
Sciences Switzerland
(FFHS)
http://lwsffhs.wordpress.com
http://lws.ffhs.ch
Follow @blattnerma

2. Team
2
Data enthusiasts

3. Agenda
3
• Big Data and Data Science – what the heck?
• HR and “Big” Data – a perfect match?
• Cases
• Discussion

4. Big Data
4
“Knowing the name of something does
not mean to know something…”
- Richard P. Feynman

5. Big Data
5
Everybody is talking about it

6. Big Data
6
Machine
Learning
Hadoop
Big Data
Search term popularity
(fetched 12.9.14)

7. Big Data – why?
7
Unlock the hidden information
in data with advanced
analytical methods.
New insights lead to
competitive advantages

8. Big Data - Industries
8
Healthcare Academia Finance
Manufacturing HR
…you name it

9. Big Data – future driven
9
Business value
Costs/Complexity
standard
reports
raw data
ad hoc
reports
standard
stats
past driven
whatever
predictive
analytics
Big Data
future driven

10. Big Data
10
Source: Gartner

11. Big Data - Providers
11
….there are a lot of players…..

12. Big Data – Definition
12
Volume
• Petabyte and more
Velocity
• Speed of generation of
data
Variety
• Diverse categories
Definition: Gartner (2012)
3 V’s

13. Big Data
13
Volume
• Petabyte and more
Velocity
• Speed of generation of data
Variety
• Diverse categories
Current definition (3 V’s) + high expectations
=
misleading associations

14. Big Data – misleading associations
14
Big data = Data analysis
(extracting useful information needs
a vast amount of data)

15. Big Data – misleading associations
15
Big Data = Big company and big infrastructure
(Big Data is only an option for big companies)

16. Big Data
16
The common thinking about Big Data
leads to a digital “two-tier society”.
Big Data rich and Big Data poor institutions/companies

17. Big Data - Volume
17
Volume
• Petabyte and more
Velocity
• Speed of generation of data
Variety
Misconception #1
• Diverse categories More data carry more insights.
1. Signal-to-Noise ratio can be worse
2. Strong but spurious correlations
3. Fooled by the curse of dimensionality

18. Big Data – Technology matters
18
Volume
• Petabyte and more
Velocity
• Speed of generation of data
Variety
Misconception #2
• Diverse categories Technology matters most.
1. Algorithms do not generate knowledge
2. Technology for technology’s sake
3. Technology beats business

19. Big Data – Data Science
19
Volume
• Petabyte and more
Velocity
• Speed of generation of data
Variety
Misconception #3
• Diverse categories Big Data projects generate facts.
1. Big Data is not a science
2. Whatever you do, you can’t predict the future

20. Data
20
To most relevant ingredients for a
successful “Big” Data project:
• Curiosity and creativity
• Carefully selected data (not necessarily big)
• A useful and strategic relevant business question

21. Data Scientist
21
From raw data to business insights!
Who can do this?

22. Data Scientist
Domain knowledge
22
Math
Modeling
Visualization
Technology

23. Data Scientist
…but you can not hire
this guy. He lives in the
land of OZ
23
We need a data hero called data scientist

24. Data Science Team
24
Source: Doing Data Science, Published by O’Reilly Media, Inc., 2013

25. Data Science Team
Team up
a balanced
skill landscape
25

26. Data Science Team
26
Business
Question
Data
Acquisition
Data
Normalization
Modeling
Model
Assessment
Validation
Communication
Visualization
Data Science
Team
Number crunching
Human interpretation

27. Summary Big Data and Data Science
27
Takeaway message #1:
Methods and Algorithms developed within the
Big Data Hype are useful and work on smaller
data sets as well (sometimes even better).

28. Summary Big Data and Data Science
28
Takeaway message #2:
To successfully extract strategic relevant information
from your data you need a good mix of skills (team).
Develop explorative, fast, and fail early.

29. Summary Big Data and Data Science
29
Takeaway message #3:
Business domain knowledge is key.

30. Relevance for HR?
• Candidate does not
see your job offer
(time and location)
30
• Organization does
not reach candidate
(time and location)

31. Relevance for HR - Case
31
Possible business question:
What time is the right time to
proactively approach
a potential candidate?

32. too early… too late…..
time
candidate job seeking activities
Job seeking activity patterns?
passive active

33. Job seeking activity patterns?
learn pattern application
active phase
‘sweet spot’
time
candidate job seeking activities

34. Job seeking activity patterns - data

35. Job seeking activity patterns
crawled subscription (social login)
LinkedIn Twitter Facebook Xing
profile matcher
skill matcher
job recommender
(time dependent)
pattern learning
Feedback

36. Job seeking activity patterns - data
passive
active

37. People first approach
Example: technical staff

38. Skill mixing (the nerd slide)
highlight various aspects of B-Rank, a toy network
introduced. For simplicity all links between ob-jects
users are equally weighted wi = 1 8i.
Example: team-up heterogeneous skill landscape
Candidate
Blattner, M. (2009),
Skills (measured)
'B-Rank: A top N Recommendation Algorithm',
Toy net to CoRR illustrate abs/0908.2741 B-.
Rank. Circles represent
hyperedges (users), squares are hypervertices, i.e. ob-jects.
votes are illustrated as links between objects

39. Discussion