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Big Data and HR - some thoughts
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1
Laboratory for Web ScienceUniversity of Applied Sciences Switzerland
(FFHS)
http://lwsffhs.wordpress.comhttp://lws.ffhs.ch
Follow @blattnerma
2
Team
Data enthusiasts
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Agenda
• Big Data and Data Science – what the heck?• HR and “Big” Data – a perfect match?• Cases• Discussion
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Big Data
“Knowing the name of something doesnot mean to know something…”
- Richard P. Feynman
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Big Data
Everybody is talking about it
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Big Data
Machine Learning
Hadoop
Big Data
Search term popularity(fetched 12.9.14)
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Big Data – why?
Unlock the hidden informationin data with advancedanalytical methods.
New insights lead tocompetitive advantages
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Big Data - Industries
Healthcare Academia Finance
Manufacturing HR
…you name it
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Big Data – future driven
Business value
Cos
ts/C
ompl
exity
raw data
standardreports
ad hocreports
standardstats
past driven
predictiveanalytics
whatever
future driven
Big Data
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Big Data
…high expectations…
let’s call it a hype
Source: Gartner
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Big Data - Providers
….there are a lot of players…..
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Big Data – Definition
Volume
• Petabyte and more
Velocity
• Speed of generation of data
Variety
• Diverse categories
Definition: Gartner (2012)
3 V’s
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Big Data Volume
• Petabyte and more
Velocity
• Speed of generation of data
Variety
• Diverse categories
Current definition (3 V’s) + high expectations =
misleading associations
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Big Data – misleading associations
Big data = Data analysis(extracting useful information needs
a vast amount of data)
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Big Data – misleading associations
Big Data = Big company and big infrastructure(Big Data is only an option for big companies)
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Big Data
The common thinking about Big Dataleads to a digital “two-tier society”.
Big Data rich and Big Data poor institutions/companies
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Big Data - Volume
Volume
• Petabyte and more
Velocity
• Speed of generation of data
Variety
• Diverse categories More data carry more insights.
Misconception #1
1. Signal-to-Noise ratio can be worse2. Strong but spurious correlations3. Fooled by the curse of dimensionality
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Big Data – Technology matters
Volume
• Petabyte and more
Velocity
• Speed of generation of data
Variety
• Diverse categories Technology matters most.
Misconception #2
1. Algorithms do not generate knowledge2. Technology for technology’s sake3. Technology beats business
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Big Data – Data Science
Volume
• Petabyte and more
Velocity
• Speed of generation of data
Variety
• Diverse categories Big Data projects generate facts.
Misconception #3
1. Big Data is not a science2. Whatever you do, you can’t predict the future
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Data
To most relevant ingredients for asuccessful “Big” Data project:
• Curiosity and creativity• Carefully selected data (not necessarily big)• A useful and strategic relevant business question
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Data Scientist
From raw data to business insights!Who can do this?
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Data Scientist
Modeling
Math
Visualization
Domain knowledge
Technology
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Data Scientist
We need a data hero called data scientist
…but you can not hirethis guy. He lives in the land of OZ
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Data Science Team
Source: Doing Data Science, Published by O’Reilly Media, Inc., 2013
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Data Science Team
Team upa balanced
skill landscape
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Data Science Team
Business Question
Data Acquisition
Data Normalization
Modeling
Model Assessment
Visualization
Communication
Validation
Data ScienceTeam
Num
ber crunchingH
uman
inte
rpre
tatio
n
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Summary Big Data and Data Science
Takeaway message #1:
Methods and Algorithms developed within theBig Data Hype are useful and work on smaller
data sets as well (sometimes even better).
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Summary Big Data and Data Science
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.
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Summary Big Data and Data Science
Takeaway message #3:
Business domain knowledge is key.
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Relevance for HR?
• Candidate does not see your job offer(time and location)
• Organization doesnot reach candidate(time and location)
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Relevance for HR - Case
Possible business question:
What time is the right time toproactively approacha potential candidate?
too late…..too early…
time
cand
idat
e jo
b se
ekin
g ac
tiviti
esJob seeking activity patterns?
passive active
active phase
‘sweet spot’
applicationlearn pattern
Job seeking activity patterns?
time
cand
idat
e jo
b se
ekin
g ac
tiviti
es
Job seeking activity patterns - data
Job seeking activity patterns
LinkedIn Facebook XingTwitter
subscription (social login)crawled
profile matcher
skill matcher
job recommender (time dependent)
Feedback
pattern learning
Job seeking activity patterns - data
passive
active
People first approach
Example: technical staff
Skill mixing (the nerd slide)
Example: team-up heterogeneous skill landscape
Blattner, M. (2009), 'B-Rank: A top N Recommendation Algorithm',
CoRR abs/0908.2741 .
Candidate
Ski
lls (
mea
sure
d)
Discussion
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