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Laboratory for Web ScienceUniversity of Applied Sciences Switzerland

(FFHS)

http://lwsffhs.wordpress.comhttp://lws.ffhs.ch

Follow @blattnerma

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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