1. Datafication of Recruitment:
Measuring and quantifying
relevant information

2. Introductions
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North America RPO President (Pontoon)
• More than 100 client engagements in North America, EMEA, and APAC
• Team is responsible for >73,000 hires per annum
• One every 7 minutes of a business day
• Prior: Executive Director, HR Outsourcing Association
Director of Recruiting, North America (Gate Gourmet)
• Responsible for the talent acquisition of more than 10,000
employees across North America
• 16 years of Recruiting & HR leadership experience

3. Datafication of recruitment: some context for “big data”
• Not looking for a needle in a haystack, but
rather looking for a unique piece of hay in
a moving field of millions of haystacks
• Attributes differ from traditional data in
three main ways:
– Volume, Velocity, Variety
• Stands apart from traditional analysis
– Not organized in tables and rows
– Uses data flow versus stock
– Best used by process owners instead of data
analysts or IT (e.g. owned by the business
process/function/operations)
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4. • Volume
– Data will be measured in Exabytes very soon (1,000 petabytes)
– Wal-Mart handles a million customer transactions per hour and has c 2.5
petabytes of disparate data, Google has 24 petabytes (24,000 terabytes)
• Velocity
– Data is flowing in real-time, not static
• Blogs, Twitter, Facebook, LinkedIn, etc.
– Real-time data extremely valuable, but degrades in value very quickly
– Data may be fleeting if not captured
• Variety
– Myriad possible sources of data
– Data is unstructured, in multiple systems, in different back-end formats, etc.
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The three V’s

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Growth of Big Data
Monetary value of
big data by industry

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The promise of Big Data in everyday life

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The promise of Big Data in recruitment?

8. The three V’s applied to recruitment
• Variety
– Internal Systems – ATS, HRIS, Performance Review Systems, POS data, Sales
performance and comp systems
– External Systems - Compensation, Demographics, Recruiting informatics, Traffic
and transportation, Social Media , Employment Social
• Volume
– Up to 2,520 data points per candidate
• Contact and profile information
• History and status information
• Each candidate dataset has a different set of data points
• Velocity
– Hundreds of applicants per day, different requisitions, statuses, hiring managers,
locations etc.
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9. • With the data that sits in your current systems, right now, you could
probably answer the following questions:
– What is the relationship between the compensation of a position and the distance
candidates are willing to commute?
– What is the interaction between posted job title and number of applicants?
– What is the maximum ideal number of interviews for a candidate? (btw, it’s 4)
– Does using the phrase “proactive and detail oriented” drive a higher quality
candidate slate?
– What is the relationship between spelling errors on a resume and a candidates
ultimate success on the job?
• Do you think having access to this kind of information can make you
more actionable around your approach?
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Example: Big Data opportunity in context of recruitment

10. 10
Case Study:
How Gate Gourmet Datafied
Recruitment in SFO
Datafication of Recruitment: Dallas September 2013

11. • Gate Gourmet is the core business behind gategroup
• World’s largest independent provider of catering and provisioning for the
Airline & Railroad Industries
• Gate Gourmet serves over 250 million meals per year out of our 120 airport
locations around the globe
• Global HQ in Zurich, Switzerland and North American HQ in Reston, VA
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About Gate Gourmet

12. 12Datafication of Recruitment: Dallas September 2013
A shameless infomercial about Gate Gourmet 

13. Situation before leveraging Big Data insights and analysis
• 626 employees working 24/7/365 shifts
• Hourly workforce understaffed by 20% driving 15.7% OT
• Wages and transportation costs barriers to staffing optimization
Goal: focus sourcing to overcome these challenges
• Data in three sources:
– CRM (Avature) for candidate outreach, source, and location data
– ATS (Taleo Business Edition) for applicant location data
– HRIS (Oracle) for retention/turnover data
• Big data sets:
– CRM 156,022 candidates – 31,204,500 data points
– ATS 108,252 candidates – 29,769,300 data points
– HRIS 35,226 records – 3,522,600 data points
• Implemented proprietary data visualization technology
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Case study: San Francisco International Airport (SFO)

14. • Red dots represent all candidates who
were sourced and we connected with
• Green dot in middle represents the job
location (SFO)
• Notice candidates pulled from all
directions – North, South, East, West
– Predominantly South and Southeast
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Logically we were sourcing candidates from a wide area

15. • Yellow dots represent all applicants
who applied to the job
• Notice the applicants are heavily
clustered to the NORTH of SFO, much
fewer willing to come from the SOUTH
and across the San Mateo Bridge
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Big Data analysis of applicants revealed new insights

16. And when it came to actual hires, the visual is self-evident
• Blue dots represent applicants who
were hired for the job
• Notice the applicants are heavily
clustered
• Most hires resulted from NORTH of
the job location with heaviest
concentration on the peninsula
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17. After launching this location with our RPO partner in 2012:
• We became much more productive: effective and efficient
• We stopped wasting time and money sourcing people from areas that did
not bring good results – and the business results were profound!
– Increased candidate attraction by 408%
– Net Headcount growth increased 21%
– Interview to offer ratio improved from 1.6 to 1, now is at 1.1 to 1
– Reduced overtime and temporary labor expense by over $2 Million
– Achieved “fully staffed” status for first time ever
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Results

18. 18
Practical Advice
Datafication of Recruitment: Dallas September 2013

19. • What business problem can you tackle and solve that might produce
real bottom line impact?
• What are some of the assumptions in your current recruitment and
talent management process that you might validate or disprove?
• Who owns your data (or access to it)?
• Can you make a business case for investment?
• What system are you going to normalize the data into?
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Points to consider

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Let’s action our learning

21. Michael Beygelman
North America RPO President, Pontoon
michael.beygelman@pontoonsolutions.com
Lisa Johnson
Director of Recruiting, North America, Gate Gourmet
Ljohnson@gategroup.com
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Questions, follow up contact