The Implications of Big Data on Employee Referrals and Recruiting
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Transcript of The Implications of Big Data on Employee Referrals and Recruiting
BIG DATA: THE IMPLICATIONS FOR RECRUIT ING & REFERRALS
A R O L E P O I N T W H I T E P A P E R B Y @ B I L L B O O R M A N
3RolePoint Inc. © 2014 Big Data
4RolePoint Inc. © 2014 Big Data
B I G D A T A
INTRODUCTION
We have always created and stored data
in one format or another. Historians can
go back thousands of years to
understand how people lived in centuries
past thanks to the evidence and
indications of what has happened that is
stored deep in museums and archives,
historical documents and hand curated
registers such as the census. The
collation, interrogation and interpretation
of data and what it means is far from a
new concept, but what has now brought
the discussion to the fore in all aspects of
business is the incredible volume of data
that is being created every moment of
every day, and the development of data
mining and interpretation tools that are
available for anyone to use.
We are creating data and data trails at an
unequaled rate. Pretty much everything
we do is recorded somewhere. Every
keystroke, click, call or action is recorded
and searchable. Our domestic appliances,
cars, games and everyday appliances
leave a trail while mobile devices leave
data trails for geolocation use. This data,
often called ‘multi-structured’, includes
loosely structured social media data and
content, as well as the deluge of
machine-generated data like geolocation
and data usage information coming from
networked devices and packaged goods
with embedded sensors. The data
generated from these devices offers
game-changing opportunities in
operational process optimization and
reinvention, as well as in business
analytics and intelligence. HR and
recruiting is just one of the many business
functions to benefit from this new way of
working.
In April 2012, Information Management
reported that we create 2.5 quintillion
bytes of data every day, with 90% of the
data in the world having been created in
the last two years alone. Every hour,
Wal-Mart handles 1 million transactions,
feeding a database of 2.5 petabytes,
which is almost 170 times the data in the
Library of Congress. The entire collection
of the junk delivered by the U.S. Postal
Service in one year is equal to 5
petabytes, while Google processes that
amount of data in just one hour. The total
amount of information in existence is
estimated at a little over a zettabyte.
The real challenge starts when attempting
to deal with the wealth of data that is
available. What organizations really want
is to be able to navigate the data
landscape in order to find and interpret
5RolePoint Inc. © 2014 Big Data
what is useful in order to make informed
business decisions. The hottest job in
business over the past 18 months has
been data analyst, as more and more
businesses look to understand just what
this data means to them and how they
can use it to their advantage in every
aspect of their business, not least
recruiting. Internal data alone has massive
implications for all HR functions
throughout business, because decisions
based on data are based on fact.
In this paper we will not attempt to make
the case for big data in recruiting and
referral - you ignore it at your peril.
Rather, we will look at practical
applications within recruiting. The easiest
way to understand big data concepts is
that big data practices take numbers
from multiple sources structured and
unstructured (such as social media
channels), identify relationships and
interpret meaning to different data sets
and deliver an output that is easy to
understand without expert knowledge
(data visualization.) A 2013 report by the
Aberdeen Group found that of
organizations that use visual discovery
tools, 48 percent of business information
users are able to find the information they
need without the help of IT staff. Without
visual discovery, the rate drops to a mere
23 percent. When data is visual and real
time, everyone benefits from being
informed and current. It’s an old saying,
but it has never been more applicable: “In
God we trust, everyone else bring data!”
6RolePoint Inc. © 2014 Big Data
S E C T I O N H E A D E R
V
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B I G D A T A
INTERNAL DATA
Companies have always recorded and
stored data. One of the challenges
companies face is that their corporate
data is kept in silos within individual
databases. For the most part, the
historical business database was built for
data storage rather than data retrieval.
Within HR, companies may be collating
and retaining data in 5 - 8 individual
places with no link from one to the other.
HR systems might consist of:
Payroll System:
Contains pay data and personal data
relating to past and present
employees, earnings, benefits,
company reporting structure,
employment history etc. The payroll
system is an accurate data record of
work history, rewards and other
personal information.
Performance Management System:
Contains performance data, reviews,
appraisals, report, performance
disciplinary records etc. The
performance management system is
an accurate data record of the work
completed by employees against
management expectations, enabling
the ranking or grouping of employees
by actual performance.
Learning and Development System:
Contains training data and
assessments, skills profiles, reviews etc.
The learning and development system
is an accurate data record of
employees’ development, progress and
potential, and enables the
measurement of skills gaps and
workforce planning.
Applicant Tracking System:
Contains applicant and employee data
past and present, recording progress
through the application process. The
ATS is an accurate data record of all
applications for employment,
successful or unsuccessful, and
important recruiting metrics such as
time to hire, source of hire and
applicant to hire ratio. Includes
metrics that are important for
understanding recruiting efficiency,
enabling best use of resources.
However, these are often hard to track.
Candidate Relationship Management
System:
Works like the client relationships
management system and commonly
used in marketing. The CRM enables
the indexing and tracking of candidate
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communication. The CRM follows the
trend for talent communities and
talent networks.
A talent community, such as those
operated by the likes of Microsoft,
focus on a single discipline or interest,
enabling members of the community
to communicate with each other whilst
creating a dynamic profile and leaving
a data trail. While this approach is
often confused with a talent network,
its popularity has exploded over recent
years fueled by social media discussion
and debate. A community can be
defined as a collection of people who
are connected by a topic, where each
member can raise or comment on a
discussion and connect directly with
each other. A good example would be
a LinkedIn group.
A talent network, by contrast, can be
compared to targeted e-mail lists of
past years, although other
communication channels such as SMS
or via a mobile app also qualify, as well
as traditional methods of
communication. Registration for a
talent network is usually as simple as
one-click connections of a potential
candidate with the company, with
profiles populated with external data
from sources such as LinkedIn profiles.
Communication lines are vertical
between candidate and company in
contrast to a talent community, where
anyone can connect and communicate.
The real benefit and appeal of the
talent network is that candidate data
can be analyzed to ensure relevance in
messaging (the same concept that
applies to referral messaging).
Relevance of topic and content is a
critical factor in success, with 75% of
messaging being opened on a mobile
device and the recipient often making
instant decisions over ditching the
message or opening and reading -
research indicates this decision is
made in a maximum of 3 seconds.
Other HR Systems:
Individual companies may well have
other systems housing additional HR
data. The problem for most
organizations is that each HR system
operates and stores data in isolation.
The benefit of utilizing internal data is
that it is owned by the organization
and is considered structured data. The
challenge is combining all of the
datasets for interrogation and
understanding how one dataset might
relate to another, enabling the
discovery of trends and relationships
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B I G D A T A
for interpretation and decision-making.
What hampers this is often internal
resistance to opening up HR data
between departments and opening the
APIs between one technology and
another. An application programming
interface (API) specifies how some
software components should interact
with each other. For organizations to
unlock the potential offered by internal
big data depends on:
• A single data flow in one direction
• Data conversion in to a single
format effectively speaking the
same language
• Data retrieval from all technologies
made possible by an open API
• Refreshed latent data pulling from
real time unstructured social data,
combining internal data with public
data sources such as LinkedIn,
Facebook or Twitter
LATENT DATA
Latent data is dated and becomes old the
day it is recorded. A good example of this
within talent acquisition is the vast
number of resumes that have been
submitted to a company for job openings
as they arise. When we consider the
resume, it becomes an historical, out of
date document from the day it is
submitted as an e-mail address or
contact number may change over time.
Furthermore, candidates will add to their
experience and skills through promotions,
transfers, learning or changing employer.
None of these changes are recorded on
the resume held within the ATS, hiding
many potential matches to new
opportunities. Companies like London
based Work Digital identified the benefit
of searching unstructured social data to
update the latent resume data contained
within the ATS, because social data is real
time. Combining a historical resume with
the data contained on a LinkedIn profile,
a Facebook profile, an about me page
and other similar sources refreshes the
ATS keeping candidate details up to date
in real time, retrievable and relevant in
search.
9RolePoint Inc. © 2014 Big Data
THE SEARS STORY
In a presentation at SourceCon in 2013,
Donna Quintall, the Senior Manager
Executive Talent Acquisition at Sears
Holdings, told the story of how they had
implemented this methodology in to their
recruiting function. Quintell commented
that HR teams very often have all the
data recruiters needed to plan recruiting,
but it is hidden behind the walls built by
HR and recruiting. Sears set about tearing
down the walls to create the HR data
warehouse, fed by their HR systems. Data
feeds come from each of their HR
systems including performance
management, CRM and ATS to produce
reports, dashboards, and analysis. This
means the recruiters can interrogate data
to understand what they should be
recruiting for, costs, speed of hire, loss
due to not hiring and other things like the
best companies to hire from, the best
industries and the best schools. The data
includes things like performance
management, reviews, appraisals and
financials: every aspect of HR data in
order to be proactive, predictive and to
be able to use data to influence hiring
managers.
Every new starter at Sears completes a
profile and all the HR data through their
career gets added in real time. When you
have data, you can interrogate it. How
many organizations recognize that this
type of data would be useful to recruiters,
and how many people think it should be
locked away from the hiring team? When
you have data, you can influence
decisions.
Recruiters get to know when people are
high-risk—triggered by actions like
performance plans—and this models the
recruiting plan. The recruiters source
according to projected needs rather than
simply reacting to jobs as they come up
at the 11th hour. The sourcing plan covers
internal employees as well as external
targets. When you have data, succession
planning and internal mobility become a
reality.
Sears have worked hard to allow
recruiters to have conversations about
internal opportunities freely without
needing to go through layers of
permission. This takes some doing. I
remember having the same conversation
with Arie Ball at Sodexo. Companies talk
internal mobility but block the access to
it through politics and turfism. The best
recruits with the least risk, who are
already known, usually live within the
company, but many recruiters are driven
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B I G D A T A
to source outside. The key in all of this is
transparency of data and trust. Sears has
profiles on over 400,000 employees.
That’s a huge data source.
Every interview is a source of competitor
information that goes into the system,
hired or not. Recruiters are trained to
gather data in the interview (they jokingly
compare this to being interviewed by the
CIA). When I think about how much
market information recruiters could
collect to help influence sourcing and
hiring decisions, the potential is
frightening. This means building a whole
process for data collection and an
emphasis on retrieval through data
mining rather than storage.
When recruiters have the data to
influence and advise recruiters—what
they need to be doing—the perception of
the role changes from being reactive
people-finders to strategic partners.
Sears runs their talent community
through Find.ly. They track all social
media activity to see what topics are
trending. Things like Family Guy and
Eminem are massive trends. They switch
this back into their thinking on content
and content placement. This is starting
with a Family Guy campaign because that
is where their hires are and what they are
interested in.
I love the direction Sears is going with
this thinking. Data drives decisions further
than opinions, just as soon as the walls
come down and recruiters get access,
allowing sourcing from a reactive, just-in-
time function, to playing a more strategic
part in business planning. Decisions are
driven by data and what is really
happening, and Sears are reaping the
benefits.
UNSTRUCTURED DATA
According to Wikipedia, unstructured
data is defined as “information that either
does not have a pre-defined data model
or is not organized in a predefined
manner. Unstructured information is
typically text-heavy, but may contain data
such as dates, numbers, and facts as well.
This results in irregularities and
ambiguities that make it difficult to
understand using traditional computer
programs as compared to data stored in
fielded form in databases or annotated
(semantically tagged) in documents.”
In 1998, Merrill Lynch cited a rule of
thumb that somewhere around 80-90%
11RolePoint Inc. © 2014 Big Data
of all potentially usable business
information may originate in unstructured
form. In recent years the volume of
unstructured data has exploded as a
result of the embedding of social media
sites and the adoption of mobile devices.
Lets consider some of the stats relating
to Facebook, taken from the blog Digital
Marketing Ramblings (http://
expandedramblings.com):
Facebook User Stats
• Total number of Facebook users: 1.26 billion
as of 10/6/13
• Total number of Facebook monthly active
users (MAU): 1.23 billion as of 01/29/14
• Total number of Facebook daily active users
(DAU): 757 million as of 1/29/14
• Daily active users in the US: 128 million as of
8/13/13
• Size of user data that Facebook stores:
more than 300 petabytes as of 11/7/13
Facebook User Activity Stats
• Number of times daily that the Facebook
Like or Share buttons are viewed: 22 billion
(Tweet this stat) as of 11/6/13
• Number of sites that contain Facebook Like
or Share buttons: 7.5 million (Tweet this
stat) as of 11/6/13
• Total number of Facebook friend
connections: 150 billion as of 2/1/13
• Total number of Facebook likes since launch:
1.13 trillion
• Average daily Facebook likes: 4.5 billion as
of 5/27/13
• Total number of location-tagged Facebook
posts: 17 billion
• Total number of uploaded Facebook photos:
250 billion as of 9/17/13
• Average daily uploaded Facebook photos:
350 million as of 2/1/13
• Average number of photos uploaded per
Facebook user: 217 photos as of 9/17/13
• Average number of items shared by
Facebook users daily: 4.75 billion as of
9/17/13
• Number of Facebook messages sent daily:
10 billion as of 9/17/13
• Percentage of Facebook users that login
once a day: 76% as of 7/2/13
• Percentage of users that check Facebook
multiple times a day: 40% as of 12/30/13
• Average number of page likes per Facebook
user: 40 as of 7/12/13
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B I G D A T A
Twitter stats
• Unique monthly visitors to Twitter.com
(desktop only): 36 million as of 5/1/13
• Total Number of Tweets Sent: 300 billion as
of 10/3/13
• Monthly Active Twitter Users: 231.7 million
as of 10/17/13
• Percentage of Twitter MAUs located outside
U.S.: 77% as of 10/3/13
• Percentage of MAUs that accessed Twitter
via mobile device: 75% as of 10/3/13
• Daily Active Twitter Users: 100 million
(Tweet this stat) as of 10/3/13
• Average Number of Followers per Twitter
User: 208 (Tweet this stat) as of 10/11/12
• Average Number of Tweets Sent Per Day:
500 million as of 10/3/13
• Average Number of Tweets per Twitter User:
307 as of 1/11/13
• Number of tweet impressions outside
Twitter properties in Q3 2013: 48 billion as
of 10/3/13
Percentage of Twitter Users Accessing Via
Mobile: 60% as of 12/18/12
When we consider the volume of data
being posted in public social media
channels, documents, forums and other
places, it is easy to understand that, if we
can sort the data and sift social data by
relevance and value, 80% of the world’s
data is considered to be unstructured.
The challenge of unstructured data is not
availability but sifting for relevance and
determining meaning. Unstructured data
is facing the same challenges that
structured information faced in its early
days. CIOs must overcome fragmentation
of information and processes; the
infamous three Vs of information –
volume, variety and velocity; security; and
governance issues. To get meaning we
need to separate the signal from the
noise and that’s not easy to do with
unstructured data.
13RolePoint Inc. © 2014 Big Data
In terms of recruiting, this means
identifying what you want the data to tell
you as an outcome, the questions that
need answering and the areas of decision
making that have historically been based
on opinion. Social data enables profiling
of candidates and audience, identifying
the best fit and the most receptive
targets. The challenge here is three fold:
• Volume of data created
• Velocity at which data is
multiplying in real time, for
example, Twitter recorded 58
million tweets/day mark - over a
billion tweets a month. There are
over 2-1bn Twitter search engine
queries each day. That’s velocity of
data. Source: http://www.statisticbrain.
com/twitter-statistics
• Variety of sources we can access
data from. From Twitter, Facebook,
LinkedIn or YouTube to Pinterest or
Instagram, new data sources are
being added every day.
Data is multiplying at an exponential rate
day-by-day, week-by-week, year-by-year.
Everything that we do leaves a data trail.
Our every action is recorded even if we
are not connected, Your mobile leaves a
trail of your location, your credit or debit
card outlines your spending habits and
the products and services you like and
use outlines how, where and when you
buy things.
In November 2012, Analyst Josh Bersin, of
Bersin Deloitte wrote:
“Start with the problem, not the data. We
are all flooded with data: employee data,
location data, social data, compensation
data, and much more. If you start an
analytics project by collecting all the data
you can find, you may never come to an
end. Rather you have to start with the
problem: What big decisions would you
like to be able to make? What problems
would you like to solve?
One common talent problem, for
example, may be sales productivity. What
factors contribute to a predictable high-
performing sales person? Every company
would like to understand this better. And
once you understand these
characteristics, how can you better
source, attract, and hire such people?
Another may be turnover. What factors
contribute to high turnover in your
company and in particular groups?
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B I G D A T A
These questions are worth millions of
dollars to answer. Be careful you don’t
start by only looking at data. It leads to
lots of money spent, systems built, and
often little or no return.”
Source: http://www.bersin.com/blog/post.
aspx?id=574b5527-ce55-4ec6-8c45-c8f05f85162e
The point Bersin is making is an
important one. Big data is, well, big. There
is such a range of data sources that is
possible to just keep mining and never
extract any real value. Big data projects
should begin with objectives and a
desired outcome. In the case of recruiting,
we want to achieve certain clear
objectives:
1. We want to understand what ‘good’
looks like. Who are our best
performers? Who are the best
performers in the industry? What did
their background look like? What data
trends connect them?
2. When we have identified the best
candidate profile, we want to know
who fits the profile internally, or if
internal mobility is actually the best
option. The CareerXroads Source of
Hire Survey for 2013 indicates that the
principle source of hire in the USA is
internal hiring, with 42% of job
openings in the companies surveyed
filled by internal candidates:
“77,200 positions out of 185,450
positions were filled in the US by the
responding firms through internal
movement and promotion. This is
~42% of all the openings filled and
reminds us that the largest source of
hire by far is our own employees”.
Source: http://www.careerxroads.com/news/
SourcesOfHire2013.pdf
3. Next we need to identify who fits the
profile externally and how we are
connected to them? The best
candidate may have already applied
and could be hidden in the ATS, talent
network or connected with the
company in some other way, for
example, as a fan of the company
page on Facebook or a follower on
LinkedIn. Alternatively, they may well
be connected via social networks or
e-mail with our existing employees.
4. When we have targets, we want to
understand the best way to reach
them so that we can personalize the
message whilst determining the best
method of delivery and the best time
to get a response.
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5. We want to understand our
recruitment process so that we can
identify any blockage in order to
reduce time and cost of hire.
There may be many other objectives that
can be solved by taking a big data
approach to hiring. The key point Bersin
is making is to begin with the end in
mind. Understand the problem you are
trying to solve and then apply data
mining, collection and analytics to find a
solution, or at least to understand why
the problem exists.
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B I G D A T A
THE GOOGLE FLU TRENDS
It might seem strange that we are
discussing this particular initiative in
relation to recruiting, but there are some
clear lessons to be learnt from this that
apply to talent acquisition and big data.
Every time you search Google, you leave
a trail as to the information you want now
and meaning can be derived from this, as
well as an understanding of the sources
of information you trust.
A controlled experiment between 2009
and 2010 validated this using search data
to forecast hospital visits for influenza
over a 21 month period. By monitoring
millions of users’ health tracking
behaviors online, the large numbers of
Google search queries gathered were
analyzed to reveal whether there was the
presence of flu-like illness in a population.
Google Flu Trends compared these
findings to a historic baseline level of
influenza activity for its corresponding
region and then reported the activity
level as minimal, low, moderate, high, or
intense. These estimates have been
generally consistent with conventional
surveillance data collected by health
agencies, both nationally and regionally.
“ A study in Clinical Infectious Diseases
shows that a Google tool can predict
surges in hospital flu visits more than a
week before CDC. For the study, Johns
Hopkins School of Medicine researchers
compared Baltimore-specific data from
the Google Flu Trends website, which
estimates influenza outbreaks based on
online searches for flu information, to ED
crowding and laboratory statistics from
Johns Hopkins Hospital. Using Google Flu
Trends, researchers found that the
number of online searches for flu
information increased at the same time
that the hospital’s pediatric ED
experienced a rise in cases of children
with flu-like symptoms. The Google Flu
Trends data had a moderate correlation
with patient volume in the adult ED.
Moreover, Google Flu Trends signaled an
uptick in flu cases seven to 10 days earlier
than CDC’s U.S. Influenza Sentinel
Provider Surveillance Network. Based on
the findings, the researchers suggested
that platforms like Google Flu Trends
could help hospital administrators
anticipate flu outbreaks and make
appropriate staffing and capacity
planning decisions.”
Source: http://www.advisory.com/daily-
briefing/2012/01/13/google-flu
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Whilst this study was concluded four
years ago, the tracking has continued via
the Google Flu Trends report, and much
has been learnt about using search data
to forecast outcomes in a meaningful
way. There has been some variance to the
forecast from Google Flu Trends over the
past year which presents another
challenge when using data to predict
outcomes: that of context and meaning in
unstructured data. This article from the
Harvard Business Review outlines the real
challenge of taking data based forecasts
as verbatim:
“AT THE CORE OF THE ISSUE WITH FLU
MEASUREMENT (AND MOST PROJECTS
INVOLVING LARGE AMOUNTS OF DATA)
IS AMBIGUITY; BOTH IN THE INTENT OF
A SEARCH QUERY, AND IN THE SENSE
THAT THE REFERENCE RATE FROM THE
CDC MEASURES INFLUENZA-LIKE
ILLNESSES, WHICH MIGHT INCLUDE
NON-FLU AILMENTS THAT CAUSE
FEVER, COUGH, OR SORE THROAT.
SEARCH TERMS DIRECTLY RELATING
TO A FLU SYMPTOM OR COMPLICATION
ARE CONFLATED BETWEEN PEOPLE
WHO ACTUALLY HAVE THE FLU AND
THOSE THAT ARE EXPRESSING
CONCERNED AWARENESS ABOUT IT —
AND CDC MEASUREMENTS MINGLE
PEOPLE WHO ACTUALLY HAVE THE FLU
AND THOSE THAT ARE JUST
EXPRESSING SOME FLU-LIKE
SYMPTOMS. TRYING TO DETERMINE
THE ACTUAL FLU INCIDENCE REQUIRES
SOME CAREFUL DISAMBIGUATION. THIS
IS ONE PLACE WHERE SMARTER
ALGORITHMS MAY COME INTO DATA
VIGILANTISM: PULLING OUT THE
INFORMATION THAT YOU ACTUALLY
WANT TO MEASURE FROM YOUR BIG,
MESSY PILE OF DATA.”
Source: http://blogs.hbr.org/2013/07/how-google-
flu-trends-is-getting-to-the-bottom/
This highlights the need to continuously
measure predicted results against actual
outcomes and to constantly measure the
variance, adjusting the algorithm
accordingly. The benefit we have in
recruiting is that we have access to plenty
of structured data in the ATS and other
HR systems to test outcomes against. By
reverse engineering the data on hires, we
can measure if the historical data trail
follows the same path. It is important to
regularly challenge our datasets and
predictions against known favorable
outcomes in order to ensure the integrity
of predictions.
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VARIETY OF DATA SOURCES
Another area to pay attention to is the
variety of data sources available,
particularly when using unstructured data
to influence strategy. The data source
needs to be representative of the
population in order to avoid bias or a lack
of diversity. Pew internet research
highlights this problem when looking at
the demographics of internet users. The
report from December 2013 identifies that
whilst Facebook is popular across a mix
of demographic groups, other channels
have developed their own unique
demographic user profiles. For example,
Pinterest holds particular appeal to
female users (women are 4 times more
likely to visit the site then men) and
LinkedIn is particularly popular among
college leavers and users from higher
income households. Twitter and
Instagram have particular appeal to
younger users, urban dwellers and non-
whites and there is a strong overlap
between Twitter and Instagram users.
Source: http://pewinternet.org/Reports/2013/
Social-Media-Update/Main-Findings/73-of-online-
adults-now-use-social-networking-sites.aspx
Data demographics can be especially
useful when applying diversity to
sourcing where a percentage of the
desired workforce population is
underrepresented by placing a greater
weighting to data from sources where the
target population is represented. We can
also ensure data integrity by adjusting
the weighting given to certain data to
allow for the data demographics. In order
to derive meaning from data we need to
understand the DNA of the sources we
are mining to allow for any bias.
In recruiting terms, we can apply this
thinking to technology by targeting the
data we want to collect, identifying the
data source to protect against bias and
applying contextual understanding. This
involves investigating meaning in the data
being analyzed. In the case of Google Flu
Trends, the variance occurred because
the reason people were turning to Google
for advice changed from those who were
concerned about symptoms they were
experiencing to people wanting to take
precautions in advance of an epidemic
because of increased publicity and news.
When we can understand data trends, we
can apply meaning to what triggers this
reaction. One of the real benefits we
have in the recruiting space is that we
can use this thinking to identify the
internet behaviors of people who are
beginning to prepare for a job change. A
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candidate in a talent network preparing
for a job change needs greater attention
in matching results against job openings,
and we know they are likely to be more
receptive to a referral message. Our
tracking has shown that on LinkedIn, for
example, a user thinking about a change
will update their profile, seek and add
new recommendations, increase their
connections, particularly with recruiters
and follow more companies. Whilst these
actions in isolation might mean very little,
when they are combined over a four week
period, they are likely to be thinking
about moving. The same thinking can be
found in other social channels, with
predictive behaviors identified by mining
the social media behaviors of candidates
as they apply in order to understand what
candidates look like in terms of their data
footprint so that people with a similar
footprint can be identified.
THE SOCIAL GRAPH
Facebook CEO Mark Zuckerberg
popularized the concept of the social
graph to describe his approach to
mapping the world’s social relationships,
in the process, unlocking untold value for
people by digitizing their social networks.
The social graph has been referred to as
“the global mapping of everybody and
how they’re related”. The term was
popularized at the Facebook F8
conference on May 24, 2007, when it was
used to explain that the Facebook
Platform, which was introduced at the
same time, would benefit from the social
graph by taking advantage of the
relationships between individuals, that
Facebook provides, to offer a richer
online experience. The definition has been
expanded to refer to a social graph of all
Internet users.
When this is applied to referral networks,
mapping the social graph of the
organization and employees highlights
the best sources for distributing targeted
messages and opportunities. The average
number of LinkedIn connections per
employee is 225, with 130 Facebook
connections. This offers targeted reach
for relevant messaging, and tracking
employee activity and response to
messaging will highlight those employees
most likely to participate in employee
referral networks. Collecting on-going
data over user behavior and outcomes
enables recruiters to identify their most
active referrers with the strongest
relationships. The strength of a
relationship can be calculated by mining
all of the channels where there is a
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connection in order to understand the
nature of the relationship. Whilst we
might identify a relationship as a
connection between two people, e.g. they
are friends on Facebook or connections
on LinkedIn, we can use other factors to
identify the depth of the relationship in
order to apply a weighting to the depth
of connection.
Interactions between users in social
channels that indicate the depth of
relationship can include the measurement
of:
• Number of connection points
• Shared connections
• Frequency of interactions e.g. @
messages on twitter, comments
and likes on Facebook, shared
pictures etc.
• Social reputation e.g. Stackoverflow
votes to answers or questions
• Shared work history
Whilst this list is by no means exhaustive,
it is easy to see how relationships
between target candidates and existing
employees can be ranked to identify who
has the closest relationship. It stands to
reason that the closer the relationship
and the more mutual respect that exists,
the greater the likelihood of eliciting a
positive response to the referral message.
A big problem that exists when sourcing
talent is over messaging. This is a
fundamental problem with many social
recruiting platforms where matching and
targeting is based on a limited data set
such as job title and location. Typically, an
individual will likely have multiple
connections in the same organization. If
each contact sends the same referral
message in the same way, the risk is over
messaging considered spam. In the same
way as the social graph can be used to
identify connections with a target profile,
so too the relationship graph can be
applied to prioritize who should be
reaching out and through which channel.
The relationship graph leads to the most
likely route for a successful outcome.
PEOPLE PROFILING AND RELEVANCE
OF MESSAGING
In the first section of this paper, we
identified the way in which structured
data from internal sources can be used to
profile the best performers in the
company, and how their skill sets,
aptitude and other factors can be used to
compile job specs and a sourcing plan.
21RolePoint Inc. © 2014 Big Data
When you know what good looks like,
you can plan how to find it again. Once
you have profiles of your best employees,
you have a data footprint of what you are
looking for, and a template to match
against, This can be used in a variety of
ways including identifying potential hires
now and in the future or identifying an
audience for your employer brand
content. Mapping the people you want to
hire against the social graph of your
current employees enables you to identify
your employees with the deepest
relationship with the highest likelihood of
being heard.
The unstructured social data enables the
profiling of targets of professional and
personal data, online behaviors, interests
etc in order to deliver a personalized
tailored message from a trusted source.
In a world of noise, tailored marketing to
an audience of one greatly improves the
probability of success, and probability is
what big data is all about.
DATA VISUALIZATION
In the age of big data, data visualization
is becoming critical to deriving meaning
from the masses of information available
to us. Visualization is the creation and
study of the visual representation of data,
meaning information that has been
abstracted in some schematic form,
including attributes or variables for the
units of information. The vast majority of
us are not data experts or analysts, and
that’s why we use tools to do the work.
We find it hard to understand numbers in
columns and rows, but we understand
pictures, graphs, maps and symbols.
Whilst we may not be able to spot a
possible problem hidden in a row of data,
we can easily locate a possible problem if
it is indicated by a red flag. Whilst we
may find it difficult to identify the extent
of a skills shortage in a given location by
studying academic data and comparing it
with job openings, if you overlay the
same data on a map to create a heat map
of talent against openings, you can
immediately spot where there is a skills
shortage or over supply.
Airline caterers Gate Gourmet were able
to apply this technique in San Francisco
to change their whole recruiting strategy.
Recruiting unskilled labor in Silicon Valley
is no mean feat; historically Gate Gourmet
had targeted all the towns surrounding
the city, took a truck out and announced
job openings. They did this for a number
of years, and were always
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understaffed. The big problem as they
saw it was a combination of their
location, the availability of unskilled labor
and the fact that being airside and a low
margin business, they paid $2 below
minimum wage.
Seeking a solution to an on-going
problem, they overlaid where they had
experienced success in hiring with a map
of the local area. This told an interesting
story that had previously been hidden
from them: their hires came from 3 towns.
Changing strategy, rather than try to
blanket cover the whole state, they
concentrated all of their efforts on the 3
towns identified on the data map with
increased activity and investment. The
net result just 12 months later is that they
now have a waiting list for opportunities
at the airport. The decision to change
direction was made simply by making the
obvious visible in a format everyone
understood.
A multi-national company was able to
reduce their time to hire from 95+ days to
16 by mapping out their end-to-end
process from the raising of a requisition
to a hire starting. They then measured the
time taken at each stage and overlaid this
data on the end-to-end process map. This
highlighted where blockages were
occurring, allowing them to make
improvements. The process map made
every stage visible, and the addition of
data revealed where significant
improvements could be made. It is
common in organizations that poor
practice continues year on year because
it is the way things have always been
done or just the way things are, and
making data visual has the potential to
change that. Data needs to be presented
in a format that is easy to digest without
expert knowledge in order to bring about
best practice.
SELF-SERVICE TOOLS ON DEMAND
More users are expecting self-service
access to data, without the need to call
on data or computer experts. This means
that the users need to be able to set their
own parameters for data interrogation
and access results in a format they can
understand. A good example of this
would be giving recruiters control to set
their own search criteria, eliminating, for
example, employees from a certain
company. This gives the recruiter control
of the data at their disposal without
being dependent on a machine dictated
algorithm. As much control as possible
needs to be in the hands of the user, and
23RolePoint Inc. © 2014 Big Data
this instantly results in a format they can
understand, because making data
accessible and understandable improves
decision making, operates in real time
from the latest data when it is needed
most.
MACHINE LEARNING
Machine learning concerns the
construction and study of systems that
can learn from data. For example, a
machine learning system could be trained
on e-mail messages to learn to distinguish
between spam and non-spam messages.
After learning, it can then be used to
classify new e-mail messages into spam
and non-spam folders.
The core of machine learning deals with
representation and generalization.
Representation of data instances and
functions evaluated on these instances
are part of all machine learning systems.
Generalization is the property that the
system will perform well on unseen data
instances; the conditions under which this
can be guaranteed are a key object of
study in the subfield of computational
learning theory.
There are a wide variety of machine
learning tasks and successful applications.
Optical character recognition, in which
printed characters are recognized
automatically based on previous
examples, is a classic example of machine
learning. Source: http://en.wikipedia.org/wiki/
Machine_learning
When it comes to big data, machine
learning is a branch of artificial
intelligence (AI), the practice of getting
computers to think like people. Analytics
of user interpretation of data enables
technology to make decisions and rank
results based on the past reaction of
users. A good example of this might be a
recruiter who rejects the result of a
search or removes similar resumes from
those under consideration. Machine
learning interprets these actions in order
to change the way future results are
arrived at and determined in the future.
Google uses machine learning to order
search results by past actions, ranking
search results by items the user has
shown trust in previously, like blog posts
from a particular writer or academic text.
This works on the principle that the more
the user interacts with the technology,
the better the technology gets at
understanding the user in the same way a
person would. Interaction is rewarded by
a continually offering a better user
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B I G D A T A
experience. Machine learning is applied to
big data for recruiting in the same way.
Modern recruiting technology facilitates
this methodology, bringing the way the
technology thinks closer to the elusive
artificial intelligence.
SUMMARY
In September 2011, super sourcer Glen
Cathey, the SVP of Talent Strategy and
Innovation at recruiters KForce, published
a post, entitled “Moneyball Recruiting”, on
his excellent resource, the Boolean Black
Belt blog. In this post Cathey drew on the
story of Billy Bean, who used a computer
to select baseball team Oakland A’s. The
story of the success this team achieved is
well documented elsewhere, but Cathey
expressed the opinion that such
methodology could be applied to change
recruiting and human capital
management.
Cathey concluded with the statement:
“I AGREE WHOLEHEARTEDLY WITH
MIKE LOUKIDES THAT: “THE FUTURE
BELONGS TO THE COMPANIES WHO
FIGURE OUT HOW TO COLLECT AND
USE DATA SUCCESSFULLY.” HOWEVER,
I’D ADD THAT THE FUTURE BELONGS
MORE ACCURATELY TO THE
COMPANIES WHO FIGURE OUT HOW TO
COLLECT AND USE HUMAN CAPITAL
DATA SUCCESSFULLY. THAT’S BECAUSE
THE COMPANIES THAT CAN
CONSISTENTLY HIRE GREAT PEOPLE,
THROUGH IDENTIFYING PEOPLE AND
BASING HIRING DECISIONS ON DATA
AND NOT INTUITION AND
CONVENTIONAL WISDOM, ARE MORE
LIKELY TO DEVELOP THE BEST TEAMS.
AND THE BEST TEAMS WIN.”
See more at: http://booleanblackbelt.com/2011/09/
big-data-data-science-and-moneyball-
recruiting/#sthash.rcfD4Xgh.dpuf
When Cathey wrote this post, we were
still speculating about the potential
offered up by the information age. Other
areas of business like marketing and sales
were showing themselves as the early
adopters of data mining and analytics to
derive business value, and a lot of what is
being developed in the recruiting space
can be attributed to the lessons learnt at
this time, lessons we are now applying to
talent acquisition and hiring.
25RolePoint Inc. © 2014 Big Data
Josh Bersin, of Bersin Deloitte, has created a model to outline the spread of big data analytics
across business functions on the journey towards HR and Recruiting:
The case now in 2014 is overwhelming. We can utilize the vast volume of structured and
unstructured data in all areas of recruiting, talent acquisition, workforce planning and hiring. We
can expect big data to play an increasingly important role in identifying, pipelining and hiring the
best talent in the most effective way, and we have only just started.
ANALYTICS IS DEFINITELY COMING TO HRThe Evolution of Business Analytics in other Functions
The Waves of Business Analytics
Logistics & Supply
Chain Analytics
1980s Financial
& Budget Analytics
Integrated
Supply Chain
Integrated ERP and
Financial Analytics
Customer Analytics -
CRM (Data Warehouse)
Customer Segmentation
Shopping Basket
Web Behavior
Analytics
Predictive Customer
Behavior - CRM
Recruiting, Learning,
Performance
Measurement
Integrated
Talent Management
Workforce Planning
Business-Driven Talent
Analytics
Predictive Talent
Models HR Analytics
The Industrial Economy
The Financial Economy
The Customer Economy and Web
The Talent Economy
Steal, Oil, RailroadsConglomerates, Financial,
Engineering
Customer Segmentation
Personalized Products
Globalization,
Demographics, Skills and
Leadership Shortages
Early 1900s 1950s-60s 1970s-80s Today
26RolePoint Inc. © 2014 Big Data
B I G D A T A
N E X T S T E P S
W W W . R O L E P O I N T . C O M
I N Q U I R I E S @ R O L E P O I N T . C O M
Nasdaq clients, building the principles that
help companies generate 70%+ referral
rates into a software-as-a-service platform.
Understanding that at the core of any
successful referral program is the
employee, RolePoint focuses on providing
an engaging, transparent and frictionless
experience, making it easy to identify
talented connections to refer.
For recruitment teams, RolePoint offers a
comprehensive set of tools, enabling
tracking, automation and recruitment
intelligence for greater control and insight
into referrals within your organization.
B I L L B O O R M A N
The author, Bill Boorman, has over 30
years’ experience in and around recruiting.
He has spent the last 3 years working with
social recruiting technology start-ups on
product and with corporate clients
including Hard Rock Café, Oracle and the
BBC to integrate social into their recruiting
practices. Bill has also hosted recruiting
events in over 30 countries worldwide.
R O L E P O I N T
RolePoint delivers employee referral
solutions to a range of Fortune 500 and
C O N TAC T U S TO S C H E D U L E A F R E E E M P LOY E E R E F E R R A L
C O N S U LTAT I O N W I T H B I L L B O O R M A N
C O N TAC T U S TO F I N D O U T M O R E A B O U T RO L E P O I N T A N D A R R A N G E
A D E M O N S T R AT I O N
27RolePoint Inc. © 2014 Big Data
R O L E P O I N T
THE MOST POWERFUL
SOURCING SOLUTION AT
DISCOVERING TALENTED
CANDIDATES WITHIN
YOUR EMPLOYEES’
PROFESSIONAL
NETWORKS
HIGHER QUALITY CANDIDATES
REDUCED TIME-TO-HIRE
LOWER COST-PER-HIRE
IMPROVED EMPLOYER BRAND
29RolePoint Inc. © 2014 Big Data
W W W . R O L E P O I N T . C O M
I N Q U I R I E S @ R O L E P O I N T . C O M