Sourcing talent a key recruiting differentiator part 2 - the (Big) Data Landscape

Recruitment Workshop
Sourcing
in a global talent landscape
Part 2 A
(Big) Data Landscape Overview
1
Alexander Crépin
recruitment strategist, coach & trainer
freelance, interim recruiting & management
Alexander Crépin AC@Recruitmentcoach.nl

Recruitment Workshop
Sourcing in a global talent landscape
Part 1: What & How (A STEP process)
Part 2 A: Data Landscape Overview
Part 2 B: Sourcing Craftsmanship
Part 3: Sourcing Tools, Methods & Tech
2

AttractStrategize
SAAA Corporate Recruitment Model
Analyse Search Trace Engage Present
AppointAssess
Talent
Acquisition
A STEP = Sourcing Talent
Outbound
Job Branding
3
Inbound

Sourcing Pillars of Success
• Data / Digital Technology & Tools
• Craftsmanship of recruitment team
• Lean Processes, to make and/or buy
Sourcing Talent

Sourcing in the Digital Age
Data & Tech
Social media, Mobile, Big Data & Cloud
• Bits & bytes, data, data& data
• Informed,Smart Data Driven Business,
powered by algorithms & tools
• Search Tech&MatchTech& Data Miningmajor
differentiators!
• Data Driven Recruiting/ Sourcing/ Hiring emerging
Sourcing Talent

Data & Analytics Revolution
Sourcing Talent
6

Real Revolution (Big Data & Analytics)
• Term Big Data didn’t exist 25 years ago!!
• In 2000 digital world still in its infancy
• Access to meaningful data exceptionally difficult
• Now billions social data real time accessible!!
• Data volume growing exponentially (> 40% p/y)
Big Data becoming bigger!
Unstructured data growth curve substantially greater than of structured data
Sourcing Talent

(Big) Data, the new oil !!
• Data crucial fuel for business to grow & thrive
• The new commodity to be refined (analyzed)
& then “business wise” exploited!
Sourcing Talent

Big Data Ownership
Data ‘Wars’ likely, over access usage rights, over
data & monopolistic behaviour from companies
seeking to restrict ability of consumers & other
businesses to use its free flow
Paul Weiskopf, SVP at Domo
Sourcing Talent

Big Data, Big Win?
• Most raw data, especially big data, not
suitable for human consumption!!!
• Information derived from the data is!
Dr. Michael Wu chief scientist Lithium Technologies
Sourcing Talent

Big Data for Smart Data for
• Improved sourcing/ recruiting performance
by
• Using data, taking advantage of data analysis for
informed decision making
• No more dominance of subjective hiring ideas
and gut instinct!
Sourcing Talent

From Big Data to Smart Data
Translating raw data into actionable data
requires “some” (basic) understanding of:
• exploratory data analysis
• data mining methods
• & appreciation of subject matter business processes
• software development
• project & change management methods for implementation
/ applying insights
• and so on ……
Sourcing Talent

Algorithm Revolution?
• Algorithms, automated methods, must-have in
data driven world
• Lists of well-defined instructions or a step-by-
step procedure, precisely encoded as a set of rules,
to find info related to a problem
• General form of a computer algorithm:
INPUT → PROCESSING → OUTPUT
NB: Mathematical Algorithms not easy to explain but fun to work with!
Sourcing Talent

Algorithm Revolution?
• Algorithms getting smarter by Machine
Learning Methods
• (Mathematical) Algorithms not easy to explain
but fun to work with!
Sourcing Talent

Big Data Tech & Sourcing
• Just at the beginning
• Search & match intelligence gathering
• not (yet?) as simple as just typing
• a few keywords into Yahoo or Google!!
Sourcing Talent

Big Data?
Term describing large volumes of high velocity,
complex and variable data
Requiring advanced techniques & technologies to
enable the capture, storage, distribution,
management & analysis of the information
- TechAmerica Foundation's Federal Big Data Commission
Sourcing Talent

Unstructured data (≥80%)
• Raw unorganized, multi-structured, stored
data at many places
• Like text & multimedia content
e-mail messages, word processing documents, videos, photos,
audio files, presentations, webpages …….
• Incredible amount of bytes!
• Big Data = Really Big!
• Big requiring innovative solutions
Sourcing Talent

Data Surface, Deep & Dark web
Sourcing Talent
Source: Gurprit Bhambra

Unstructured data challenge
Making quantitative data shift a qualitative shift as well.
Sourcing Talent

Sourcing Talent

Unstructured data challenge
• Information overload Why & how to manage?
• Garbage In, Garbage Out! (less = more?!)
• Data Purpose? Knowing why to capture & use data?
‒ Do some, most or all data have expiration date?
‒ “Store everything” approach no longer valid!
‒ Define minimum key data points to start targeting right talent
• Costs (storage, analytics) & Governance issues vs benefits
NB. Tech solutions also advancing , but still a way to go
Sourcing Talent

Structured data ( ≤ 20%)
• Data in searchable databases (ATS, HRIS, Linkedin) !
• Meaning of (each) data item already defined
• Important: setting clear standards & definitions with regards
to good, relevant, clean data (garbage in – garbage out!)
• Tech & Tools: Search algorithms & data aggregation
& data analytics
• Less complex compared to unstructured data
Sourcing Talent

Quality & Comparability of Data (input)
Data structured & unstructured
Sourcing Talent

Big Data and Analytics Manifesto
Sourcing Talent
Source: IBM

From Big Data to Smart, (actionable) Data
Using data tech for deeper, actionable insights
into customers, partners & businesses …………. Target Talent?!
Sourcing Talent

Real challenge = not collecting data!
• It’s understanding importance (or lack of) data points
• It’s identifying the right data points
• It’s getting access to the data
• It’s filtering data
• It’s data mining
• It’s using smart tech & tools
• it’s in interpreting, understanding output
• It’s developing information, knowledge & wisdom
Sourcing Talent

DIKW pyramid of increasing insight
• Data
• Information
• Knowledge
• Wisdom
Fundamental problem:
People don’t know what they don’t know!!
How to look for things we don’t know to look for?
Sourcing Talent

From Big Data to Insight
Sourcing Talent
Data Source 1
Data Source 2
Data Source 3
Structured &
Unstructured
Data
Data Mining
DM output
Interpretation
Insight

Data Mining (DM)
• “Insight discovery” in large databases from
(semi) (un)structured data
• Developing & applying algorithm(s)
• DM tools quickly explore mountains of data, cull
& aggregate info to uncover patterns & correlations,
some never thought of!
• Data miners usually use open source tools
Sourcing Talent

Data Mining = multi-disciplinary
Not a new discipline, but intersection of / combining
• Information / Data / Web Retrieval
• Statistics
• Machine Learning
• Artificial Intelligence
• Data Base system management
• Distributed Computing
• Information Visualisation
Sourcing Talent

Data Scientist = T-shaped Skillset
Deep expertise in 1 area AND breadth of generalized experience
Generalist
&
Specialist
Sourcing Talent
Breath of ExpertiseIn-depth
Expertise

Data Mining & Machine Learning Methods
• MLM = “self learning”, ability to adapt the
program by learning from previous “results”
• MLM uses data to improve program's own understanding & to
“improve” when exposed to new data
Result:
• Algorithms getting smarter & smarter
• to quickly & automatically produce models to analyse bigger,
more complex data & deliver faster, more accurate results
Sourcing Talent

Learning by doing!
• “self learning”, from previous “results”
• I learned through being curious & experimenting
• Learning by doing, and more specifically by failing /
struggling, and not by copying & pasting somebody else’s
work
Glen Cathey Sourcing Leader
Sourcing Talent

Big Data …… is just data!!!
• We have more of it
• We can already do more with it!
• By new technologies for:
• Analysis of large, complex & rapidly changing
data sets comprised of structured, semi-structured or
unstructured data
Sourcing Talent

Big Data new tech, tools & services
http://dfkoz.com/big-data-landscape/
Sourcing Talent

Sourcing Data Driven
Talent Data Sourcing
Big Data &
The Digital Footprint
All online interactions uncovering
who we are, what we do & how
we feel
Sourcing Talent

Your questions ?

Tracking People & Big Data
• Rise of tools tracking consumers like:
• super cookies ● browser fingerprinting
• location-based identifiers ● behavioural tracking etc.
• All real-time behaviours, searches, actions & statuses
reflecting questions, emotions & experiences etc.
• Psychometric data monitored & accumulating
• Data & algorithms taking profiling to higher levels
Sourcing Talent

Big Data & Identity marketplace
• Commodification of “digital footprint”
• Trading personalised (ad) communication (real time)
• Digital Identity ecosystems in Big Data context
emerging
• Global digital oligopolies (Google, Facebook etc.)
Sourcing Talent

Unifying (Big) Data & Digital Footprint
NB Intrusive cookies: Nobody would tolerate this sort of privacy
invading behaviour in real life
Sourcing Talent

Consumer profile
Management
for
Consumer
Intelligence
by
Digital Footprint
Data unification
Sourcing Talent

Web Crawlers & Data Extraction Services/Tech
Source: Promptcloud web-crawler-software-as-a-data-service/
Sourcing Talent

Talent (sourcing) Intelligence development
• Digital Footprint Research
• Identifying internal & external data sources
• Comparing & Combining data sources
• Unifying data for talent profiling
• Creating a “complete” picture of a talent
long before stepping foot in an office for an interview
Sourcing Talent

Behavioural profiling, soft skill sourcing
• Tech interpreting meaning & intent behind text-
based content & social behaviours
– eiTalent algorithms for identifying core values & driving
motivators using (Linkedin) resumes
– IBM Watson analyses LinkedIn profiles http://chh.mybluemix.net/analyze
NB. Linkedin = structured data
Sourcing Talent

NB. Soft (skill) data growing interest
yawning STEM skills gap
• emphasis will continue to be on
‒ attitude
‒ personality
‒ train-ability over specific skills
Sourcing Talent

Sourcing Tech utopianism:
The Ultimate sourcing solution ……
Provides valid data points enabling informed
decision making when searching & hiring as
good as possible new employees
Sourcing Talent

Ultimate (future) sourcing solution
• parsing (globally) resumes & profiles
• into highly structured data
• that could be searched
• via semantic search (autopilot)
• & extended Boolean (manual control)
• to ensure anyone could quickly find right people
under any circumstance
- Glen Cathey, Sourcing Leader
Sourcing Talent

Ideal Search Technology
• Algorithms understanding human, conversational
language & grasp meaning of words & sentences
• Semantic Searching Tech/Tool taking in consideration
‒ user intent / purpose / digital footprint
‒ how subject of search relates to other information
in a wider sense, or its contextual relevance
• rather than smartly executing a string of keywords
Sourcing Talent

Ideal Search Tech, pitfall
• Suggests subject area expertise on almost every
search, by automatically finding & using most relevant
search terms
• Less need for in depth analysis of profile & keyword filters
• Danger of eliminating need to understand what actually
to be searching for!
• Danger of “old fashioned” low quality Resume Supply Sourcing
Sourcing Talent

Ideal Matching Technology 1.
• Algorithms, for digital footprint data identification &
integration, combining multiple profile data
• Successful internal hiring data, as reference
• Identify internal & external success factors for many job types
• algorithm identifying patterns, (cultural & personal)
factors affect successful hiring decisions (which recruiter, hiring
manager, source, assessment type/outcome, onboarding etc.)
• reducing the impact of a hiring manager’s bias
• diversity needs attention in algorithm development
Sourcing Talent

• Data Driven, Not resume / CV driven
• ……. Résumés will be displaced by constantly
evolving representations of individual experiences,
skills & aptitudes existing purely in the digital realm
• ……. Innovative tools will be used to match &
screen, using many sources & give valuable insight
into individual job seekers …….
Bob Myhal, director CBC Advertising
Sourcing Talent

• No longer depend on traditional education &
employers data as mentioned in resume
• As more data become available …..
• Better, more exact criteria will take a place &
• Smart, driven people will then have more career
opportunities ….. than ever before ……..
Sourcing Talent

Future driving forces 1.
• Search engines understanding regular “talk”,
conversational language
• Understanding intent behind words of search query
• Many Companies & Scientists developing tools
• Facebook,artificial intelligence researchers showed a system reading a
summary of The Lord of The Rings & answer questions about the books!
• Microsoft can translate your speech into another language
• IBM Watson already offering to search semantically for answers.
Sourcing Talent

• Machine Learning Tech to auto-improve / auto
refine search & matching outcomes
• Open source tech, using power of crowd to refine,
improve tech & tools
• Algorithms getting better,“smarter & smarter”
Sourcing Talent

• Consumer Profiling, Marketing bigger market &
leading (more money for tech development )
• Military Intelligence, Profiling investments
• Recruitment War for Talent global market for global
players interesting money maker
• Smart Data = Big Money Monetizing data by selling or
providing value-added content
Sourcing Talent

“Robot” sourcers?
• …… As data growth continues to skyrocket, there
will eventually be so much of it that data analysts
won’t be able to keep pace.
• At that point, we’ll have to hand over at least some
decision-making to the systems that are more
knowledgeable than we can ever hope to be ……….
Forbes
Sourcing Talent

Why using algorithms? 1.
• The more data, the more difficult to analyse &
draw meaning from data
• Too much data (not yet being used!)
• Saving Time, algorithms can do in seconds
what can take a human hours, days, or even weeks
Sourcing Talent

Why using algorithms? 2.
• Humans are biased decision makers!
• More Accuracy, fact/data driven decisions
usually better than using gut-feel!
• Taking the guesswork out of recruiting,
no longer fully rely on biased opinions, instinct & intuition
NB
• data’s supposed objectivity can mask other biases built into
algorithms …… nobody is perfect & algorithms aren’t either
Sourcing Talent

Summary:
Fundamental changes in technology & how data
& information being used in sourcing/recruiting
• General trend toward leveraging big data opportunities
• Growing data-intensity of recruiting & HR processes
• Algorithm development crucial to handle all data
• Increasing expectation to offer “experience”
• Marketing tech, tools & expertise “invading” recruiting
• Profiling to match & more personal communication
Sourcing Talent

Epilogue 1.
• In many ways, we're still in the early innings
of the Big Data phenomenon …..
• Big Data & Machine Learning/Ai will drive incredible
innovation across pretty much every industry …..
also in recruiting/sourcing/hiring
• As Big Data continues to mature, however, the term
itself will probably disappear …….
Matt Turck Managing Director at FirstMark Capital
Sourcing Talent

Epilogue 2.
• Better informed decision making will drive changes!
• Are you willing & capable to embrace changes?
• Organizations have to be
‒ embracing the idea of data science offering opportunities
‒ open to accept what data are “telling” to do
‒ prepared to change/optimize methods, systems &
processes
‒ acknowledge that time to adapt is likely to be needed
Sourcing Talent
65

Big Data in 2016 & beyond
Big Data could bring success in sourcing
• if handled in moderation, step-by-(big)step
• if handled ethically, respecting privacy & data
ownership
• if investing in expertise development
• if in line with rest of organisation’s use of data
Sourcing Talent

Big Data & Analytics in 2016 & beyond
• The “algorithm economy” is coming …..
• Marketing profiling tech being used
• Tech developing fast & likely to transform (&
disrupt) recruiting in coming years
• but … Talent Sourcing not (yet?) a fully
automated process
Sourcing Talent

Big Data & Sourcing in 2016 …..
Starting Realistically
Sourcing Talent

Big Data & Sourcing in 2016 …..
Starting in searchable databases
• Resumes & Job posting archives*
• Mountains of more or less structured data
• Especially cloud-based ATS & Job Board companies &
sites like Linkedin, Facebook, Indeed, Monster, etc.
• Search & Match Tools (Algorithms) to quickly &
accurately identify profile matching talent
* NB Privacy regulation not allowing endless storing of personal data
Sourcing Talent

Challenge: How take advantage of
which Tech & Tools?
‒ Which tech & tools fit the recruiting strategy &
support the operational need & budget?
‒ Which type/level of expertise & experience
needed in recruitment teams?
‒ Cost & Benefits of investing in Tech & Tools
(development)
Sourcing Talent

Sourcing Tech – Tools – Methods
Recruitment Technology (development)
• Interesting Market to invest in
‒ Recruitment increasingly global market ….
‒ War for Talent
• (New) suppliers offering new options/solutions
• Dilemma: early adaptor, pragmatist,
conservative or a laggard?
Sourcing Talent

Before focussing on Tech Have in mind: - 1
• Cost vs benefits of applying (new) technology
• Shortage of skilled staff to execute
• Is it “real” differentiator bringing competitive
advantage if available for everyone?
• Tools & Tech can fake comfort, make belief
sourcing is easy!
Sourcing Talent

• Data security
• Privacy concerns
• Important barriers in freely collecting, using & managing
customers' personal data (data oligopolies)
• Consumer attitudes changing toward companies using their
personal information, data ownership battle
• Ensure investments in tech & tools taking this into account!
Sourcing Talent

Data as good as:
• Access to data & retrieval & filtering options
• Data relevance & being up-to-date
• Quality & comparability of data (input)
• Ability to look from different perspectives:
Analyse data from min. 2 angles! Data has power to mislead, make
sure it's telling the story accurately!
Sourcing Talent

• First clarity what exactly to look for & how?
‒ Understanding data = Understanding added value
‒ No analytics until having access, data identified as
(possibly) relevant
‒ How many data? 1, 2, 3, … data points to offer
useful & reliable output?
NB. Even incomplete data may provide insight!
Challenge how to fill data gaps
Sourcing Talent

Sourcing (HR/Talent) Data Eco-System
• Big Data is currently much talked about
• Big Data Driven Recruiting / Sourcing / Hiring
is more likely the first step to make ….
• Start developing a Data Eco-System
Sourcing Talent
X

• Vision Why & how role of data in strategy?
• Need What specific needs to be addressed
by intelligently using data?
• Outcome How & by whom will results be used &
integrated into overall goals?
• Context What are enablers & barriers?
• Budget What are Cost vs Benefits now & later
Sourcing Talent

• For actionable data set (accuracy, accessibility, flexible use)
• Set standards/specs for sourcing data (set)
• Align to corporate HR data set
• Consolidate internal & external data sources
• Minimize amount of (internal) data bases enable smart
use of HRIS / ATS / Recruiting System(s)
• Get tools to be able to work data driven
Sourcing Talent

Sourcing Data Eco-System & Tech
Sourcing Talent

• …….. Software and computers won’t replace
recruiters, because it’s unlikely you’ll develop an
algorithm that can effectively overcome objections,
represent a culture, and actually build a
relationship with candidates. Even if we get really
close, Asimov’s 3 laws of Robotics tell us that we’ll
never trust our emotions to robots, and those
emotions are what recruiting is all about …….
Matt Charney - Recruiting Daily
Sourcing Talent

• Data is only as powerful & useful as the
quality/relevance of the question to
answer
• A good question can “organize” a data set (for
use in a query of algorithm)
• “If I had an hour to solve a problem I'd spend 55 minutes thinking about
the problem and 5 minutes thinking about solutions.” Albert Einstein
Sourcing Talent

• ….. If IBM Watson can find hidden
correlations that help your business, then
why can't IBM Watson stem a 3 year
sales drop at IBM? … - Kevin Hillstrom (@minethatdata)
• So it is not yet that simple to get sophisticated analytics
software and techniques running on "big data" to run out in
front of your changing business and radically benefit it. …..
- John Foreman, Blogging data scientist
Sourcing Talent

• Data is only as powerful & useful as
• The interpreter of the output of a data set!
‒ Understanding analytics options
‒ Understanding which problem(s) to solve
‒ Knowing the right questions to ask
‒ Experience, pragmatic and/or strategic following-up
• Ultimately,the best “tool” is your brain Amybeth Quinn, sourcing blogger
Sourcing Talent

Interpretation of the output
Sourcing Talent

Data & getting the “full picture” ?
Sourcing Talent

People still (?!) main differentiator
• Actually make sense of data
• Point methods, tools & tech in the right direction
• Configure facets/filters (also out of the box!)
• Make appropriate decisions
• Take appropriate actions
• Connect, Engage, Relate be human!
• After all hiring is about people, not (only!?)
about data!
Sourcing Talent

Sourcing Pillars of Success
• Data / Digital Technology & Tools
• Craftsmanship of people in recruitment
team
Sourcing Talent

People still (?!) main differentiator
• ……. for all the efficiency that technology creates, it
can’t replace a real human handshake .…….
• Hiring isn’t an algorithm. It’s about people, talent &
culture fit
• Be able to straddle both sides of the equation, tech
& human ………..
Meghan M. Biro global HR Tech brand strategist
Sourcing Talent

Next
Sourcing
in a global talent landscape
Part 2 B
Sourcing Craftsmanship
91
Alexander Crépin
recruitment strategist, coach & trainer
freelance, interim recruiting & management

Interested in
Data Driven Sourcing ?
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Sourcing talent a key recruiting differentiator part 2 - the (Big) Data Landscape