Post on 15-Apr-2017
transcript
© 2016, Future of Talent Institute
Kevin WheelerTalent Acquisition Tech Conference
Austin | November 15-16, 2016
The Promise & Perils of Predictive Analytics
1
© 2016, Future of Talent Institute
Challenge 1: Ambiguity
Recruiters are asked to find people with skills we’re never heard of, in unrealistic timeframes, in remote places.
© 2016, Future of Talent Institute
Challenge 2: Complexity
We need to know how to find the signal in the noise. Desperatly these days!
© 2016, Future of Talent Institute
Challenge 3: Friction
Our processes do not flow. Required information is often not easily accessible. There are choke points and ambiguities everywhere.
© 2016, Future of Talent Institute
Challenge 4: Interaction
Communication is unclear. We need to know what to say. What is effective?
© 2016, Future of Talent Institute
Challenge 5: Innovation
6
Old thinking and bureaucratic processes stifle innovation and change.
© 2016, Future of Talent Institute
The Promise
7
MORE EFFECTIVE COMMUNICATION
FASTER PROCESSES
GREATER CANDIDATE
UNDERSTANDING
HIGHER QUALITY
HIRES
INCREASED INTERACTION
ENAGING CANDIDATE
SERVICE
PEOPLE ANALYTICS
© 2016, Future of Talent Institute
Predictive Analytics Promises Answers
8
• Who are our top performers?
• When & how should we connect with them?
• What attracts them to our firm?
• Which assessment is more accurate?
• Which hires will be the most productive?
• What would increase our quality of hire?
• Which interview questions are most effective?
• What will our turnover rate be in the next quarter?
© 2016, Future of Talent Institute
Descriptive & Predictive Analytics Compared
9
Descriptive Analytics Predictive Analytics
PurposeUnderstand the Past
Observe TrendsDiscuss
Gain InsightsMake Decisions
Take Action
Timeframe Past and Current Future
Metrics Type Lagging Leading
Data Used Raw/Tabulated Information
Data Type Structured Structured and Unstructured
BenefitsUnderstanding
EfficiencyInformation & Insights
Effectiveness
© 2016, Future of Talent Institute
Predictive analytics uses algorithms, machine learning, statistical analysis, sentiment analysis, semantic analysis, and other complex methods to provide insight.
But there are challenges and many things to lead you astray. . .
10
© 2016, Future of Talent Institute
Disruptive Technologies
11
Internet
Social
Mobile
Cloud
Big Data - Analytics
Technology Foundation
Trends & Innovations
Internet of Things
Robotics
Disruptive Scenarios
Passive candidate assessment
Algorithms Automate Recruiting
Intelligent Personal AgentUltrasonic Tracking
Predictive Analytics DNA Analysis/Assessment
Virtual/Augmented Reality
ChatbotsBiometric Assessment
Life Span
Blockchain
Supply Chains
Contingent Workers
Climate Change
Decline of Nation State
Urbanization
Emerging Economies
© 2016, Future of Talent Institute
Data Does Not Tell a Story
12
Data by itself takes no position and holds no bias.
Biases & other issues only occur when we interpret it, look for predictions, use it to make decisions,.
© 2016, Future of Talent Institute
The Many Perils, Traps and Biases
13
AssumptionsPredictions based on proxiesNot questioning The future = the past
PrivacyBlack boxesUsing data without candidates knowledgeLack of guidelines
BasicsNo clear problem statementPredicting what has no impact
StructuralLack of clean dataSample size too smallToo simplistic/too complex models
BiasesUnintentional/InherentFavoring one set of data over another
© 2016, Future of Talent Institute
What do you want to predict?What is the problem you want to solve?Do you have the right data?Do we have enough data?Do you have enough relevant data?How do we prevent diversity issues?What is a quality hire?How do we define effective?What’s in the algorithm?
Weapons of Conformity & Discrimination?
14
Only 14% of organizations have data to prove the positive business impact of their assessment strategy. -Aberdeen 2014
© 2016, Future of Talent Institute
AssumptionsWhat are we assuming as we search?How valid are our search criteria? How do you know?
Scoring algorithmsWhat is in the algorithm?How do we know it is actually measuring what we think it is?Is it discriminating?Is it fostering clones?How do we introduce diverse thinking?
Sourcing
15
© 2016, Future of Talent Institute
Is a Facebook/LinkedIn profile accurate?Can they predict ability, skill, or job performance?
Using Facebook or LinkedIn
16
“. . .researchers hired HR types to rate hundreds of college students’ Facebook pages according to how employable they seemed.
. . .the over 500 guinea pigs, just 56 of the employers responded. So the sample is small, but the researchers found a strong correlation between those employers’ reviews and the employability predictions they had made based on folks’ profile pages.”
© 2016, Future of Talent Institute
Check out a candidate’s social media profiles – even informallyUse any of the social media info to influence your opinion of a candidateDo not let a candidate know if you lookedDo not let them know what you looked for
Social Media & Privacy
17
Potential legal problems if you. . .
© 2016, Future of Talent Institute
What are the criteria?Does personality testing correlate with performance?Do we want everyone the same?
Assessment
18
The personality test is a black box, and it’s not clear what it is actually assessing, and whether using it constitutes discriminatory hiring practices.
© 2016, Future of Talent Institute
What are the criteria?What is the context?When and to who was the comment made?
Sentiment Analysis
19
“Even in the best of circumstances, [sentiment analysis] is only 65% to 70% accurate.”
-Susan Etlinger, analyst Altimeter Group
© 2016, Future of Talent Institute
Matching algorithmsAccuracy? Relevance? Privacy? Discrimination?
Passive AssessmentPrivacy? Relevance? Accuracy?
ChatbotsAssumptions? Discrimination?
Online Assessments/gamesPrivacy? Relevance? Correlation=Causation?
A Few Emerging Tools
20
How transparent are the vendors about their algorithms and assumptions?
© 2016, Future of Talent Institute
Could We Automate Selection & Assessment? Should We?
21
People are complex, contradictory, and so varied that even complex tools and algorithms are rarely accurate. We will continue to need human judgement and tolerance.
© 2016, Future of Talent Institute
Most of the tools we are using today create as many questions as answers.No single test can predict anything with high certainty.Many tools use proxies, which may not represent reality.We tend to validate a tool when it confirms our suspicions.Sample sizes are often way too tiny to be valid.There is inherent bias in almost all screening.People are highly complex and there are no simple ways to say one person is better than another.
Unfortunately. . .
22
© 2016, Future of Talent Institute
PA can provide insight and validate or disprove assumptions.Can augment human judgement.Valuable when used responsibly following openness guidelines. Can provide early warning that employees are unhappy or are thinking about leaving.Can identify competencies and skills and predict their value to a particular role.
Predictive Analytics
23
© 2016, Future of Talent Institute
Have a privacy disclosure policy that is shared with candidates and is on your career site.Let candidates know why you have rejected them, especially if based on social media information.Have clear definitions of what you are looking for and how you will know when you find it.Train recruiters in what is acceptable, reliable, and accurate.Always use human judgement along with any AI or other assessment or prediction.
Good Predictive Analytics Practices
24
© 2016, Future of Talent Institute
. . .use more than one test or predictive tool.
. . .when choosing a tool, know what is in the algorithm. Know what is being looked for, tested, analyzed, and how each factor is weighted.. . .make sure proxies are valid and really predictive.. . .not adopt tools hastily and without careful thought
and knowledge.. . .always question our own assumptions and beliefs.
We should. . .
25
© 2016, Future of Talent Institute26
THANKS
YOUR THOUGHTS & QUESTIONS
Follow me on Twitter: @kwheelerEmail: kwheeler@futureoftalent.org