Using Analytics to build A Big Data Workforce
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USING ANALYTICS TO BUILD A
BIG DATA WORKFORCE
Greta RobertsIIA Faculty Member
CEO Talent Analytics, Corp.©2014 Talent Analytics, Corp. | All Rights Reserved
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Modeland optimizehuman performance
TALENT ANALYTICS, CORP.
employee
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Quantitatively measures “raw talent or mindset”11 scores per personEasily outputs to a .csvCombines with any / all other performance
variables (big or little data)TA 11 variables often useful as independent
variablesAdvisor 4.0 is ideal platform for deploying
predictive models during hiring cycle (or optimizing current employees)
TALENT ANALYTICS PLATFORM ADVISOR®
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BUSINESS CHALLENGES WE SOLVE
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BUSINESS CHALLENGES WE SOLVE
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Young field
Young practitioners
Role requirements not well defined
Comparables difficult
“The sexiest job of the 21st century”1
1 Thomas Davenport, D. J. Patil, October 2012 HBR
BUSINESS CHALLENGESBUILDING ANALYTICS BENCH
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Talent Supply Research and model workingData Scientists
2 APPROACHES
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Over-specified
Generic
Competing requirements
Result: Impossible to fill
ROLE REQUIREMENTS
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We hire externally
Internal candidates don’t have the right skills
CONTRADICTIONS
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Biggest mistake you can make is hiring for technical skills
CONTRADICTIONS
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WHICH “SET” IS MOST IMPORTANT?
Mindset
Skillset
Dataset
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WHICH “SET” IS MOST IMPORTANT?
Mindset
Skillset
Dataset
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WHICH “SET” IS MOST IMPORTANT?
Mindset
Skillset
Dataset
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NOW THE SCIENCE
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Talent Analytics, Corp.
International Institute for Analytics
STUDY TEAM
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Quantitative approach to defining raw talent in analytics professionals
“Raw Talent” (mindset) vs. Achievements (skillset)
Practical outcomes vs. purely academic
STUDY SUMMARYUNIQUE ELEMENTS
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Global Sample: 304 “deep dive”
Data Scientists / Analytics Professionals
Data gathered online via questionnaire
Sources: Analytics Media, PAWCON,
Meetup, LinkedIn Groups, IIA Members
Google Spreadsheet/Forms + Talent
Analytics Advisor™
METHODOLOGY
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Primary Analysis Tool: R
Three Methods:Regression MethodsFuzzy ClusteringTree Modeling
DATA ANALYSIS
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ANALYTICS PROFESSIONALS
DESCRIPTIVE STATISTICS
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AGE
57% under 40
17% over 50
GENDER
72% male
Gender trend similar across all age groups
AGE AND GENDER
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47% have Masters
36% have Bachelors Degree or Less
16% have PhDs
HIGHEST EDUCATIONAL DEGREE
BSBA
MSMA
Ph.D.
None
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Dominated by:
Math, Statistics, Business
Many:
Computer Science, Engineering, Liberal Arts,
Engineering, Operations Research
Surprisingly few:
Finance, Economics, Creative
DEGREE AREA
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Consistent with Age
45% < 10 years
TOTAL YEARS PROFESSIONALLY EMPLOYED?
0 10 20 30 40 50
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Recent Analysts
29% < 5 years
YEARS EMPLOYED AS ANALYTICS PROFESSIONAL?
0 10 20 30 40
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Recent Hires
52% < 3 years
YEARS EMPLOYED BY CURRENT EMPLOYER?
0 10 20 30
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New in Role
49% < 2 years
88% < 5 years
YEARS EMPLOYED IN CURRENT ANALYTICS ROLE?
0 5 10 15
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Young
Mostly male
Most quite new to:Analytics
Current company
Current role
BIG PICTURE
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FUNCTIONAL CLUSTERS
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Analysis Design Data Acquisition and Collection Data Preparation Data Analytics Data Mining Visualization Programming Interpretation Presentation Administration Managing other Analytics Professionals
FUNCTIONAL DATAHOURS / WEEK SPENT IN ANALYTICS WORKFLOW
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Data Preparation Data acquisition, preparation, analytics
Programmer Programming, some analytics
ManagerManagement, Admin, Presentation, Interpretation,
Design
Generalist Little bit of everything
TASKS CLUSTER 4 FUNCTIONAL CLUSTERS
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TIME SPENT IN ANALYTICS WORKFLOWBY FUNCTIONAL CLUSTER
Demand
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“RAW TALENT”BENCHMARK
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RAW TALENT MINDSET FOR ANALYTICAL WORK?
Mindset
Skillset
Dataset
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RAW TALENT MEASURES
MEASURE SCORE 1 - 100
Approach to:
Problem Solving Collaborative Independent
Working with people Task People
Project Pacing No Process Process
Protocol & Details Low Detail High Detail
Deep Desire for:
Achieving Goals
Helping Others
Intellectual Curiosity
Discipline and Rigor
Drive to Compete
Creativity
Unique Projects
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ALL CLUSTERS ARE“INTELLECTUALLY CURIOUS”
Level of Intellectual CURIOSITY (The further right, the more Curious.)
All Clusters Skew High. Clearly Curiosity is a
“must” regardless of function in analytics role
All Clusters Skew High. Clearly Curiosity is a
“must” regardless of function in analytics role
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ALL CLUSTERS ARE“CREATIVE”
Level of CREATIVITY (The further right, the more
Creative.)
Creativity Skews High
in all Clusters
Creativity Skews High
in all Clusters
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CLEAR RAW TALENT FINGERPRINT
CURIOSITY CREATIVITY OBJECTIVITY
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ADVISOR 4.0PREDICTIVE MODEL
DEPLOYMENT PLATFORM
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“OLG’s Analytic Centre of Excellence has operationalized Talent Analytics’ Data Scientist Benchmark into our hiring process. We are now able to identify and proactively explore potential gaps during the interview process rather than discovering them after making the hire.
It’s proven to be an immensely valuable tool and should be considered by any analytics hiring manager wanting to enhance their success rate in hiring top data scientists/analytics professionals.”
Peter CuthbertDirector, Business Planning & Analyt icsOntar io Lottery and Gaming (OLG)
ACCOLADES
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STUDY CONCLUSIONS
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Demographics
Many Analytics Professionals newer to business, analytics, role and company
PhD not a requirement
Degree and skills often used as proxy for “how someone thinks”
STUDY CONCLUSIONS
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Functional Clusters
Analytics workflow clusters into functional areas
Few people well suited to entire analytics spectrum; unrealistic; doesn’t scale
Many analysts less interested in: financial compensation only; being promoted to management role
STUDY CONCLUSIONS
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Raw Talent Mindset
Analytics professionals have a clear, quantifiable “Raw Talent Mindset”
Employers using analytics to:Compare analytics candidates to industry benchmark
Develop a baseline of existing analytics professionals
STUDY CONCLUSIONS
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Be honest. Why analytics?
Other than skills, what makes you stand outGenerate demand? ROI insight? Focused expertise
in the workflow? Employee analytics?
Interview the interviewer about place in the
workflow
ANALYTICS CAREER
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OTHER RESOURCES
BurtchWorks.comSalary survey of data scientists
Rexer Analytics2103 Data Miner Survey Summary Reporthttp://www.rexeranalytics.com/Data-Miner-Survey-Results-2013.html
Greta Roberts
617-864-7474 x.101