Using Analytics to build A Big Data Workforce

USING ANALYTICS TO BUILD A

BIG DATA WORKFORCE

Greta RobertsIIA Faculty Member

Modeland optimizehuman performance

TALENT ANALYTICS, CORP.

employee

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®

BUSINESS CHALLENGES WE SOLVE

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

Talent Supply Research and model workingData Scientists

2 APPROACHES

Over-specified

Generic

Competing requirements

Result: Impossible to fill

ROLE REQUIREMENTS

We hire externally

Internal candidates don’t have the right skills

CONTRADICTIONS

Biggest mistake you can make is hiring for technical skills

CONTRADICTIONS

WHICH “SET” IS MOST IMPORTANT?

Mindset

Skillset

Dataset

Mindset

Skillset

Dataset

Mindset

Skillset

Dataset

NOW THE SCIENCE

Talent Analytics, Corp.

International Institute for Analytics

STUDY TEAM

Quantitative approach to defining raw talent in analytics professionals

“Raw Talent” (mindset) vs. Achievements (skillset)

Practical outcomes vs. purely academic

STUDY SUMMARYUNIQUE ELEMENTS

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

Primary Analysis Tool: R

Three Methods:Regression MethodsFuzzy ClusteringTree Modeling

DATA ANALYSIS

ANALYTICS PROFESSIONALS

DESCRIPTIVE STATISTICS

57% under 40

17% over 50

GENDER

72% male

Gender trend similar across all age groups

AGE AND GENDER

47% have Masters

36% have Bachelors Degree or Less

16% have PhDs

HIGHEST EDUCATIONAL DEGREE

Dominated by:

Math, Statistics, Business

Computer Science, Engineering, Liberal Arts,

Engineering, Operations Research

Surprisingly few:

Finance, Economics, Creative

DEGREE AREA

Consistent with Age

45% < 10 years

TOTAL YEARS PROFESSIONALLY EMPLOYED?

0 10 20 30 40 50

Recent Analysts

29% < 5 years

YEARS EMPLOYED AS ANALYTICS PROFESSIONAL?

0 10 20 30 40

Recent Hires

52% < 3 years

YEARS EMPLOYED BY CURRENT EMPLOYER?

0 10 20 30

New in Role

49% < 2 years

88% < 5 years

YEARS EMPLOYED IN CURRENT ANALYTICS ROLE?

0 5 10 15

Mostly male

Most quite new to:Analytics

Current company

Current role

BIG PICTURE

FUNCTIONAL CLUSTERS

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

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

TIME SPENT IN ANALYTICS WORKFLOWBY FUNCTIONAL CLUSTER

Demand

“RAW TALENT”BENCHMARK

RAW TALENT MINDSET FOR ANALYTICAL WORK?

Mindset

Skillset

Dataset

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

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

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

CLEAR RAW TALENT FINGERPRINT

CURIOSITY CREATIVITY OBJECTIVITY

ADVISOR 4.0PREDICTIVE MODEL

DEPLOYMENT PLATFORM

“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

STUDY CONCLUSIONS

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

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

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

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

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

greta@talentanalytics.com

617-864-7474 x.101

Using Analytics to build A Big Data Workforce

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