USING ANALYTICS TO BUILD YOUR ANALYTICS BENCH...USING ANALYTICS TO BUILD YOUR ANALYTICS BENCH Greta...

USING ANALYTICS TO BUILD

YOUR ANALYTICS BENCH

Greta Roberts IIA Faculty Member

CEO Talent Analytics, Corp.

20 March 2013 ©2013 Talent Analytics, Corp. | All Rights Reserved 1

¡ Problem building analytics bench ¡ Current approaches ¡ 2012 Analytics Professional Study

§ Macro view - demographics

§ What they do at work

§ Micro view – what drives them

¡ Fingerprint / Benchmark


AGENDA

20 March 2013 3 ©2013 Talent Analytics, Corp. | All Rights Reserved

TALENT ANALYTICS, CORP.

¡ In the business of predicting human behavior

¡ Customer behavior

TALENT ANALYTICS, CORP.

Employee


THE TALENT ANALYTICS MODEL

¡ Well documented mental model § (1) Human Ambitions (2) Human Behaviors § 11 quantitative factors

¡ Modern cloud technology directly measures “natural talent” to this model

¡ We (and Consulting Partners) do the data science § Correlate with business performance § Benchmark success


20-Mar-13 6

SIMPLE, ELEGANT WORKFLOW

Online Questionnaire

Talent Analytics’ Advisor™

Output

•  11 numbers •  CSV for Analytics

Models •  Directly inside of

Salesforce.com •  Other software

FACULTY: INTERNATIONAL INSTITUTE FOR ANALYTICS

¡ Research Director: Tom Davenport, Ph.D. ¡ IIA: The only research firm dedicated exclusively to

defining the path to analytics excellence

¡ Offers the reliability of a world-class research library and faculty team, the benefits of a professional association, and the inspiration of a face-to-face network

www.iianalytics.com



BUILDING ANALYTICS BENCH:

THE CHALLENGE

FACTORS: WHY BUILDING ANALYTICS BENCH IS CHALLENGING

¡ Practice of analytics still in formation

¡ Very young field

¡ Very young practitioners

¡ Comparison to others is difficult

¡ “The sexiest job of the 21st century”1 1 Thomas Davenport, D. J. Patil, October 2012 HBR


¡ Demand > Supply § McKinsey Global

Institute: “By 2018, the US could face a shortage of 140,000 to 190,000 analytics professionals”

§ CIO.com Bob Violino: “Every single client says they are struggling with finding and retaining BI talent”

FACTORS: WHY BUILDING ANALYTICS BENCH IS CHALLENGING


2 POSSIBILITIES

¡ Talent supply problem

¡ Role definition problem


¡ Requirements over-specified?

¡ Competing requirements?

¡ Impossible to fill

¡ Study begins clarifying what is important / not

DEFINITION PROBLEM?



APPROACHES TO HIRING

APPROACHES TO BUILDING ANALYTICS BENCH

¡ Subjective measures ¡ Science and objective measures ¡ Both


Analytics Thought Leaders

FIRST SOME ANECDOTES


ANECDOTAL GOLD

¡ Simon Zhang, LinkedIn - “People we’ve rejected are those who do exactly what has been told. We need people who go way beyond what has been asked, even to the point of asking why the question has been asked.”

¡ John Elder, Elder Research - “Great analytics professionals are used to working with uncertainty; willing to work with noise.


ANECDOTAL GOLD

¡ Jeanne Harris, Accenture – “What signals a bad hire? If commercialization makes them fall apart (a schedule, project demand, business output) this can be a huge barrier.”

¡ Ted Vandenberg, Farmers Insurance – “A hiring mistake? Making an advanced degree an absolute. Academic backgrounds are a proxy for how analytics professionals think.”


¡ “We hire externally because internal candidates don’t have the technical skills”

BIGGEST CONTRADICTION


¡ “We hire externally because internal candidates don’t have the technical skills”

¡ “Biggest mistake you can make is hiring for technical skills”

BIGGEST CONTRADICTION



NOW THE SCIENCE

¡  Talent Analytics, Corp. § Greta Roberts CEO §  Pasha Roberts CAO §  John Muller Chief Data Scientist

¡  International Institute for Analytics

§  Tom Davenport IIA Cofounder, Research Director § Robert Morison IIA Faculty, Co-Author Analytics at Work §  Bill Franks IIA Faculty, Chief Analytics Officer, Teradata &

Author Taming the Big Data Tidal Wave § Greta Roberts IIA Faculty, CEO Talent Analytics, Corp.


STUDY TEAM

¡ Gathered data online via questionnaire ¡ Dates: June – August 2012

¡ Sources: Meetup, LinkedIn Groups, Analytics Media, PAWCON

¡ Referrals: Several companies with > 25 people

¡ Google Spreadsheet/Forms + Talent Analytics Advisor™

¡ Collected: 304 “deep dive” Data Scientists / Analytics Professionals

METHODOLOGY

22 ©2013 Talent Analytics, Corp. | All Rights Reserved 20 March 2013

¡ Analytics approach can help solve problem of building analytics bench

¡ Included measurements of “potential” (natural talent)

¡ Focus on practical outcomes vs. purely academic interests

STUDY SUMMARY UNIQUE ELEMENTS


¡ Primary Tool: R

¡ Three Methods: § Descriptive Statistics § Fuzzy Clustering § Tree Modeling

DATA ANALYSIS


ANALYTICS PROFESSIONALS

DESCRIPTIVE STATISTICS


AGE

¡ 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 Doctorates

HIGHEST EDUCATIONAL DEGREE

degree.highest

Pct

0

10

20

30

40

None Bachelors Masters Doctorate

3

33

47

16


BS BA

MSMA

Ph.D.

None

¡ Dominated by: § Math, Statistics, Business

¡ Many: § Computer Science, Engineering, Liberal Arts,

Engineering, Operations Research

¡ Surprisingly few: § Science, Finance, Economics


DEGREE AREA

¡ Consistent with Age

¡ 45% < 10 years

¡ 9% > 30 years

TOTAL YEARS PROFESSIONALLY EMPLOYED?

yrs.work

Pct

0

5

10

15

20

0 10 20 30 40 50

2223

17

10

13

7

2

0 0


0 10 20 30 40 50

¡ Recent Analysts

¡ 29% < 5 years ¡ 60% < 10 years

¡ 6% > 20 years

YEARS EMPLOYED AS ANALYTICS PROFESSIONAL?

yrs.ana

Pct

0

10

20

30

0 10 20 30 40

29

31

1112

54

1 10


0 10 20 30 40

¡ Recent Hires

¡ 52% < 3 years

¡ 7% > 10 years

YEARS EMPLOYED BY CURRENT EMPLOYER?

yrs.curr

Pct

0

10

20

30

40

50

0 10 20 30

52

29

7

5

10 0 0 0


0 10 20 30

¡ New in Role ¡ 49% < 2 years ¡ 88% < 5 years

¡ 2% > 10 years

YEARS EMPLOYED IN CURRENT ANALYTICS ROLE?

32 20 March 2013 yrs.role

Pct

0

10

20

30

40

50

0 5 10 15

49

29

10

32 1 1 0 0

0 5 10 15 ©2013 Talent Analytics, Corp. | All Rights Reserved

¡ Young, mostly male ¡ Most new to: § Analytics § Company § 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 DATA HOURS / WEEK SPENT IN ANALYTICS WORKFLOW

¡ Data Preparation § Data acquisition, preparation, analytics

¡ Programmer § Programming, some analytics

¡ Manager § Management, Admin, Presentation, Interpretation,

Design

¡ Generalist § Little bit of everything


TASKS CLUSTER 4 FUNCTIONAL CLUSTERS

TIME SPENT IN ANALYTICS WORKFLOW BY FUNCTIONAL CLUSTER


ANALYTICS PROFESSIONAL

FINGERPRINT



TALENT ANALYTICS DATASET RAW TALENT / POTENTIAL

Quantified Characteristics

•  Altruistic •  Absolute •  Collaborative •  Competitive •  Creative •  Curious •  Detailed •  Objective •  Process Oriented •  ROI Focused •  Unique Approach

STUDY RESULTS: STRONG “NATURAL TALENT” BENCHMARK

Quantified Characteristics

Signal Strength

•  Curious •  Creative •  Objective •  Structured •  Detailed •  Absolute •  ROI Focused •  Collaborative •  Altruistic •  Competitive •  Unique Approach

•  High •  High •  High •  High •  High •  Medium •  Medium •  Medium •  Low •  Low •  Low



STRONG “NATURAL TALENT” FINGERPRINT

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CURIOUS CREATIVE OBJECTIVE

¡ Anecdote § “Hire Artists”

¡ Data Science Approach § ”Hire people with Theoretical Score centered near 80”

COMPARE THESE APPROACHES


BUSINESS CONCLUSIONS OF

STUDY

¡ Analytics role is over-specified, with competing requirements

¡ Analytics talent pool is deeper/broader ¡ Technical skills, and Ph.Ds. are proxy

for how a candidate thinks § Many false positives

¡ You can measure natural talent directly


BUSINESS CONCLUSIONS

IRONY AND OPPORTUNITY

¡ Predicting human behavior is not foreign ¡ Advanced data science predicts

customer behavior, who are humans

¡ Your analytics bench? Also human ¡ No need to over rely on subjective measures

when building analytics bench ¡ We are already experts at this


¡ Measure potential of your own top (bottom) analytics professionals for patterns and trends

¡ Measure “potential” in Analytics Candidate

¡ Compare candidates to your own top (bottom) performers, or

¡ Comparative: Industry Benchmark created by this Study


USE ANALYTICS TO BUILD ANALYTICS BENCH

NEXT STEPS

¡ Business card for: § Updated presentation § Interest in our Analytics Benchmark as

comparative § Research Brief § Follow up conversation

Greta Roberts

[email protected] 617-864-7474 x.111


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