TALENT ANALYTICS, CORP.
Model
and optimize
employee
human
performance

10 February 2014

©2014 Talent Analytics, Corp.

|...
TALENT ANALYTICS PLATFORM
ADVISOR®
 Quantitatively measures “raw talent or mindset”

 11 scores per person
 Easily outp...
BUSINESS CHALLENGES WE
SOLVE

10 February 2014

©2014 Talent Analytics, Corp.

|

All Rights Reserved

4
BUSINESS CHALLENGES
WE SOLVE

10 February 2014

©2014 Talent Analytics, Corp.

|

All Rights Reserved

5
BUSINESS CHALLENGES
BUILDING ANALYTICS BENCH

Young field

“The sexiest job of
the 21 st century” 1

Young
practitioner...
2 APPROACHES
Talent Supply

10 February 2014

Research and
model working
Data Scientists

©2014 Talent Analytics, Corp.
...
ROLE REQUIREMENTS
Over-specified
Generic
Competing
requirements
Result:
Impossible to fill
10 February 2014

©2014 Tale...
CONTRADICTIONS
We hire
externally
Internal
candidates don’t
have the right
skills

10 February 2014

©2014 Talent Analyt...
CONTRADICTIONS
Biggest mistake
you can make is
hiring for
technical skills

10 February 2014

©2014 Talent Analytics, Cor...
WHICH “SET” IS MOST
IMPORTANT?
Dataset

Skillset
Mindset

10 February 2014

©2014 Talent Analytics, Corp.

|

All Rights R...
WHICH “SET” IS MOST
IMPORTANT?
Dataset

Skillset
Mindset

10 February 2014

©2014 Talent Analytics, Corp.

|

All Rights R...
WHICH “SET” IS MOST
IMPORTANT?
Dataset

Skillset
Mindset

10 February 2014

©2014 Talent Analytics, Corp.

|

All Rights R...
NOW THE SCIENCE

10 February 2014

©2014 Talent Analytics, Corp.

|

All Rights Reserved

14
STUDY TEAM
Talent Analytics, Corp.
International Institute for Analytics

10 February 2014

©2014 Talent Analytics, Corp...
STUDY SUMMARY
UNIQUE ELEMENTS

Quantitative approach to defining
raw talent in analytics professionals
“Raw Talent” (min...
METHODOLOGY
Global Sample: 304 “deep dive”
Data Scientists / Analytics Professionals
Data gathered online via questionna...
DATA ANALYSIS
Primary Analysis Tool: R
Three Methods:
Regression Methods
Fuzzy Clustering
Tree Modeling

10 February ...
ANALYTICS
PROFESSIONALS
DESCRIPTIVE STATISTICS

10 February 2014

©2014 Talent Analytics, Corp.

|

All Rights Reserved

1...
AGE AND GENDER
AGE

GENDER

57% under 40

 72% male

17% over 50

 Gender trend similar
across all age groups

10 Febr...
HIGHEST EDUCATIONAL
DEGREE
47% have Masters

47

MS
MA

40

33

BS
BA

30

Pct

36% have
Bachelors Degree
or Less

20

1...
DEGREE AREA
Dominated by:
Math, Statistics, Business

Many:
Computer Science, Engineering, Liberal Arts,
Engineering, ...
TOTAL YEARS
PROFESSIONALLY EMPLOYED?
 Consistent with Age

23
22

20

17

 45% < 10 years
15

Pct

13

10
10

7

5

2

0...
YEARS EMPLOYED
AS ANALYTICS PROFESSIONAL?
Recent Analysts

31
29
30

29% < 5 years
Pct

20

11

12

10

5
4

1

1
0

0

...
YEARS EMPLOYED
BY CURRENT EMPLOYER?
Recent Hires

52
50

52% < 3 years

40

29

Pct

30

20

10

7
5
1
0

0

0

0

0

0
...
YEARS EMPLOYED
IN CURRENT ANALYTICS ROLE?
New in Role
49% < 2 years

88% < 5 years

10 February 2014

0

©2014 Talent A...
BIG PICTURE
Young
Mostly male

Most quite new
to:
Analytics

Current company
Current role

10 February 2014

©2014 T...
FUNCTIONAL
CLUSTERS

10 February 2014

©2014 Talent Analytics, Corp.

|

All Rights Reserved

28
FUNCTIONAL DATA
H O U R S / W EEK SPEN T IN AN ALYT IC S W O R KF L O W

 Analysis Design
 Data Acquisition and Collecti...
TASKS CLUSTER
4 FUNCTIONAL CLUSTERS
Data Preparation
 Data acquisition, preparation, analytics

Programmer
 Programmin...
TIME SPENT IN ANALYTICS WORKFLOW
BY FUNCTIONAL CLUSTER

Demand

10 February 2014

©2014 Talent Analytics, Corp.

|

All Ri...
“RAW TALENT”
BENCHMARK

10 February 2014

©2014 Talent Analytics, Corp.

|

All Rights Reserved

32
RAW TALENT MINDSET FOR
ANALYTICAL WORK?
Dataset

Skillset
Mindset

10 February 2014

©2014 Talent Analytics, Corp.

|

All...
RAW TALENT MEASURES
MEASURE

SCORE 1 - 100

Problem Solving

Independent

Working with people

Task

People

Project Pacin...
ALL CLUSTERS ARE

“ INTELLECTUALLY CURIOUS”
All Clusters Skew
High. Clearly
Curiosity is a
“must” regardless
of function i...
ALL CLUSTERS ARE
“CREATIVE”

Creativity
Skews High
in all Clusters

Level of CREATIVITY
(The further right, the more Creat...
0.000 0.005 0.010

0

50

0.000 0.005 0.010

0.000 0.005 0.010

OBJECTIVITY
O

0
100 50

100

0

ECO

Value

©2014 Talent ...
ADVISOR 4.0
PREDICTIVE MODEL
DEPLOYMENT PLATFORM

10 February 2014

©2014 Talent Analytics, Corp.

|

All Rights Reserved
...
10 February 2014

©2014 Talent Analytics, Corp.

|

All Rights Reserved

39
10 February 2014

©2014 Talent Analytics, Corp.

|

All Rights Reserved

40
ACCOLADES
 “OLG’s Analytic Centre of Excellence has operationalized
Talent Analytics’ Data Scientist Benchmark into our h...
STUDY
CONCLUSIONS

10 February 2014

©2014 Talent Analytics, Corp.

|

All Rights Reserved

42
STUDY CONCLUSIONS
Demographics
Many Analytics Professionals newer to
business, analytics, role and company
PhD not a req...
STUDY CONCLUSIONS
Functional Clusters
Analytics workflow clusters into functional
areas
Few people well suited to entire...
STUDY CONCLUSIONS
Raw Talent Mindset
Analytics professionals have a clear,
quantifiable “Raw Talent Mindset”
Employers u...
ANALYTICS CAREER
Be honest. Why analytics?

Other than skills, what makes you stand out
 Generate demand? ROI insight? ...
OTHER RESOURCES
BurtchWorks.com
 Salary survey of data scientists

Rexer Analytics
2103 Data Miner Survey Summary Repo...
Using Analytics to build A Big Data Workforce
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Using Analytics to build A Big Data Workforce

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Synopsis:
Many innovative businesses and IT organizations appreciate the competitive advantage analytics capabilities can provide and have ambitions to reach increasing levels of analytics maturity. However, the well-documented shortage of analytic talent leaves many firms without a strong analytic talent bench and little knowledge about how and where to find analytics professionals needed to get there.

In this presentation, Greta Roberts will discuss results of a major quantitative Study of the "raw talent" of professional analytics professionals. This Study crossed industries, experience and skills.

Practical insights shared will include: raw talent characteristics businesses are looking for in their analytics professionals, trends and correlations that lend unexpected insight into how organizations are building a strong and scalable analytic talent bench.

Attendees will be provided with the ability to compare themselves to the Analytics Professional benchmark for no fee.

About the Speaker:

Greta Roberts, CEO, Talent Analytics [http://www.talentanalytics.com/] , Corp.

Greta Roberts is the CEO of Talent Analytics, Corp and a faculty member at the International Institute for Analytics. She has 20+ years working for world-class technology innovators like Lotus, Netscape, WebLine, Cisco and Open Ratings.
Under her direction, Talent Analytics has grown to be a leader in predicting employee behavior — the next logical step beyond predicting customer behavior. In 2012, she led a Research Team with the International Institute for Analytics that resulted in the world's only Benchmark for hiring Data Scientists / Analytics Professionals.

Greta is a sought-out thought leader, presenter, and author. In 2013, she has spoken at the Predictive Analytics World events around North America, SAS Day at Kennesaw State, SAP Sapphire NOW, IIA's Chief Analytics Officer Summit, SAS's Analytics 2013 & other major analytics & business events. She is also a frequent guest on the SAP's Game-Changers Radio Show. Greta has recently been quoted in MIT Sloan Management Review, the Harvard Business Review blog network, Forbes, VentureBeat, Information Management, Computerworld, Data Informed, Tech Target, and many other major influential publications. Follow Greta on twitter @GretaRoberts [ https://twitter.com/GretaRoberts ].

Microsoft [ http://microsoftnewengland.com ] for providing awesome venue for the event.

a2c[ http://a2c.com ] for providing the food/drinks.

cognizeus [ http://cognizeus.com ] for providing book to give away as raffle.

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  • For more than a decade Talent Analytics has been focused on modeling employee performance. Analytics has advanced – predicting and optimizing human performance – but with customers. Talent Analytics uses many of the same analytics approaches to model and optimize human performance– but with employees.Do a search on the term Talent Analytics. Even over the past 6 months there has been a huge explosion of interest in and solutions for these kinds of solutions. 95% of the Talent Analytics solutions out there are focused on taking existing activities and trying to use them as a proxy for inferring an understanding about employees.Talent Analytics is perhaps the only company in the world taking an analytics approach to directly measuring employee characteristics.Talent Analytics has been at least a decade ahead of the curve. Their solutions are tested. Mature. Advanced. High tech. Scalable and ready for deployment today.
  • “Give me someone curious and they’ll teach themselves . . .“
  • I wanted to begin with showing what all 4 clusters have in common. This slide shows a graph type called a Density Plot. Along thebottom (or X axis) we are measuing CURIOSITY. As a point of reference a BELL CURVE is a DENSITY plot as well. What you can see is that all 4 clusters are extremely curious. Every single position in our study showed people working in the role who were deeply curious, eager to learn, research oriented – people who are motivated by solving very sophisticated problems. NOTE: WHAT WE ARE MEASURING HERE IS CALLED RAW TALENT. THIS IS NOT SOMETHING YOU CAN TRAIN
  • IN this slide we are measuring another RAW TALENT characteristic – Creativity. We can see that all clusters tend to being highly creative people. We’re not coving it in this presentation – buit we did ask about people’s college degrees and majors and a very small percentage of people had a crativedegree.meaning to find these folks requires another way other than college degrees or majors.
  • Density plot – showing the likelihood one person would
  • Using Analytics to build A Big Data Workforce

    1. 1. USING ANALYTICS TO BUILD A BIG DATA WORKFORCE ©2014 Talent Analytics, Corp. | All Rights Reserved Greta Roberts IIA Faculty Member CEO Talent Analytics, Corp. 1
    2. 2. TALENT ANALYTICS, CORP. Model and optimize employee human performance 10 February 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 2
    3. 3. TALENT ANALYTICS PLATFORM ADVISOR®  Quantitatively measures “raw talent or mindset”  11 scores per person  Easily outputs to a .csv  Combines with any / all other performance variables (big or little data)  TA 11 variables often useful as independent variables  Advisor 4.0 is ideal platform for deploying predictive models during hiring cycle (or optimizing current employees) 10 February 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 3
    4. 4. BUSINESS CHALLENGES WE SOLVE 10 February 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 4
    5. 5. BUSINESS CHALLENGES WE SOLVE 10 February 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 5
    6. 6. BUSINESS CHALLENGES BUILDING ANALYTICS BENCH Young field “The sexiest job of the 21 st century” 1 Young practitioners 1 Thomas Davenport, D. J. Patil, October 2012 HBR Role requirements not well defined Comparables difficult 10 February 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 6
    7. 7. 2 APPROACHES Talent Supply 10 February 2014 Research and model working Data Scientists ©2014 Talent Analytics, Corp. | All Rights Reserved 7
    8. 8. ROLE REQUIREMENTS Over-specified Generic Competing requirements Result: Impossible to fill 10 February 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 8
    9. 9. CONTRADICTIONS We hire externally Internal candidates don’t have the right skills 10 February 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 9
    10. 10. CONTRADICTIONS Biggest mistake you can make is hiring for technical skills 10 February 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 10
    11. 11. WHICH “SET” IS MOST IMPORTANT? Dataset Skillset Mindset 10 February 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 11
    12. 12. WHICH “SET” IS MOST IMPORTANT? Dataset Skillset Mindset 10 February 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 12
    13. 13. WHICH “SET” IS MOST IMPORTANT? Dataset Skillset Mindset 10 February 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 13
    14. 14. NOW THE SCIENCE 10 February 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 14
    15. 15. STUDY TEAM Talent Analytics, Corp. International Institute for Analytics 10 February 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 15
    16. 16. STUDY SUMMARY UNIQUE ELEMENTS Quantitative approach to defining raw talent in analytics professionals “Raw Talent” (mindset) vs. Achievements (skillset) Practical outcomes vs. purely academic 10 February 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 16
    17. 17. METHODOLOGY 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™ 10 February 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 17
    18. 18. DATA ANALYSIS Primary Analysis Tool: R Three Methods: Regression Methods Fuzzy Clustering Tree Modeling 10 February 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 18
    19. 19. ANALYTICS PROFESSIONALS DESCRIPTIVE STATISTICS 10 February 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 19
    20. 20. AGE AND GENDER AGE GENDER 57% under 40  72% male 17% over 50  Gender trend similar across all age groups 10 February 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 20
    21. 21. HIGHEST EDUCATIONAL DEGREE 47% have Masters 47 MS MA 40 33 BS BA 30 Pct 36% have Bachelors Degree or Less 20 16 16% have PhDs Ph.D. 10 3 None 0 None Bachelors Masters Doctorate degree.highest 10 February 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 21
    22. 22. DEGREE AREA Dominated by: Math, Statistics, Business Many: Computer Science, Engineering, Liberal Arts, Engineering, Operations Research Surprisingly few: Finance, Economics, Creative 10 February 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 22
    23. 23. TOTAL YEARS PROFESSIONALLY EMPLOYED?  Consistent with Age 23 22 20 17  45% < 10 years 15 Pct 13 10 10 7 5 2 0 0 0 0 0 10 February 2014 ©2014 Talent Analytics, Corp. | 10 10 All Rights Reserved 20 yrs.work 30 20 30 40 40 50 50 23
    24. 24. YEARS EMPLOYED AS ANALYTICS PROFESSIONAL? Recent Analysts 31 29 30 29% < 5 years Pct 20 11 12 10 5 4 1 1 0 0 0 0 10 February 2014 ©2014 Talent Analytics, Corp. | 10 10 All Rights Reserved 20 20 yrs.ana 30 30 40 40 24
    25. 25. YEARS EMPLOYED BY CURRENT EMPLOYER? Recent Hires 52 50 52% < 3 years 40 29 Pct 30 20 10 7 5 1 0 0 0 0 0 0 0 10 February 2014 ©2014 Talent Analytics, Corp. | 10 10 All Rights Reserved 20 yrs.curr 20 30 30 25
    26. 26. YEARS EMPLOYED IN CURRENT ANALYTICS ROLE? New in Role 49% < 2 years 88% < 5 years 10 February 2014 0 ©2014 Talent Analytics, Corp. | 5 All Rights Reserved 10 15 26
    27. 27. BIG PICTURE Young Mostly male Most quite new to: Analytics Current company Current role 10 February 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 27
    28. 28. FUNCTIONAL CLUSTERS 10 February 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 28
    29. 29. FUNCTIONAL DATA H O U R S / W EEK SPEN T IN AN ALYT IC S W O R KF L O W  Analysis Design  Data Acquisition and Collection  Data Preparation  Data Analytics  Data Mining  Visualization  Programming  Interpretation  Presentation  Administration  Managing other Analytics Professionals 10 February 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 29
    30. 30. TASKS CLUSTER 4 FUNCTIONAL CLUSTERS Data Preparation  Data acquisition, preparation, analytics Programmer  Programming, some analytics Manager  Management, Admin, Presentation, Interpretation, Design Generalist  Little bit of everything 10 February 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 30
    31. 31. TIME SPENT IN ANALYTICS WORKFLOW BY FUNCTIONAL CLUSTER Demand 10 February 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 31
    32. 32. “RAW TALENT” BENCHMARK 10 February 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 32
    33. 33. RAW TALENT MINDSET FOR ANALYTICAL WORK? Dataset Skillset Mindset 10 February 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 33
    34. 34. RAW TALENT MEASURES MEASURE SCORE 1 - 100 Problem Solving Independent Working with people Task People Project Pacing No Process Process Protocol & Details Approach to: Collaborative Low Detail High Detail Achieving Goals Helping Others Intellectual Curiosity Deep Desire Discipline and Rigor for: Drive to Compete Creativity Unique Projects 10 February 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 34
    35. 35. ALL CLUSTERS ARE “ INTELLECTUALLY CURIOUS” All Clusters Skew High. Clearly Curiosity is a “must” regardless of function in analytics role Level of Intellectual CURIOSITY (The further right, the more Curious.) 10 February 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 35
    36. 36. ALL CLUSTERS ARE “CREATIVE” Creativity Skews High in all Clusters Level of CREATIVITY (The further right, the more Creative.) 10 February 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 36
    37. 37. 0.000 0.005 0.010 0 50 0.000 0.005 0.010 0.000 0.005 0.010 OBJECTIVITY O 0 100 50 100 0 ECO Value ©2014 Talent Analytics, Corp. E | 0 All Rights Reserved 50 100 50 100 ALT 0.00 0.01 0.02 0.03 0.04 0.000 0.005 0.010 0.015 50 50 0 100 Data Preparation Generalists Managers 100 Programmers 0.0000.0050.0100.015 10 February 2014 0 R .010 O 100 0.000 0.005 0.010 010 0.015 CRE Data Preparation Generalists Managers Programmers 100 CRE AUTCREATIVITY C Density 50 50 0 100 50 0 10050 0 0.000 0.005 0.0100.015 0 0.010 0.000 100 0.00 0.01 0.02 0. 0.000 0.005 0.010 50 0 10050 100 CURIOSITY IND THE 0.00 0.01 0.02 0.03 0.04 50 00 0.000 0.005 0.0100.015 POL 0 0.000 0.005 0.010 0.015 0.0000.0050.010 0.000 0.005 0.01 0050 100 CLEAR RAW TALENT FINGERPRINT 37 0 50 100
    38. 38. ADVISOR 4.0 PREDICTIVE MODEL DEPLOYMENT PLATFORM 10 February 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 38
    39. 39. 10 February 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 39
    40. 40. 10 February 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 40
    41. 41. ACCOLADES  “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 Cuthbert D i r e c t or, B u s i n e ss P l a n ni n g & An a l yt i c s Ontario Lottery and Gaming (OLG) 10 February 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 41
    42. 42. STUDY CONCLUSIONS 10 February 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 42
    43. 43. 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” 10 February 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 43
    44. 44. 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 10 February 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 44
    45. 45. 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 10 February 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 45
    46. 46. ANALYTICS CAREER Be honest. Why analytics? Other than skills, what makes you stand out  Generate demand? ROI insight? Focused expertise in the workflow? Employee analytics? Interview the interviewer about place in the workflow 10 February 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 46
    47. 47. OTHER RESOURCES BurtchWorks.com  Salary survey of data scientists Rexer Analytics 2103 Data Miner Survey Summary Report http://www.rexeranalytics.com/Data-MinerSurvey-Results-2013.html Greta Roberts greta@talentanalytics.com 617-864-7474 x.101 10 February 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 47

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