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.

1. USING ANALYTICS TO
BUILD A
BIG DATA WORKFORCE
Greta Roberts
IIA Faculty Member
CEO Talent Analytics, Corp.©2014 Talent Analytics, Corp. | All Rights Reserved 1

2. Model
and optimize
human
performance
TALENT ANALYTICS, CORP.
employee
15 April 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 2

3.  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)
15 April 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 3
TALENT ANALYTICS PLATFORM
ADVISOR®

4. 15 April 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 4
BUSINESS CHALLENGES WE
SOLVE

5. 15 April 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 5
BUSINESS CHALLENGES
WE SOLVE

6. 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
15 April 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 6
BUSINESS CHALLENGES
BUILDING ANALYTICS BENCH

7. Talent Supply Research and
model working
Data Scientists
15 April 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 7
2 APPROACHES

8. Over-specified
Generic
Competing
requirements
Result:
Impossible to fill
15 April 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 8
ROLE REQUIREMENTS

9. We hire
externally
Internal
candidates don’t
have the right
skills
CONTRADICTIONS
15 April 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 9

10. Biggest mistake
you can make is
hiring for
technical skills
CONTRADICTIONS
15 April 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 10

11. 15 April 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 11
WHICH “SET” IS MOST
IMPORTANT?
Mindset
Skillset
Dataset

12. 15 April 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 12
WHICH “SET” IS MOST
IMPORTANT?
Mindset
Skillset
Dataset

13. 15 April 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 13
WHICH “SET” IS MOST
IMPORTANT?
Mindset
Skillset
Dataset

14. 15 April 2014 14©2014 Talent Analytics, Corp. | All Rights Reserved
NOW THE SCIENCE

15. Talent Analytics, Corp.
International Institute for Analytics
15 April 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 15
STUDY TEAM

16. Quantitative approach to defining
raw talent in analytics professionals
“Raw Talent” (mindset) vs.
Achievements (skillset)
Practical outcomes vs. purely academic
STUDY SUMMARY
UNIQUE ELEMENTS
15 April 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 16

17. 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
17©2014 Talent Analytics, Corp. | All Rights Reserved15 April 2014

18. Primary Analysis Tool: R
Three Methods:
Regression Methods
Fuzzy Clustering
Tree Modeling
DATA ANALYSIS
18©2014 Talent Analytics, Corp. | All Rights Reserved15 April 2014

19. ANALYTICS
PROFESSIONALS
DESCRIPTIVE STATISTICS
15 April 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 19

20. AGE
57% under 40
17% over 50
GENDER
 72% male
 Gender trend similar
across all age groups
AGE AND GENDER
15 April 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 20

21. 47% have Masters
36% have
Bachelors Degree
or Less
16% have PhDs
HIGHEST EDUCATIONAL
DEGREE
degree.highest
Pct
0
10
20
30
40
None Bachelors Masters Doctorate
3
33
47
16
21©2014 Talent Analytics, Corp. | All Rights Reserved15 April 2014
BS
BA
MS
MA
Ph.D.
None

22. Dominated by:
Math, Statistics, Business
Many:
Computer Science, Engineering, Liberal
Arts, Engineering, Operations Research
Surprisingly few:
Finance, Economics, Creative
15 April 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 22
DEGREE AREA

23. Consistent with Age
45% < 10 years
TOTAL YEARS
PROFESSIONALLY EMPLOYED?
yrs.work
Pct
0
5
10
15
20
0 10 20 30 40 50
22
23
17
10
13
7
2
0 0
23©2014 Talent Analytics, Corp. | All Rights Reserved15 April 2014
0 10 20 30 40 50

24. Recent Analysts
29% < 5 years
YEARS EMPLOYED
AS ANALYTICS PROFESSIONAL?
yrs.ana
Pct
0
10
20
30
0 10 20 30 40
29
31
11
12
5
4
1 1
0
24©2014 Talent Analytics, Corp. | All Rights Reserved15 April 2014
0 10 20 30 40

25. Recent Hires
52% < 3 years
YEARS EMPLOYED
BY CURRENT EMPLOYER?
yrs.curr
Pct
0
10
20
30
40
50
0 10 20 30
52
29
7
5
1
0 0 0
0
25©2014 Talent Analytics, Corp. | All Rights Reserved15 April 2014
0 10 20 30

26. New in Role
49% < 2 years
88% < 5 years
YEARS EMPLOYED
IN CURRENT ANALYTICS ROLE?
2615 April 2014
0 5 10 15©2014 Talent Analytics, Corp. | All Rights Reserved

27. Young
Mostly male
Most quite new
to:
Analytics
Current company
Current role
15 April 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 27
BIG PICTURE

28. FUNCTIONAL
CLUSTERS
15 April 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 28

29.  Analysis Design
 Data Acquisition and Collection
 Data Preparation
 Data Analytics
 Data Mining
 Visualization
 Programming
 Interpretation
 Presentation
 Administration
 Managing other Analytics Professionals
15 April 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 29
FUNCTIONAL DATA
HOURS / WEEK SPENT IN ANALYTICS WORKFLOW

30. Data Preparation
Data acquisition, preparation, analytics
Programmer
Programming, some analytics
Manager
Management, Admin, Presentation, Interpretation, D
esign
Generalist
Little bit of everything
15 April 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 30
TASKS CLUSTER
4 FUNCTIONAL CLUSTERS

31. TIME SPENT IN ANALYTICS WORKFLOW
BY FUNCTIONAL CLUSTER
15 April 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 31
Demand

32. “RAW TALENT”
BENCHMARK
15 April 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 32

33. 15 April 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 33
RAW TALENT MINDSET FOR
ANALYTICAL WORK?
Mindset
Skillset
Dataset

34. 15 April 2014 34
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
©2014 Talent Analytics, Corp. | All Rights Reserved

35. ALL CLUSTERS ARE
“INTELLECTUALLY CURIOUS”
©2014 Talent Analytics, Corp. | All Rights Reserved
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
15 April 2014 35

36. ALL CLUSTERS ARE
“CREATIVE”
©2014 Talent Analytics, Corp. | All Rights Reserved
Level of CREATIVITY
(The further right, the more Creative.)
Creativity
Skews High
in all Clusters
15 April 2014 36

37. 15 April 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 37
CLEAR RAW TALENT
FINGERPRINT
00
0.0000.0050.01
0 50 100
0.0000.0050.010
0 50 100
00
0.000.010.020.030.04
0 50 100
THE
0.0000.0050.0100.015
0 50 100
AUT
0.0000.0050.0100.015
CRE
O
.010
R
0100.015
E
Data Preparation
Generalists
Managers
Programmers
Value
50 100
0.0000.0050.010
0 50 100
0.000.010.020.
0 50 100
0.0000.0050.010
0 50 100
50 100
POL
0.0000.010
0 50 100
IND
0.0000.0050.0100.015
0 50 100
CRE
Density
0.0000.0050.0100.015 0 50 100
C
0.0000.0050.010
0 50 100
O
0.0000.0050.010
0.0000.0050.010
0 50 100
ECO
0.0000.0050.0100.015
0 50 100
ALT
0.000.010.020.030.04
Data Preparation
Generalists
Managers
Programmers
CURIOSITY CREATIVITY OBJECTIVITY

38. 15 April 2014 38©2014 Talent Analytics, Corp. | All Rights Reserved
ADVISOR 4.0
PREDICTIVE MODEL
DEPLOYMENT PLATFORM

39. 3915 April 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved

40. 4015 April 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved

41.  “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
Director, Business Planning & Analytics
Ontario Lottery and Gaming (OLG)
15 April 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 41
ACCOLADES

42. 15 April 2014 42©2014 Talent Analytics, Corp. | All Rights Reserved
STUDY
CONCLUSIONS

43. 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”
15 April 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 43
STUDY CONCLUSIONS

44. 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
©2014 Talent Analytics, Corp. | All Rights Reserved
STUDY CONCLUSIONS
15 April 2014 44

45. 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
15 April 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 45
STUDY CONCLUSIONS

46. 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
15 April 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 46
ANALYTICS CAREER

47. OTHER RESOURCES
BurtchWorks.com
Salary survey of data scientists
Rexer Analytics
2103 Data Miner Survey Summary Report
http://www.rexeranalytics.com/Data-Miner-
Survey-Results-2013.html
Greta Roberts
greta@talentanalytics.com
617-864-7474 x.101
15 April 2014 ©2014 Talent Analytics, Corp. | All Rights Reserved 47