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Using Analytics to build A Big Data Workforce
Using Analytics to build A Big Data Workforce
Using Analytics to build A Big Data Workforce
Using Analytics to build A Big Data Workforce
Using Analytics to build A Big Data Workforce
Using Analytics to build A Big Data Workforce
Using Analytics to build A Big Data Workforce
Using Analytics to build A Big Data Workforce
Using Analytics to build A Big Data Workforce
Using Analytics to build A Big Data Workforce
Using Analytics to build A Big Data Workforce
Using Analytics to build A Big Data Workforce
Using Analytics to build A Big Data Workforce
Using Analytics to build A Big Data Workforce
Using Analytics to build A Big Data Workforce
Using Analytics to build A Big Data Workforce
Using Analytics to build A Big Data Workforce
Using Analytics to build A Big Data Workforce
Using Analytics to build A Big Data Workforce
Using Analytics to build A Big Data Workforce
Using Analytics to build A Big Data Workforce
Using Analytics to build A Big Data Workforce
Using Analytics to build A Big Data Workforce
Using Analytics to build A Big Data Workforce
Using Analytics to build A Big Data Workforce
Using Analytics to build A Big Data Workforce
Using Analytics to build A Big Data Workforce
Using Analytics to build A Big Data Workforce
Using Analytics to build A Big Data Workforce
Using Analytics to build A Big Data Workforce
Using Analytics to build A Big Data Workforce
Using Analytics to build A Big Data Workforce
Using Analytics to build A Big Data Workforce
Using Analytics to build A Big Data Workforce
Using Analytics to build A Big Data Workforce
Using Analytics to build A Big Data Workforce
Using Analytics to build A Big Data Workforce
Using Analytics to build A Big Data Workforce
Using Analytics to build A Big Data Workforce
Using Analytics to build A Big Data Workforce
Using Analytics to build A Big Data Workforce
Using Analytics to build A Big Data Workforce
Using Analytics to build A Big Data Workforce
Using Analytics to build A Big Data Workforce
Using Analytics to build A Big Data Workforce
Using Analytics to build A Big Data Workforce
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: …

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
  • Transcript

    • 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

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