GETTING YOUR COLLEGE HIRES JOB READY Jillian Payne, Director Analytic Development Program.

Slides:



Advertisements
Similar presentations
HART RESEARCH P e t e r D A S O T E C I
Advertisements

Life Science Services and Solutions
CIRPA Conference October 23, CIRPA Conference 2011  Organization Overview  BI Program – Enrolment Management Project ◦ Project Drivers ◦ Business.
Plateau Competency Management and Assessment Overview v 5.8.
A World Of Opportunities Operations, Management Services and Technology Programs.
Enabling Science Developing Team Skills: Testing Professional Development Framework Mike Jarred & Dr. Luke Avsejs.
Oregon Common Core State Standards Transitioning to New Standards and Assessments.
Building Value into the Hiring Process
OPERATIONS and LOGISTICS MANAGEMENT
SAS Project Insight Planning Session 1 Good Morning!
Missouri Industry Competency Models- Aligning Skills and Curriculum to Demand Mary Bruton| Missouri Economic Research and Information Center| Missouri.
 Here’s What... › The State Board of Education has adopted the Common Core State Standards (July 2010)  So what... › Implications and Impact in NH ›
1 The Ten Essentials of Developing a Successful Balanced Scorecard.
SCHINDLER Sales Force Training Needs Assessment and Development Project Michael Yurchuk Sales Training Manager, Schindler Elevator Richard Dapra Ph.D.,
IMPACT OF SOFT SKILLS TRAININGS ON BOTTOM LINE. SKILLS GAP SKILLS GAP IS A SIGNIfiCANT GAP BETWEEN AN ORGANIZATION’S CURRENT CAPABILITIES AND THE SKILLS.
Program Participants: Department Managers, Project Leaders, Senior officers, Black Belt candidates and anyone who desires an understanding of Lean Six.
The Vision Implementation Project
MA course on language teaching and testing February 2015.
Campaign Readiness Project Overview Enabling a structured, scalable approach to customer-centric campaigns.
SALES PERFORMANCE MANAGEMENT T Owen Group. Sales Performance Management T Owen Group The T Owen Group Consultants work with Sales Leaders, HR Business.
Overview of the workout TimeContentMethod / Person 10 minutesWelcome & objectives Trainer led 20 minutes Coaching – what it is; why it’s so important;
Epiphany Partners Corporation HealthTechNet ClerePath TM Solution July 21, 2006.
TEST With Johan Beeckmans
GOLDEN ERA TECHNOLOGIES DIRECTOR: B.GANESH
+ Is your School's Instructional Program Ready for Common Core? Reach Institute for School Leadership.
CS 110: Introduction to Computer Science Frequently asked questions about a CS major and CS career.
November 2006 Copyright © 2006 Mississippi Department of Education 1 Where are We? Where do we want to be?
PREPARING [DISTRICT NAME] STUDENTS FOR COLLEGE & CAREER Setting a New Baseline for Success.
Demystifying the Data Scientist Dan McClary, Ph.D. Big Data Product Management Oracle Note: The speaker notes for this slide include detailed instructions.
1 Common Core State Standards: Overview and Update for NH Deb Wiswell, NH Department of Education Curriculum, Assessment, Accountability, School Improvement.
Developing Institutional Capacity for Learning Assessment  Institutional Structures in India : An Overview  Institutional Initiatives for Learning Assessment.
MAP the Way to Success in Math: A Hybridization of Tutoring and SI Support Evin Deschamps Northern Arizona University Student Learning Centers.
©2015 The Advisory Board Company eab.com Ensuring Best-Fit Program Selection from Day One KCCD Early Action and Results Student Success Collaborative™—Navigate.
Oregon Diploma & Essential Skills Task Force Phase I : Defining the Essential Skills Work Session October 1, 2007.
PERSONAL DEVELOPMENT PLANNING Helping to set goals and reach potential 1 The Lloyds Bank Foundation is committed to providing this information in a way.
Automatic Discovery and Processing of EEG Cohorts from Clinical Records Mission: Enable comparative research by automatically uncovering clinical knowledge.
QUALITY HOMES IN GREAT COMMUNITIES Your Personal Review and Development Plan.
Training the Next Generation of Mainframe Engineers Joseph Bedell Jason Breen Joe Parisi ECC June 11 th 2013.
How To Use Collaborative Goal Setting and Develop a Performance Feedback Process BPI Emerging Leader Series.
Measuring Development Impact: Beyond Satisfaction Deloitte Services LP Janelle HughesJohn DeVille Development LeaderTalent Analytics Manager.
DSCYF Curriculum Frameworks Educational Excellence for Every Student, Every Day!
Jane Greenstein Content Strategy & UX February, 2016.
Launch_blank. Set3_TitleandContent First Time Leader Department Leaders Operational Leaders Leaders of Leaders L1: Influential L2: Emerging L3: Strategic.
Leadership Guide for Strategic Information Management Leadership Guide for Strategic Information Management for State DOTs NCHRP Project Information.
FACULTY EXTERNSHIP OPPORTUNITIES IN DATA SCIENCE AND DATA ANALYTICS Facilitated by: FilAm Software Technology, Clark Freeport Zone Ecuiti, San Francisco,
Talent Pipeline Management. Goals 1. Introduce USCCF and our work focused on closing the skills 2. Familiarize you with the tools, resources, and supports.
Our Services Outbound Call Center Services
The Power of Using Artificial Intelligence
Performance Management
Office 365 Security Assessment Workshop
Top Ten List for Directors of Technology
Empower your Data Analyst
PARENT/ TEACHER HIGH LEVEL OVERVIEW
Session Code: 314 Competing in an Analytical Environment
Huntsville City Schools
Common Assessment Overview Marina Aminy, Ph.D.
HR Management for Business Plans
SKILL ASSESSMENT OF SOFTWARE TESTERS Case Study
Beyond the BACoE: Developing Business Analysis Maturity.
From Data to Insights Evolving your Program with Analytics.
Note: Red boxes indicate where slides will need to be customized for each organization Building Future Leaders Session 1: Kickoff and identify current/future.
Wellingtone PMO Practitioner
May May June Boston
PBC Mary dowling.
People Lead: This is the visual representation of our model. This model supports and reinforces our definition of leadership - achieving results, with.
KEY INITIATIVE Financial Data and Analytics
College of Social Sciences
OU BATTLECARD: Oracle Systems Learning Subscription
Presentation transcript:

GETTING YOUR COLLEGE HIRES JOB READY Jillian Payne, Director Analytic Development Program

222 © 84.51° 2015 | Confidential We believe in making people’s lives easier by putting the customer at the center of everything we do WHO IS 84.51°

333 © 84.51° 2015 | Confidential 3 ANALYSTS AT 84.51° 75% of the day is spent programming

444 © 84.51° 2015 | Confidential 4 GREAT ANALYSTS BALANCE TECHNICAL COMPETENCY WITH COMMERCIAL ACUMEN

555 © 84.51° 2015 | Confidential 5 WE CLUSTERED OVER 40 ANALYST SKILLS INTO DIMENSIONS ALL COMPANY GENERAL ANALYSIS DATA SCIENCE METHODOLOGY PRODUCT ENGINEERING BUSINESS Understanding capabilities Problem Solving Data usage Data mining & manipulation Diagnostics/ troubleshooting Programming Languages Data integration & interpretation Business & commercial acumen Story telling Data visualization Product development Data modeling Classical statistics Multivariate statistics / choice-based techniques Sampling methods Big data Machine learning Theoretical mathematics

666 © 84.51° 2015 | Confidential 6 MOST COLLEGE HIRES DON’T HAVE ALL OF THESE SKILLS, BUT THEY HAVE POTENTIAL We can hire based on potential and train them to do what we need them to do

777 © 84.51° 2015 | Confidential 7 PROJECT INTAKE WEEKS 10+ PROJECT SHADOWING WEEKS 8+ TRAINING WEEKS 1-8 BUSINESS ROLE ALIGNMENT MONTHS 4-6 WE GET ANALYSTS READY TO ADD VALUE TO THE BUSINESS IN 4-6 MONTHS

888 © 84.51° 2015 | Confidential 8 THE ANALYST DEVELOPMENT PROGRAM FOCUSES ON BUILDING THE FOUNDATION ALL COMPANY GENERAL ANALYSIS DATA SCIENCE METHODOLOGY PRODUCT ENGINEERING BUSINESS Understanding capabilities Problem Solving Data usage Data mining & manipulation Diagnostics/ troubleshooting Programming Languages Data integration & interpretation Business & commercial acumen Story telling Data visualization Product development Data modeling Classical statistics Multivariate statistics / choice-based techniques Sampling methods Big data Machine learning Theoretical mathematics We focus on building 18 fundamental 84.51°analysis skills

999 © 84.51° 2015 | Confidential 9 TRAINING COURSES ARE DESIGNED AND CHOSEN TO DEVELOP THESE PROFICIENCIES TRAINING COURSES Sector Overviews Case Studies Capability Spotlight: #lifeofananalyst ADP Alumni Panels ALL COMPANYGENERAL ANALYSISBUSINESS Understanding capabilities Problem Solving Data usage Data integration & interpretation Business & commercial acumen Story telling Data mining & manipulation Diagnostics/ troubleshooting Programming Languages SKILLS

10 © 84.51° 2015 | Confidential 10 TRAINING COURSES ARE DESIGNED AND CHOSEN TO DEVELOP THESE PROFICIENCIES TRAINING COURSES Sector Overviews Case Studies Capability Spotlight: #lifeofananalyst ADP Alumni Panels ALL COMPANYGENERAL ANALYSISBUSINESS Understanding capabilities Problem Solving Data usage Data integration & interpretation Business & commercial acumen Story telling Data mining & manipulation Diagnostics/ troubleshooting Programming Languages SKILLS Data Training SAS / SQL / R Golden Rules QA Training

11 © 84.51° 2015 | Confidential 11 TRAINING COURSES ARE DESIGNED AND CHOSEN TO DEVELOP THESE PROFICIENCIES TRAINING COURSES Sector Overviews Case Studies Capability Spotlight: #lifeofananalyst ADP Alumni Panels ALL COMPANYGENERAL ANALYSISBUSINESS Understanding capabilities Problem Solving Data usage Data integration & interpretation Business & commercial acumen Story telling Data mining & manipulation Diagnostics/ troubleshooting Programming Languages SKILLS Data Training SAS / SQL / R Golden Rules QA Training What, So What, Now What Client 101 Storyboarding Effective Powerpoints

12 © 84.51° 2015 | Confidential 12 EACH TRAINING IS MAPPED TO DNA AND SPECIFIC LEARNING OUTCOMES DNA skills we expect each training will build Learning objective (Why am I sitting here?)

13 © 84.51° 2015 | Confidential 13 SHADOWING PROJECTS ALLOWS FOR TESTING SKILLS ON LIVE WORK Working on a life project in parallel with another analyst Exposure to live client work A chance to own part of a bigger project Low-risk PROJECT SHADOWING

14 © 84.51° 2015 | Confidential 14 PROJECT WORK SPANS A BREADTH OF CAPABILITIES GAIN A BREADTH OF EXPOSURE What do they like to do? Where do they want to grow and develop more? ASSESS BEST-FIT TEAM Where do they fall across the technical / commercial spectrum? Where can they add the most value to the business? Where are their skills needed the most?

15 © 84.51° 2015 | Confidential 15 FEEDBACK DETERMINES SUCCESS AND INFLUENCE PROGRAM CONTENT  Felt prepared for role  Felt trainings were effective and useful  Felt new starters were ready to contribute GRAD ANALYST FEEDBACK MANAGEMENT FEEDBACK What do we need to refine? What are we missing? Do we have the right timeline? What should be scaled beyond the ADP Program? What isn’t value-add?  How many grads were ready for a role by month 4? Month 5? Month 6?  Were all DNA skills demonstrated?  Which trainings were not effective? GRAD MANAGER ASSESSMENT

16 © 84.51° 2015 | Confidential 16 CONTINUOUS FEEDBACK ENABLES US TO GET SMARTER ABOUT HOW WE TRAIN NEW TALENT Plan Execute Assess & Refine JANUARY JUNE Plan Execute Assess & Refine

17 © 84.51° 2015 | Confidential 17 PROJECT INTAKE WEEKS 10+ PROJECT SHADOWING WEEKS 8+ TRAINING WEEKS 1-8 BUSINESS ROLE ALIGNMENT MONTHS 4-6 LEARNING DOESN’T STOP THERE… CONTINUING EDUCATION ONGOING

18 © 84.51° 2015 | Confidential 18 TO BUILD ON OUR SUCCESS OF TRAINING NEW ANALYSYTS, WE SCALED THIS METHOD ANALYST DEVELOPMENT PROGRAM 7 weeks NEW ANALYST TRAINING 2 weeks ANALYST DEVELOPMENT PROGRAM 7 weeks. JanAprilJuneSept NEW ANALYST TRAINING 2 weeks

19 © 84.51° 2015 | Confidential 19 ANALYSTS WANT MORE TRAINING THAN JUST BUILDING THE FOUNDATION – WHAT IS NEXT?

20 © 84.51° 2015 | Confidential 20 AFTER TRAINING, EACH ANALYST CAN SCORE THEMSELVES ACROSS THE ANALYST DNA SKILLS ANALYST DNA: CURRENT SKILL LEVEL ALL COMPANYGENERAL ANALYSIS DATA SCIENCEMETHODOLOGYPRODUCT ENGINEERING BUSINESS

21 © 84.51° 2015 | Confidential 21 ON-GOING SCORING IS USED TO GUIDE WHERE WE BUILD OR BUY THE NEXT TRAINING These gaps prioritize where we invest in intermediate and advanced training ANALYST DNA: CURRENT VS DESIRED SKILL LEVEL ALL COMPANYGENERAL ANALYSIS METHODOLOGYPRODUCT ENGINEERING BUSINESSDATA SCIENCE

22 © 84.51° 2015 | Confidential 22 WHAT DOES THIS PROVIDE BACK TO THE BUSINESS? SUPPORT FOR AN ANALYSIS COMMUNITY WITH AN ENDLESS APPETITE TO LEARN AND GROW We love to learn. We should always be learning new things. Analysts have a voice in what training we build or buy next. CONSISTENT, WELL-TRAINED ANALYSIS TEAMS If Analysts don’t come in with skills, we build them up to do analysis how we do analysis. As best practices are updated, everyone is informed and new analysts are trained on the latest and greatest. TIME EFFICIENCY A regular training course cycle and available recorded sessions frees up time spent doing 1:1 trainings.

23 THANK YOU!