Discover New Talent Pipelines by Radically Reskilling Your Teams: A Case Study with Booz Allen Hamilton. March 2018

Transcription

1 Discover New Talent Pipelines by Radically Reskilling Your Teams: A Case Study with Booz Allen Hamilton March 2018

2 Agenda Introductions Trends in the Marketplace General Assembly Advanced Skills Academies Booz Allen Data Science 5K Building an Academy Q&A PAGE 2

3 Meet the team Adi Hanash Head of Product, Advanced Skills Academies Jay Nappy Head of Operations, Advanced Skills Academies Jim Hemgen Senior Associate, Functional Learning & Development Cadence Weber Learning Architect, Functional Learning & Development PAGE 3

4 Trends in the marketplace

5 There are major opportunities to innovate in talent acquisition and workforce development Companies are spending more on recruitment than ever before. Digital transformation is rapidly changing the job market. Poaching talent does not increase overall supply. RECENT HEADLINES: PAGE 5

6 General Assembly Advanced Skills Academies

7 Solving Skills Gaps through Training, not Recruiting Upskilling Radical Reskilling Talent Pipeline as a Service (TPaaS) Adding critical, in demand digital skills to your teams in order to make them more effective and impactful Retraining legacy workers to take on new digital roles crucial to the success of your organization Creating new pipelines of talent by investing in the training and onboarding of candidates from a wider, more diverse talent pool PAGE 7

8 4 Key Components of an Advanced Skills Academy Each GA Advanced Skills Academy has 4 critical components that can be managed by GA, in partnership with GA, or handled internally depending on your organizational needs. Marketing Admissions Training Outcomes Robust marketing to source new pools of qualified talent that fit the profile for the position(s) for which we are sourcing and training. GA s data-driven admissions processes uses a combination of Assessments and screening interviews to vet candidates and assess their readiness for training. GA s world-class training leverages our wide network of top practitioners, blended learning models, and assessment-led learning paths. Our outcomes team or career coaches work with our students throughout the program to develop soft-skills and properly prepare them for any interview process. PAGE 8

9 Key Milestones for Launching an Academy To successfully launch an Advanced Skills Academy, General Assembly conducts a thorough research phase that allows us to determine how to contextualize our material to your organization and tailor our MATO approach to your needs. Research Marketing and Admissions Training Outcomes Stakeholder Interviews and Assessment Deployment Source and Vet Participants Blended Learning Approach Internal Placement and Role Transition GA conducts stakeholder interviews in order to contextualize the content of the courses to your organization and rolls out assessments to establish internal benchmarks. Timeline: 2-6 weeks In-depth marketing period designed to identify the Optimal Profile for candidates for training. The admissions process will use Assessments and Screening to vet candidates. Timeline: ~10 weeks GA s expert instructional team will train candidates through a combination of assessments, blended learning paths, and instructor-led delivery. Timeline: 1-12 weeks depending on training outcomes Throughout the training process, GA s support team will also work with candidates on soft skills and prepare them for any interview process post-course. Timeline: 2-4 weeks PAGE 9

10 Key Measuring Milestones ROI of for Academy Launching Training an Academy To successfully launch an Advanced Skills Academy, General Assembly conducts a thorough research phase that allows us to determine how to contextualize our material to your organization and tailor our MATO approach to your needs. Participation Assessments Capstone Projects Business Objectives Research Marketing and Admissions Training Outcomes Stakeholder Interviews and Assessment Deployment Source and Vet Participants Blended Learning Approach Internal Placement and Role Transition Metrics Tracked Monthly active users (online) # of participants # of graduates GA conducts stakeholder Daily/weekly interviews in course order to surveys contextualize Net Promoter the Score content of Value the courses for time to spent your organization and rolls out assessments to establish internal benchmarks. Timeline: 2-6 weeks Metrics Tracked Industry-benchmarked assessments (when applicable) In-depth marketing period designed Internal to identify benchmarking the Optimal with Profile assessments for candidates for training. Pre/Post The admissions Assessment process will performance use Assessments and Screening to vet candidates. Timeline: ~10 weeks Metrics Tracked Capstone projects scores based on pre-defined rubric GA s Final presentations and expert instructional team will train awards candidates assigned through by senior a combination leadership of assessments, blended learning paths, and instructor-led delivery. Timeline: 1-12 weeks depending on training outcomes Metrics Tracked Post-training performance tracking (dependent on internal systems) Throughout the training process, GA s KPIs support determined team will prior also work to with candidates training with on GA soft skills and prepare E.g., them Agile for team any interview velocity process pre/post post-course. training Timeline: 2-4 weeks SOFTWARE DEVELOPMENT DATA DESIGN PRODUCT MANAGEMENT DIGITAL MARKETING CYBERSECURITY PAGE

11 Booz Allen Hamilton Data Science 5K

12 Booz Allen Hamilton at a Glance Founded in offices worldwide 25,000+ staff members 43% of staff work in analytics roles 400+ of Fortune 500 as clients 5+ USD billion in revenue 15th largest contractor for US gov. PAGE 12

13 What is Data Science? Data Science is the art of turning data into actions - The Field Guide to Data Science Legacy Analytics Data Science Human/Machines Humans Machines Type of Analysis Descriptive Predictive Domain Expertise Provide the understanding of the reality in which a problem space exists Data source Siloed Data Warehouses Distributed Data Streams Tools Commercial, Off-The Shelf Open source tools Computer Science Analytical Skills Outputs Reports Data Products Follow-up Repeat Analysis Probe Deeper Provides the environment in which data products are created Operations, research, and mathematics PAGE 13

14 Booz Allen s Data Science Journey Formalize Data Science practice at Booz Allen. Explore Data Science made available to the public Launched the Data Science Bowl and wrote and released the Field Guide to Data Science Initiate The Data Science 5K, plan to train 5,000 data PAGE 14

15 Booz Allen s Data Science 5K Challenge Goal and Mission Statement Be the industry leader in Data Science. Everyone from the skilled data scientists to a novice who has yet to write their first lines of code must infuse Data Science principles into their work to capture the opportunity of today s technology transformation. We passionately believe that Data Science is core to everything we do at Booz Allen, and that most everyone can employ that tradecraft. With this in mind, we launched the Data Science 5K Challenge, our charge to increase our data science footprint with 5,000 data scientists. PAGE 15

16 Booz Allen s Metrics to Quantify Program Success Attract, Reskill, Retrain Data Science Criteria Metrics Project Value, Size Billability Direct Labor Value Demand vs. Vacancies Open Reqs Avg Time to Close Open Reqs Internal:External Fill Ratio for Closed Reqs Retention Rates Retention Rate, Avg. Retention Rate (Firm-wide vs. Analytics group) Supply vs. Capacity Staff Aligned Aligned to Analytics Staff Aligned to Data Science Role PAGE 16

17 Why did Booz Allen Hamilton Partner with General Assembly? Multiple companies submitted proposals, and we asked an internal panel to evaluate each company on the company s ability to execute on four criteria: 1. Engagement 2. Assessment 3. Programming 4. Business Applicant communications Student communications Assessment library Demonstration of skill mastery Post-course evaluation Capacity planning Instructor/student ratios Learning delivery methods Course customization Global scale Business compatibility Stakeholder management PAGE 17

18 General Assembly Stood Out 1. Engagement 2. Assessment 3. Programming 4. Business MyGA.com Engagement Manager Data Analysis, Level 1 Data Science, Level 1 Company-wide diagnostic Part-time Data Science Remote/in-person options Dataset customizations Location delivery flexibility Consulting familiarity GA s client list PAGE 18

19 Building an Academy

20 The Framework We Used Develop a Vision for Your Initiative Understand Current Capabilities Determine the Path Forward Pilot, Refine, and Grow Key Activity: Define the Goals & Objectives Key Activities: Partner with Business Leaders & SMEs to Determine What Capabilities are Needed Key Activity: Build vs. Buy Key Activities: Develop Workstreams, Pilot Program, Collect Metrics PAGE 20

21 Step 1 - Develop a Vision Objective Behavior Mindset Knowledge Skill PAGE 21

22 Step 2 - Understand Current Capabilities Partner with Experts Assess Skill Gap Determine Solution Experts should be: Diverse Dispersed Dedicated Don t: Rely on a gut Pre-determine a solution Do: Gather hard data Utilize third party data Key Questions: Is there a skill gap? Build vs. Buy? PAGE 22

23 Step 3 - Determine a Path Forward Identify internal experts, develop workstreams, and take advantage! Communications Performance Financial Comms plans Program awareness Building buy-in Staff deployment Content expertise Evaluation measures Inform learning roadmap Model program costs Identify funding source(s) Analyze impact Participant billing policies PAGE 23

24 Step 4 - Pilot, Refine, and Grow 2. Pre-work online (Data 1. Assess online (DA1/DS1) Fundamental) 3. Upskill in class (Data Science) PAGE 24

25 Step 4 - Pilot, Refine, and Grow Criteria Description Performance Indicator Learning Engagement Application Drastic improvements in skills World class learning experience Participants recognize value Participants thrived with the instructional support provided High conversion rate from interest to engaged High, steady rate of collaboration between students and staff Demonstrate python competency through code challenges Perform exploratory data analysis in Python on a data set Create, train, evaluate model fit for regression/knn models Create a proposal, find/clean data, model data, present findings +50% increase in post-course assessment scores 51 NPS 4.5 VTS Qualitative feedback >1600 Yammer sign-ups; 930 assessment submissions 16,719 Slack messages sent since June Project 1-100% students met expectations Project 2-100% students met expectations Project 3-100% students met expectations Project 4-100% students met expectations PAGE 25

26 Step 4 - Pilot, Refine, and Grow Area How should it change? Why should it change? Delivery Format Remove accelerated offering; offer part-time, 10-week, or part-time, 8-week, only Qualitative feedback from accelerated courses frequently cited pacing as an issue (too fast) Staffing Employ full-time instructors that teach multiple cohorts Reduce instructor prep-time required by 50%; allow for more access to instructors and more office hours Pre-course Experience Provide pre-course installfest, virtual office hours for all Pilot pre-course VTS 16% lower than in-course; qualitative feedback showed students felt unprepared for class Final Project Introduce final project earlier in class Qualitative feedback indicated students wanted more time to work on final projects Advanced Classes Offer advanced classes in specific, focused data science topics Engage participants from pilots and analysts too advanced for DS Fundamentals End-to-end Experience Be more explicit about pre-, in-, and post-course expectations Qualitative feedback indicated students were unclear about postcourse support, expected abilities post-/pre-course PAGE 26

27 Step 4 - Pilot, Refine, and Grow Students: 125 Students: 1000 Classes: 5 Classes: 47 Locations: 4 Locations: 16 PAGE 27

28 The Participant Learning Journey Join Internal Program Hub Skills Assessment Registration Period Pre-Work Classroom Learning Project Work Post- Assessment Participants join Booz Allen s internal messaging platform for access to all information and updates on the learner journey Participant partakes in relevant skills assessment (DA1 and DS1) to determine current capability level and future learning path Based on interest and location/ objectives/workstream, the participant enrolls in a program that matches their needs Participant undertakes self-paced online modular content as pre-work in order to prepare for in-class training Participant attends inperson classroom sessions. Selection of two formats (2 nights per week over 10 weeks or 8 weeks one full day per week) Participants showcase their work in Capstone projects. Career managers and senior executives in attendance Participant takes post assessment to quantify the knowledge uplift PAGE 28

29 Booz Allen s Return on Investment Impact On Work: Sarah, Associate Demonstrated simplicity of performing repetitive, predictable analysis in Python for her client. Brad, Lead Associate Implemented the usage of Python and SQL at his client site and stood up a data science practice Impact on Clients: Started the discussion about the type of analysis that open-source software can perform. The client moved forward with standing up a robust architecture to support predictive services. Client excited by quicker turnaround times and increased data processing efficiency. PAGE 29

30 Q&A