Data, Analytics, and Improving Program Development & Employer Engagement Kevin Long – Director of Planning and Policy, Montgomery College, MD Rob Sentz – Chief Innovation Officer, Emsi Who here uses or is interested in using labor market data in their group? Who here knows Emsi or has worked with Emsi in the past?

Outline Data Background Data Methodology / Application Best Practice Q/A How to think about data and apply it more courageously and confidently

Premise: Colleges are a big part of the economic development of the region Show how data collected from a variety of sources (traditional, job postings, and resumes) can help colleges make some very strategic moves to improve how students are engaged, programs are developed, and employers are sought after.

Why? Aligned Economic Ecosystems REGIONAL ECONOMY People Supply Demand Higher Ed Businesses

How? How should we look at this work? Regional Industries Resumes / profiles (People) Curriculum (Higher Ed) Job Postings (Businesses)

What? Labor Market Dynamics Supply Locations Employers Jobs Skills Local Labor Market Total employment Growth / Change Compensation Demographics Education and Training Courses Learning outcomes Skills/Knowledge Demand Locations Employers Jobs Skills

Reviewing the Data Labor Market Data – Structured, Administrative, Taxonomic Industries (growth, jobs, GRP) Occupations (compensation, totals) Demographics (age, race, ethnicity, gender) Postings – Unstructured, Unregulated, Web-Scrapped Titles Skills Resumes – Unstructured, Unregulated, Web-Scrapped Employer Title Education Higher Ed - Slightly structured, somewhat regulated, sorta administrative (but more web- scrapped) Programs Courses

Reviewing the Data LMI – Strengths LMI – Weaknesses Taxonomy Standardization Coverage Compensation LMI – Weaknesses Relevancy Taxonomy Aggregation Timeliness Postings – Strengths Specific employers Current demand Skills and titles Postings – Weaknesses Collection / Validation of #s Bias Coverage Company behavior

Reviewing the Data Resumes – Strengths Timely Locations Employers Schools Skills Resumes – Weaknesses Self-reported Tagging Ed Data – Weaknesses Methodology for creating it Not standardized Ed Data – Strengths Learning outcomes Skills and knowledge coming from ed Pipeline

Resume – Data Scientist, Amazon

Job Posting – Data Scientist, Amazon

A Workflow for the Data The narrative that colleges should follow as they use data

1. Economic Context Industry / Labor Market Perspective Step 1: Know the community drivers What really drives the economy? What is worth your attention? What brings in money? Exports What creates the types of careers that the college can really get behind What will create broader community prosperity? How is the college engaged?

2. Program Development Occupation, Job Title, Skills Perspective Step 2: Focus on the knowledge and skills the community needs Move from industry to occupation/job title Decifer marketable skills (that sit on top of a well-rounded education) Validating skills and careers against employer need

3. Career Insight Resumes, Programs, Career Opportunities Step 3: Helping students have a (data-driven)vision Cannot build a program without enrolling it Create mappings between programs and outcomes Employer outreach / making sure employers know what is up and that the college is doing the right things

4. Outcomes and Impact Labor market outcomes of students, impact of institution Step 4: Look at the Results Labor market outcomes for students Satisfaction of employers Economic impact on the community Talk about it and share it with Students Employers The community

Best Practices at Montgomery College Sensing big trends, measuring impact, and reaching out to students and employers

Labor Market Sensemaking - Opportunities Splunk was offering free software, instructor training, and certification testing vouchers for students Question: Is there a local or regional demand to make this worthwhile? EMSI Job Posting Analytics 275% increase in desire for splunk skills in the last 2 years Companies now paying to send their employees to receive training and certification through CPAM

Labor Market Sensemaking - Curriculum Development Knew there was a demand for big data and data science analytics skills Question: How do we determine the most relevant skills and demonstrate need? How do we match our offerings? EMSI Job Posting Analytics and Analyst Deep dive into data analytics skills – mathematical, coding, visual, technical WD&CE – hit the ground running with a repackaging of offerings and new courses Credit – developed certificate curriculum which was just approved by MHEC and will start in the Spring

Measuring and Making an Impact We assume we are having an impact on our students’ future career and earnings. Question: How do we demonstrate this without relying on traditional alumni surveys? MC became an early adopter of Emsi’s Alumni Insight Leveraging social media big data to map student employment outcomes Currently tracking alumni by job title and mapping back to program Also mapping by company Alumni Affairs – mentoring programs and alumni engagement

Reaching out to Students and the Community We know that students will make better decisions if given clear information. Question: How do we get educational and occupational information to them? MC’s Career Coach Started as a marketing tool but has since transitioned into something bigger Pathway planning Used by MC Career Services, ACES Coaches, MCPS, and Universities at Shady Grove Big push into local non-profit community

Questions Rob Sentz 208.301.3604 rob@economicmodeling.com Kevin Long