1. 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?

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

3. 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.

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

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

6. 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

7. 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

8. 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

9. 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

10. Resume – Data Scientist, Amazon

11. Job Posting – Data Scientist, Amazon

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

13. 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?

14. 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

15. 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

16. 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

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

18. 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

19. 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

20. 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

21. 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

22. Questions
Rob Sentz
Kevin Long