To get the most out of any dataset, you first must have a clear sense of what you want to accomplish with it. What questions are you trying to answer? What problems are you trying to solve?
This was one of the points Todd Oldham of Monroe Community College emphasized in an Emsi webinar this week on using labor market intelligence to inform educational program decisions and bolster grant narrative.
Oldham, the VP of Economic and Workforce Development at MCC, laid out the innovate framework that the Rochester, New York, college has developed to estimate workforce supply and demand. MCC’s model includes engaging with businesses through surveys, taking a deep dive into local labor market data, and using Emsi’s program-specific economic impact study to show the value to the local economy of filling skills gaps. He also gave helpful tips and suggestions—as well as technical observations about using LMI.
The work Monroe has done to understand middle-skill labor demand and take action to help regional employers is thorough and impressive. Yet as Oldham said, the college started simply, with a clear set of questions, and built on its framework over time. Oldham doesn’t have a huge team—just two full-time staffers in addition to himself. But Monroe has a strong track record of investing in grants management, economic and workforce development, and marketing. That investment has paid off in the form of developing programs that are attuned to labor market needs, winning grants that support those programs and help share data regionally, and becoming a workforce development leader in the Finger Lakes region.
Some of Oldham’s tips?
- Begin with a clear sense of what you want to understand and accomplish with labor market intelligence
- Be reasonable and allow yourself a learning curve
- Start small with a manageable dataset
- Get buy-in and industry involvement
- Think about surveys and the role of potential partnerships in your community
- Tools like Analyst are very useful and efficient once you have your questions defined
- Workforce data is messy and layered—don’t expect a black box model that is predictive. Goal is estimation and approximation
Emsi has profiled Monroe’s work in the following case studies:
- New York’s Monroe Community College Takes Steps to Identify and Address Regional Skills Gap
- Monroe Community College Measures Middle-Skill Gaps and the Economic Return of Filling Them
For more information on how Emsi can help your institution with data analysis, business surveys, and economic impact assessments, please contact us. Follow Emsi on Twitter (@DesktopEcon) or check us out on LinkedIn and Facebook.