Barnstorming the Silos! a Call to Action for Workforce Development Department Executives in Education

By Basham, Matthew J.; Rosales, Rachel | The Catalyst, Spring 2011 | Go to article overview

Barnstorming the Silos! a Call to Action for Workforce Development Department Executives in Education


Basham, Matthew J., Rosales, Rachel, The Catalyst


At the 2010 Community College Futures Assembly Frank Chong, Deputy Assistant Secretary for Community Colleges of the United States Department of Education, discussed the need for "eroding the silos" in the various departments and levels of education in order to become more efficient. Nowhere should this be more apparent than in the workforce and economic departments of community colleges. In this paper barnstorming, as with World War I pilots, refers to identification of the key critical issues (the targets), which exist between the silos to be targeted as being problematic, and thusly eradicated. This article will discuss some of those silos in the Departments of Labor, Commerce, and Education, that need to be "barnstormed" in order to operate more efficiently as workforce entities.

Eroding the Silo between the Department of Commerce and Labor

Inevitably during discussions of better use of data in decision-making some mention of mistrust of the data surfaces. There are very few reasons to actually trust in the data. However, as workforce agents we must rely upon the data, both from the Department of Labor and the Department of Commerce to form the logical basis for application to training funds. Therein lies the point of contention for inclusion here in this article for discussion. In a logical world we could trust our data. In order to learn to trust our data we must have some sort of demonstrated "variance factor" to authenticate the validity of that data. At this point in time we do not have this variance factor. We could, however, create one to allow us better trust in the data. For example, periodically we receive reports on occupational outlooks and projects over a future period. For the sake of argument let's say we are receiving projected employment data from 2008-2018. When we dig into the data we find there to be 100 projected openings in our immediate area per year for nursing, psychiatric, and home health aides (SOC code 311000). We may think it would be appropriate to write a grant to fill those openings. We have no way of know if the 100 positions are "real" or may even be exaggerated. However, we can create a variance factor (V) based upon past projections (P) and employment data (E).

This checksum formula could be used to assess the validity of the data on a periodic basis (monthly, quarterly, annually, etc.) to produce the trust we all require in our data. Let's see an example of this using fictitious data (see Table 1). If we were to apply the formula

Using this methodology we can see historically our data has only been about 71 % accurate over the past ten years, meaning we had a historical variance of 29%. Therefore, if a report is read in the near future which calls for 100 openings we can apply this information to really "trust" the 100 openings "really" means about 71 openings, which does not include those in the unemployment database. Having this information will better allow us to more accurately predict how many should be trained when applying for grants. In this fashion we do not over train and thus unintentionally misallocate funds, which could be used better elsewhere.

Implementation

On the surface this seems like a very logical and reasonable plan however, implementing the plan is more difficult than it should be. This brings us back to our discussion on the erosion of silos. Employment projections are estimated through the United States Department of Labor, Bureau of Labor data. This data is arranged according to Standard Occupational Classification (SOC) codes:

Major group 11: Management Occupations; 13: Business and Financial Operations Occupations; 15: Computer and Mathematical Occupations; 17: Architecture and Engineering Occupations; 19: Life, Physical and Social Science Occupations; 21; Community and Social Service Occupations; 23: Legal Occupations: 25: Education, Training, and Library Occupations; 27: Arts, Design, Entertainment, Sports, and Media; 29: Healthcare Practitioners and Technical Occupations; 31: Healthcare Support Occupations; 33: Protective Service Occupations: 35: Food Preparation and Serving Related; 37: Building and Grounds Cleaning; 39: Personal Care and Service Occupations; 41 : Sales and Related Occupations; 43: Office and Administrative Support Occupations; 45: Farming, Fishing, and Forestry Occupations; 47: Construction and Extraction Occupations; 49: Installation, Maintenance, and Repair; 51: Production Occupations; 53: Transportation and Material Moving Occupations; and 55: Military Specific Occupations .

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