Big Data Analysis Drives Not-for-Profit Performance: Gulf Coast-Based CareerEdge, a Workforce Development Not-for-Profit, Must Examine Its Economic Impact on the Community It Serves to Garner Funding

Article excerpt

CareerEdge Funders Collaborative was piloting a new approach to workforce development, so it wanted hard evidence of its progress to engage funders and respond to skeptics. The collaborative engaged measurement experts Capital Analytics to help execute a credible, quantitative Big Data analysis of the economic impact of its programs, which was positively received by partners and funding resources.


The opportunity

Many not-for-profits shy away from financially quantifying the impact of their programs. But increasingly, funders are becoming more discerning in how and where they grant resources. To create a strong competitive position and secure continued or additional funding, savvy not-for-profits understand that they must progress beyond basic measurements and demonstrate financial impact from investor dollars.

CareerEdge Funders Collaborative is an innovative, Florida-based partnership of business, civic, and philanthropic organizations that uses public and private dollars to help employers in the Gulf Coast area fill their workforce needs, as well as help employees and job seekers gain new skills.

Through extensive collaboration, CareerEdge and employers identify worker skills gaps and develop training programs to close them. This approach is a shift from traditional state-run workforce development efforts and, therefore, scrutinized by funders and policymakers.

It is in this environment that CareerEdge leadership recognized the critical importance of an objective evaluation of its programs. Within months of its startup, CareerEdge engaged Capital Analytics as a third-party evaluator to qualitatively and quantitatively assess its activities.

The solution

Traditionally, funding sources for the workforce development arena evaluated grant performance using metrics such as number of individuals served or training hours delivered. But CareerEdge wanted to assess the economic impact its programs were having on the communities it serves. As a result, it sought out the Big Data expertise of Capital Analytics to develop and execute its Economic Impact Study, a multiyear evaluation plan.

The evaluation needed to examine and inform stakeholders of progress toward CareerEdge's four goals:

* provide workers with the skills they need to keep up with the evolving needs and requirements of their employers and advance in their careers

* move low-wage workers into higher paying jobs

* provide employers with the skilled employees they need

* create system change regarding partnerships between business and community-based organizations, as well as public policy around workforce development.

Unlike many studies on return-on-investment that focus on a single program within a single organization, this one had multiple stakeholders and sought to determine the impact of the CareerEdge grants on workers' lives and on the Gulf Coast regional economy. Using the data collected, along with forecasting methods, the analysis needed to consider both tangible (financial) and intangible (employee and employer confidence) effects on the region.

The process

The project began with a series of meetings with key stakeholders--from employers and educators to funders and social services providers. Each group shared their own vision of success for this new approach to workforce development, and outlined specific metrics for how to measure program achievements.

Next, the Data Collection Toolkit was developed to guide each of the partners on the information they were to collect. The tool kit also outlined quarterly collection deadlines, and discussed employer participation in semistructured qualitative interviews.

Data collection templates were sent to the data-providing partners on a quarterly basis. Individual-level data were aggregated by employer and industry sector, and included wages, raises, promotions, performance, and turnover. …


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