A database tool that helps find jobs for inner-city youth? Everybody can get behind that.
Since 1999, The Philadelphia Youth Network (PYN) has developed targeted programs that allow early access to educational and employment services for underserved youth in the region. PYN has grown to one of the city’s most successful sources of jobs and career opportunities, helping to connect 126,000 young people since it was founded. It does this by pulling together constantly changing data about jobs, youth programs, service learning, internships, work experience and career exposure from dozens of organizations and hundreds of businesses.
You can’t make that kind of impact if all you’re using is spreadsheets.
A data-driven non-profit
PYN uses Toad Data Point to query and extract data stored in Oracle, SQL Server and MySQL databases. It also uses Toad to do the work of cleaning, formatting and merging data, freeing up staff to focus on the work of helping young people start a career.
Michael Pompey, CIO of PYN, points to the automation and forecasting in Toad as the features that have taken his team far beyond the spreadsheet days they were accustomed to. That and Toad’s SMS feature are helping to keep PYN’s staff in the field, meeting with young people in under served neighborhoods, which is where they do the most good. “If you’ve got folks whose job and responsibility it is to be out in the field working with youth,” says Pompey, “the last thing you want is to be dragging them in house and sitting them around the table to look at a spreadsheet, right?”
As PYN executives have been able to obtain and use information so quickly, they have grown the organization into a data-driven non-profit. More important than that, though, Toad has helped PYN pinpoint under served areas in the city more quickly, target low-income teens and give more of them their first break in the job market.
What’s not to like about that?
More on data preparation best practices
Have a look at the white paper, "Easing the Data Preparation Challenge" for best practices to overcome common big data barriers to success. We explore the five laws of data preparation, show the problems with traditional data integration workflows and offer a better approach to ensure quality analytics in your organization.