"This type of information can help us keep ahead of turnover," Schutt says, adding that knowing the demographics of the rest of the workforce in the area can help HR trend employee turnover and migration. Ideally this information can be used to identify and remedy employee dissatisfiers (such as, mandatory call or uncompetitive pay).
"Those are key pieces of data you need to do strategic workforce planning, and it's data that's been available but wasn't accessed because no one knew how to get at it," he says.
For instance, when 885-bed Stanford University Medical Center decided to add more nurses to the 2,700 registered nurses it currently employs, Schutt used GIS to learn more about the nurses already working at organization as well as those in the area. Using the combined internal and external analytics on the workforce data, the leaders could see a dot-map of details about the staff. The map indicated where pools of nurses with the right skill set were located and it also showed the system's current nursing supply and licensure levels, and other key recruiting details such as employee commuting patterns and distance traveled to work.
"The data showed us that on average nurses at Stanford lived within 12 miles of the hospital," says Schutt. "It also showed us that a large number of nurses would be nearing retirement."
Not only would the health system need to fill new openings but it may need to fill many more in the near future, explains Schutt, a 20-year human resources veteran who has worked for organizations such as Nortel, HP, and Kaiser Permanente. The GIS map included regional nurse geo-analytics, so HR could pinpoint where to look for new recruits.