Today, few organizations use data effectively to select their workforce. Why? It's not because they lack the required skills to analyze the issues at hand. Rather, it's because they don't have the right data—or, if the data exists, it's too difficult to get at it. Even the most skilled of analysts cannot make fact-based workforce decisions in a vacuum.

The solution? A whole new field, called workforce analytics, is now emerging.

Matching people with the challenges ahead
IBM used a diagnostic tool to help project the impact of aging on a workforce and to take action to manage it. In one instance, consultants were able to evaluate and distill the workforce aging problem down to less than 200 out of 3,000 workers, greatly reducing the cost and complexity of managing it.

Better data = better workforce planning
A medium-sized healthcare company needed to reorganize its project-based research and development workforce to meet growing demand. The company wanted a consistent, automated demand forecasting process for faster decision making and scenario modeling. This required a more data-driven approach.

The solution:
The firm started by classifying employees by skill type and developing roles based on various skill categories. Employees were then identified by role, and employee allocations were tracked by project. A more structured, data-driven approach enabled the company to forecast needs for permanent, contractor and outsourced work.

The benefits:

• Brought more rigor to workforce planning
• Equipped resource managers with the data to assign work to individuals with the appropriate skill sets
• Employees are now deployed much more effectively by explicitly estimating workforce peaks and troughs and assigning new project work that fits with availability and expertise
• Reduced costs through a better allocation of work

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