Grow the business and cut costs — that’s the mandate most business leaders face today. For talent managers seeking to enable that mandate, the challenge can seem almost impossible, especially in an era characterized by shortages in critical skills. They play a game of 3-D chess, seeking to build an agile roster of full-time employees with a variety of contingent talent, while trying to hold costs in check. Experience and intuition have long played a role in crafting a workforce, but in increasingly complex and competitive environments, most managers want better tools and information at their disposal, such as data analytics.

In recent years, efforts to harness the power of big data across the extended workforce landscape have led to greater transparency into the going rate of talent. Data has been aggregated and shared, enabling many companies to better optimize the cost, productivity and risk of their total talent transformation efforts. By embracing big data tools, these companies have increased their confidence that the contingent workforce they have today has the skills they need at the true market price while simultaneously increasing their bottom lines.

But what about tomorrow? The full promise of big data and the various tools/platforms it spawns has always been that of prediction. With predictive analytics, talent managers truly would be able to “see around corners” and build a workforce that balances the need for strategic skills with unforgiving cost constraints out into the future.

With a critical mass of data and varied, honed predictive models, it is now possible to forecast the cost of contingent labor as far as 12 months into the future, enabling engagement managers and business leaders to consider the impact that their sourcing strategies will have on costs, budget and strategic outcomes. With such data in hand, contingent workforce program owners can establish realistic service level agreements with suppliers and MSPs to better optimize their contingent workforce.

The likelihood of filling specific roles within a certain period of time based upon considerations that account for multiple factors, such as job titles, geographies, must-have skills, role attributes and rate parameters also can be forecasted. In creating a score that depicts how probable it is that each job role currently being sourced — individually and rolled up by internal project/business unit — can be filled under various scenarios and factors, program managers can make the strategic choices and considerations necessary to drive the results they need.

As talent management continues to ascend from the transactional model of filling requisitions one-by-one — based upon the needs of the day — toward a strategic practice in which the total workforce is positioned to meet the challenges of a fluid future state, leaders expect their strategic advisors to possess a deep understanding of the total talent landscape and to build data-driven business cases for their program optimization efforts.

Predictive tools that help to frame the trade-offs between price, location, and skillset better than ever before will be an important input to these efforts and a critical step toward making true talent optimization a reality.