Capitalizing on the New Data Analytics/BI of Contingent Workforce Management: Five Strategic Pillars
75 percent of business executives in a recent report by Deloitte identified talent analytics as an important issue. However, only eight percent believe their organizations are strong in this area.
Much of the data that is used in the industry is retrospective and the analysis is largely descriptive. The result is program analytics that are 30,000 feet up and 1,000 miles behind.
Less than 18 percent of businesses feel they have the in-house skills to gather and use data insights and apply them.
Organizations without the right contingent workforce management data analytics and BI strategy are at a competitive disadvantage, incurring additional cost and assuming unidentified risks.
The growth of digital information over the past decade is almost unfathomable. Ninety percent of all existing data has been created in the past two years, with current trends and forecasts showing a sharp growth trajectory.
The logical outtake is that organizations have the ability to transform business decision making by tapping into this immense reservoir of data. Better data yields better decisions, which generate better results.
Yet many businesses are challenged by the complexity and breadth of the data and corresponding analytics, becoming stuck in trying to decipher all that is possible. As a result, discovering real business opportunities and mapping desired outcomes have proven elusive for many organizations. Indeed, more than 80 percent of all data is never used for business intelligence (BI).
Sales and marketing functions are two areas where data analytics have produced tangible outcomes in recent years. Workforce management is an area where there is a substantial value proposition for data analytics and BI. Business executives certainly recognize the potential: 75 percent in a recent report by Deloitte identified talent analytics as an important issue. However, only eight percent believe their organizations are strong in this area.
A subset of the broader workforce, contingent labor is still a nascent space for many organizations when it comes to data analytics and BI. Much of the data that is used in the industry is retrospective and the analysis is largely descriptive. The result is program analytics that are 30,000 feet up and 1,000 miles behind.
Important Ingredients for Success
Early on, PRO Unlimited recognized the potential impact robust, real-time data analytics could have on decision-making for companies. We felt our on-the-ground experience with existing clients needed to serve as a critical lynchpin in our approach to data analytics and BI.
Our purely vendor-neutral supplier management model— which creates a level playing field wherein suppliers succeed based on true performance—also paves the way for us to deliver fully objective findings and recommendations. In addition, because we have an integrated solution that entails both services (MSP: managed services provider) and technology (VMS: vendor management system), we possess the expertise and tools to collect, store, and analyze the data.
Five Data Analytics and BI Pillars
Five important pillars comprise our highly successful and effective approach to data analytics and BI. These are applicable across any number of different use-case scenarios and industries.
Humans and technology. Intelligent-actionable data insights require both technology and human components. As less than 18 percent of businesses feel they have the in-house skills to gather and use data insights and apply them, many look to outside assistance for help. These are not data scientists, but rather subject-matter experts who provide in-depth analysis, insights, and consultation for specific business functions.
At PRO, our Strategy, Analytics, and Metrics (SAM) team— which is part of our Strategic Planning organization—works data analytics, drive solutions, and design and modify programs to address the relevant insights. This integrated and consultative approach enables customers—who are not staffed with experts on data analytics—to turn nascent BI into tangible business outcomes.
Hybrid data approach. Better business intelligence is achieved when historical and contextual data sets are combined for analysis. The best of these data sets also includes both internal and external sources. A hybrid data model collects information from multiple sources, which is leveraged to generate actionable BI findings and recommendations. It also employs analytics based on real-time transactional data that increases the velocity of decision-making while improving the overall effectiveness and relevancy of the insights.
Power in the hands of the users. Complex data analytics and BI in the hands of a few chosen is a model rooted in the past. Rather, simplified, self-service advanced analytics in the hands of business users is the way of the future. In this scenario, both tactical- and strategic-based analytics and BIare designed for ease of use and overlaid with a human component for navigational support and interpretive guidance.
Strategic and tactical decisions. Analytics should be used for tactical insights that produce real-time business changes as well as strategic workforce planning. Both are equally important.
An example of tactical insights is our market rate module that is one of the capabilities within our Wand VMS platform. Here, managers requesting a resource or statement-of-work (SOW) project are informed of what the market will bear for the type of resource or project they are requesting. They also receive a bill rate range from low to high for that specific resource or project in the geographic location where the work will be performed.
Our Wand Discovery BI module, which provides users with myriad data sets displayed in actionable visualizations, is an example where our analytics assume a more strategic element. Configurable based on customer requirements, Wand Discovery provides data insights into operational areas such as bill rate trends across locations, skill sets and assignment types, departments, and many more. Our SAM team collaborates with the customer to generate reports that align with business requirements, as well as to help interpret and apply the BI.
Descriptive and predictive. Analytics that simply describe the current contingent labor landscape are inadequate. Rather, they also must be predictive. For example, the data we collect is used in Wand to benchmark bill rates based on location, skill sets and assignment type, among other factors. These descriptive analytics are turned into predictive analytics through sourcing recommendations during the recruiting process.
Capitalizing on the new business opportunities
Contingent labor is becoming what has been dubbed the new on-demand workforce. It currently comprises nearly 20 percent of the U.S. workforce, and projections place it at 30 to 40 percent in just a few years. Having the right data analytics and BI approach in place becomes even more critical with this trajectory.
Organizations that fail to do so will be at a competitive disadvantage, incurring additional cost and assuming unidentified risks to their brand and business. They also will be unable to capitalize fully on the emergence of the new on-demand workforce, lacking the agility and speed needed to address the business opportunities that come with it.
It truly is an exciting time to be in the contingent workforce management space, and moreover witness the rise of a new data analytics and BI approach that delivers tangible business results.