Scott Robbin: Elliot, please tell us about your background, your role at Volt Consulting Group, and the services that Volt provides.
Elliot Owens: I’m currently the Vice President of Business Intelligence for Volt Consulting Group. I have more than 19 years of experience working in human capital in the U.S. as well as in Europe and Asia. I’ve been with various divisions of Volt for the last 15 years. I helped launch our staffing operations in the U.K. about 15 years ago. For the past seven years, I’ve focused on our business intelligence offering. I founded the business intelligence group and am responsible for what we believe is an industry-first data warehouse and data-driven business intelligence service offering. I’m a Six Sigma Black Belt and also a recipient of Supply & Demand Chain Executive magazine’s ‘Pros to Know’ award, which recognized some of the groundbreaking work that my team has done related to analytics. Volt Consulting Group is a managed services program provider for contingent labor. We manage the entire supply chain for the procurement of non-employee workers, which include contractors, temporary workers, and project workers on Statements of Work.
How does business intelligence relate to the contingent workforce industry?
Business intelligence has become a hot commodity in today’s marketplace. Everybody wants business intelligence, but most people think of it as providing visibility into data and reports. That’s important, but it’s only one part of business intelligence. When it comes to the contingent workforce, business intelligence involves having the ability to collect data, turn it into information, and then turn that into knowledge. By knowledge, I mean making that data actionable so that companies can make decisions based on the knowledge that they gain from the data.
In the human capital realm, business intelligence means understanding the interplay of factors that affect the procurement of talent. For the contingent workforce, various factors affect costs, quality, and the delivery of workers, as well as risk management and overall customer satisfaction. How do these factors interplay? Business intelligence should help optimize the entire process for sourcing talent, creating win/win situations with suppliers so that you’re getting the best quality talent at competitive prices.
How has the contingent workforce market changed in the past five to 10 years?
Studies have shown that contingent worker, meaning the temporary labor force, acts as a leading indicator of what’s going to happen with overall employment within our economy. When the recession hit, the first people to lose their jobs tended to be temporary workers, and that market was hit very hard. On the other hand, we’re now seeing a strong recovery in the temporary labor market. Although there was a steep decrease in demand and rates for temp labor in 2009, by 2010 and early 2011, the market had pretty much recovered. What we’re now seeing in 2012 is upward pressure. There’s greater demand for temporary labor and there’s pressure on rates as well. I have to caution that our studies have shown that, to some extent, this is dependent on the skills that are required as well as geographical location. For example, light industrial workers were very hard hit by the recession and their recovery has been much more tepid than high-end IT people as well those in the scientific and medical fields, where the recovery has been much stronger. Geographically, the one area of the country that has not recovered as much is the Rust Belt. The recovery in states in the Rust Belt, like Michigan, Ohio, and Illinois, has been much slower than in the rest of the country.
Why has the recovery in the Rust Belt been slower? Is it related to the type of industries that are located in those states?
Yes. The industry as a variable does seem to have a spillover effect on an entire region. What we found in our multi-variate analysis is that the “regional” component has been actually more important than “skill set” element in this recovery. Even high-end IT workers who are being sourced in the Rust Belt are still facing pressure in terms of lower wages and lower bill rates than in other areas of the country.
How have advanced analytics changed the way that your organization manages the contingent workforce of the supply chain?
In short, analytic tools have made us smarter and better providers of our service, which is what they should do. We’ve gotten to the point where we’ve thrown out some of the industry-standard knowledge that was not based on anything except a gut feeling and now have a better understanding of what matters. We’ve been able to develop statistically validated key performance indicators that help us consult with our clients about what is affecting the cost of labor; what is affecting the quality; what is affecting the delivery of workers. By using these data and advanced analytics, we are able to drive better behavior as well as prove statistically some of the facts that we knew as providers and our clients knew as well, but were having a tough time selling internally. The analytics have both validated some of our best practices and changed some behavior from our end as well as from our clients’ end.
What are some other lessons learned from business intelligence that Volt provides?
We know that, particularly since 2010, the demand for temporary workers has increased. The real question from clients is, ‘Am I going to have to pay more now?’ By performing the analytics, we’ve come to understand that the answer is not necessarily ‘yes.’ There are ways that companies can avoid paying more or at least mitigate the upward pressure on rates while maintaining quality and delivery. How do we know this? Because the analytics have pointed to some practices that have been validated statistically to work, helping create more efficiencies and more of a win/win situation with suppliers so that companies can maintain quality while still avoiding having to pay more.
Are there any other surprises in your findings?
The biggest surprise is the fact that quality and price are not necessarily correlated. Everyone thinks they’ve got to pay more to get better quality and the answer is ‘yes, to a certain extent.’ But within certain parameters, the answer is no. You can work smarter and continue to pay what you’re paying while maintaining quality.
How are executives incorporating analytics and business intelligence into their day-to-day functions?
That depends on the corporate culture as well as the individual executive. More and more, we’re seeing executives who use our findings to make decisions, and this amounts to a change in culture. We have credibility and enough proof from a statistical perspective to work with our clients and show them what the numbers mean. This is as close to the truth as we can get. There’s always a probability involved, but these are statistically validated points that we make with our clients. They, in turn, are then able to use those data to make decisions and gain support internally. To give you an example, there’s a common perception, especially among line managers, that only certain suppliers can provide a particular resource or the labor quality that they need. By using hard, cold data, we’re able to help them understand that this may not be the case and that practices such as supplier optimization, where you eliminate some of the suppliers from your business in order to drive more business to better performing suppliers, can actually work. It’s important to be able to sell your decisions internally, and business intelligence helps executives do that.
Have some functions within organizations incorporated business intelligence more so than others?
From what I’ve seen, purchasing departments are the first to adopt it because our business intelligence is about that supply chain. Finance has always been data driven, so companies within that industry are extremely receptive to business intelligence. However, as analytics is increasingly focused on human capital, it is starting to penetrate HR organizations..
What obstacles stand in the way of organizations’ use of analytics?
Culture. Let me give you an example. I’m a big baseball fan, and many people have probably seen the movie or read the book “Moneyball” about the Oakland A’s. Baseball is a very old game, with a lot of preconceived notions about how to measure and scout players. What the A’s General Manager said was, ‘Wait a second. None of these have been proven.’ When the general manager looked at the data, he found that in some cases, he had to throw out so-called 100-year-old wisdom and measure players and prospects differently. The Oakland A’s are now very successful, and a lot of teams have adopted the Moneyball way of doing business to an extent. So the culture of baseball has changed.
A lot of coaches in baseball were very resistant to making those changes. That’s the same with corporations. Organizations have preconceived notions about how things work and it takes time to get change to flow through an organization. But we’ve seen it within our clients, and there are other examples where organizations have taken the analytics and used it to their advantage. I would argue that Wal-Mart or Google built their businesses based on business intelligence.
Can you share any other success stories?
Sure. One of our clients is a large insurance company that has a mature contingent workforce program. A few years ago, they were using our managed services program and were experiencing much lower rates than they had been paying prior to our program going live. But they were still facing extreme pressure to cut costs even further. Our red flag reports showed that certain areas in the managed services program were moving slightly out of line. After conducting a deep-dive analysis of the program’s practices and data, we recommended to the client that we work together to create more commitment and obtain better performance from its suppliers.
We conducted a performance analysis of the client’s suppliers using one of our tools, applied the client’s objectives to the tool along with weightings of various metrics, and identified suppliers that were not adding much value. We recommended that the client phase out those suppliers and move that spend to suppliers that were performing well. The client was able to drive better volume discounts and better commitment from the remaining suppliers. The result was an additional 5 percent decrease in costs, with no loss in quality and a 20 percent improvement in the delivery metrics. The suppliers were providing quality people much faster, because the business intelligence and analytics helped point the way toward creating a win/win situation.
The importance of our analytics is not to just show a client where it’s at or where it needs to go, but also tell the company how to get there. A lot of new clients have this notion that all they need to know is what the rock-bottom competitive price is and then they can get there. In reality, they might not be able to get there. Why is your local hardware store probably paying more per unit for a hammer than Home Depot? The store probably knows what Home Depot is paying but that doesn’t mean that they can get there. Our business intelligence and analytics can help point companies in the right direction.
What’s the next step for the future of analytics and business intelligence, both in general and in the contract labor industry?
We see a number of things going on in the industry. First, and this applies across the board, is technology. Technology is changing our lives, and mobile business intelligence is huge. Everyone’s going to have easy and more instantaneous access to data. Having that easy access is great, but it’s also dangerous because it’s not just about getting at the data. It’s about being able to put it into context and analyzing it properly. I’m excited about mobile BI but I’m also concerned because it can be dangerous if you don’t take the data within context.
Another change, for better or worse, is that data is king. It’s driving so much in the marketplace and it’s going to increasingly drive organizational behavior, even in organizations that have been resistant culturally to data. The availability of data is increasing exponentially. The technology is able to handle data much better, the analytics are becoming much more sophisticated, and both are going to drive an organization’s behavior.
In terms of the contract labor industry, a lot more can be done with the data. Our clients are increasingly using the analysis that we provide to change some behaviors internally within their organizations, whether that means getting buy-in from other executives or changing the behavior of line managers who typically want to do their own thing.
Another area for analytics in the contingent workforce industry that is still in its infancy is risk management —understanding which suppliers are particularly safe, which are less likely to go out of business, or which ones may provide a talent that hasn’t been properly checked. Questions such as — Who are you paying? How are you paying them? Are they financially stable? Risk management is an area where business intelligence can help.
How else can organizations use analytics differently in the years to come?
In our industry, we were innovative, the first to start using analytics in managed services programs. At the time, clients were mainly interested in benchmarking and understanding what the market rate was for each type of talent. That continues to be the focus for most people in our industry. Obviously, that’s an important part of business intelligence, but there needs to be more emphasis on understanding how cost, quality, and delivery of talent works.
Reporting and gaining visibility into data is extremely important because you can’t get anywhere without it. But the data needs to be put into context and analyzed properly. That’s how you can make this overload of information meaningful.
Predictive modeling will also play a role in the future. We do predictive modeling to help clients understand where they would likely end up if they made certain decisions. We do not see much of this in our industry or in the marketplace, but I believe we’ll see predictive modeling more in the future.
Elliot Owens is a human capital industry veteran with more than 19 years of experience in the field. Mr. Owens spent 15 years working on global Managed Services Provider (MSP) programs in the U.S., Europe, and Asia. During this time, he was instrumental in launching Volt operations in the United Kingdom.
Mr. Owens has worked for the last 15 years with Volt Information Sciences companies, Volt, and ProcureStaff. He has incorporated his concurrent expertise in the area of data warehousing and statistical analysis to develop the services supply chain management industry’s first human capital data warehouse and market data-driven business intelligence (BI) service offering. As Vice President of Volt Consulting BI group, which he founded in 2005, Mr. Owens represents the vanguard of the BI movement in the human capital industry.
Mr. Owens was a 2009 Recipient of Supply & Demand Chain Executive’s “Pros to Know” award for recognition of his groundbreaking approach to conducting spend, rate, and quality analysis within human capital procurement.
He is a graduate of the University of California, Berkeley and a Certified Six Sigma Black Belt.
Scott Robbin is a Director at Argyle Executive Forum. In this role, Mr. Robbin manages content development, editorial speaker recruitment, and execution for 20+ annual business events. He has over five years of experience working on the production and implementation of senior-level events. He holds a Bachelor of Arts from Columbia University, where he was the captain of the varsity tennis team.