Historically, the people responsible for talent management have had an aversion to analytics. An SHL Talent Measurement Global Assessment Trends Report claims “less than half of the global companies use objective data when making workforce decisions, and fewer than 20 percent were satisfied with the ability of their current data management systems to manage talent data.”
With even more data at our disposal, it is understandable that some people feel overwhelmed by the idea of using metrics and analytics as a business tool. This made us wonder, ‘What if you could get measurable results from data, without a lot of stress?’ Imagine harnessing Big Data and being able to analyze present and future labor requirements, segment your workforce, and gauge internal and external labor markets. Would this position your organization for success?
Some companies are already finding success with data-driven CWM programs. In this blog post, we discuss seven ways to take a data-based approach to your CWM and build a talent strategy that makes sense for the future of work.
Most Contingent Workforce Programs Rely On Guesswork
Why are procurement and human resource practitioners finding it so difficult to use data to be more analytical, evidence-based, and predictive? Why do they rely on the intuitive approach and make decisions without proof or reasoning?
One reason is that few companies have neither the tools nor the skills to perform the analysis needed to make sense of the data. Without these, they rely on conjecture. Instead of anticipating trends in the market, they react to developments as best they can. If they analyze last quarter’s numbers, they are doing well. In fact, many organizations only look at basic hiring data. Without the right people and the right tools, data is not meaningful or useful in a business sense. Mark Smith, Ventana Research’s CEO and Chief Research Officer says, “It's not that companies don't have the data, it's that they need ways to make it more useful.” Managing a global talent pool requires experts who can link data with real business problems and opportunities for the business.
Another reason companies shy away from data-based decision making is fragmented data. Every time an enterprise adds a new recruiting, talent management, or performance evaluation tool, data becomes more fragmented. Organizations cannot analyze and act on data if they do not allocate time and money necessary to integrate the proper tools. At best, the workforce data would be isolated and not useful.
In short, most companies cannot access data when they need it or they do not know how to make sense of the data they do have. That leaves them unable to use Big Data to reduce costs and improve quality and performance. Understanding the cause of these problems will make learning how to take a data-based approach to your contingent workforce management a snap.
Why Are So Many Companies Missing the Mark?
There are many obstacles when it comes to adopting better Workforce Analytics. Here are some of the most common:
- Established attitudes and practices within human resources and procurement can lead to making relationship-based decisions instead of evidence-based decisions.
- It is easy to get overwhelmed when organizations first start using Big Data. Confusion leads to a tendency to make no change.
- Some companies get started and then do everything wrong. They use the wrong metrics. They ask they wrong questions. Then they decide Big Data is nothing but hype.
So how do you make fact-based business decisions without drowning in data?
How to Use Data in Your Contingent Programs
Once you develop the process and capabilities for converting big data into useful and actionable intelligence, you can use data to build a talent strategy that makes sense. There is a lot at stake. Michael Capone, Chief Information Officer at Automatic Data Processing, Inc. says, “The companies that leverage workforce analytics effectively will win the war for talent.”
1. Integrate your tools and processes. When you use a hodgepodge of tools and solutions, you add barriers to retrieving and sharing information. You have to integrate tools to understand where your information is located and to give the right people can access to that information. For example, a strong vendor management system (VMS) that can deliver real-time data is a part of many successful contingent programs.
2. Do not simply look back. Analyzing past performance is fine, but one of the benefits of Big Data is the ability to engage in predictive workforce analysis. Once you have harnessed Big Data, use your data to analyze current and future human capital requirements. You should be able to optimize your workforce in real-time.
3. Look at the big picture. Analytics allow us to spot geographic and demographic shifts before we otherwise might, as decisions to hire in a specific location or for remote workers happen daily and rates change quickly and often. Detailed analytics are useful for seeing more than the immediate challenges facing the organization.
4. Recognize the business case. Data-based decision making makes sense. Properly leveraging analytics lets you budget better for the future and gauge future spending. Whether you are tracking SOW-based projects and services or managing temporary labor, an effective framework for managing data provides increased visibility into your spend and your labor deployment. You will also be able to easily track headcount, spend, and cycle time.
5. Use business metrics, not HR or procurement metrics. According to Christopher Dwyer, “The true effectiveness of SOW-based projects, services and contractors can only be measured by leveraging analytics to understand how these items stack up against pre-defined (and communicated) goals, objectives, milestones, and delivery dates.” Align metrics with your business’s strategic goals. Although organizations task HR with human capital management, it is the business, not human resources, that is ultimately responsible for talent management.
6. Measure the “intangibles.” Use quantitative data to measure qualitative aspects of your CWM program. It is possible to use data to measure the performance of talent across the enterprise. The more organizations can measure the human dimension throughout an employment life cycle, the more effectively they can manage and improve workflow and productivity. Additionally, measuring performance allows companies to identify high-performers and train some workers for other positions.
7. Begin with the end in mind. Figure out where your organization wants to be. Detail this in your business goals and people strategy. Assess where you are now. You can use data you have already collected to do this. Now develop a plan and talent management strategies to get from where you are, to where you want to be.
As managing Contingent Staffing becomes more complex, enterprises will need better business intelligence and improved reporting and analytics. There will be no margin of error for companies relying on guesswork to run their contingent programs.
How are you using Big Data to manage your contingent workforce? Let us know by sharing your thoughts with us on Twitter at @BeelineVMS.
