By Danielle O’Neill, Vice President Global Solutions, KellyOCG
Despite the talk of data-driven workforce strategies, most organizations struggle to connect data to actionable decisions. Not long ago, a procurement leader told me, “We want to be data-driven, but our decisions are only data-decorated.” I shared this remark with colleagues at a recent CWS Summit North America roundtable that I moderated, and I saw heads nod in agreement. Data is often used after a decision to justify it but not before it to make the right choice.
The gap between data and decisions permeates workforce strategy. Managers typically lack complete information about talent costs, available resources, or project timelines. Traditional reporting can't predict or solve problems. It does not give reasons for missed deadlines or poor outcomes, so guesswork and assumptions determine next steps. This creates fulfillment delays, talent mismatches, and higher costs.
Bridging the gap between your data and workforce decisions is not only possible, but it is essential for taking control of costs and quality. So how do you course-correct your technology strategy? A good starting point is to ask yourself the following questions:
1. What data can you trust?
A data-driven strategy depends on trusting the signals your technology delivers. Consider these points:
- Don’t confuse data use with data usefulness: During the CWS roundtable, I asked the group, “What one piece of data do you trust the most?” Everyone had a favorite, and we could not agree on one. I then asked, “If you had to remove one data point, what would it be?” Everyone defended cost, time-to-fill, and hiring manager satisfaction because they were the most utilized. We could not reach an answer.
It soon became clear to the room that we could only improve our use of data if we stopped leaning on what’s worked in the past. If you are not prepared to sacrifice the types of data you are most familiar with, it will be difficult to remove the noise and improve its usefulness. - Beware of misleading metrics: In that same CWS conversation, I added another data point to the mix: headcount. A high headcount doesn’t always mean effective resource utilization, and drawing conclusions from it can be misleading. A large managed service provider (MSP) program can have cost overruns, poor quality, or unhappy hiring managers. We reached agreement on this one. The lesson here: start thinking about every data point, and whether the implications of the data are accurate. Is more headcount better? Not necessarily.
Look for genuine indicators: Think about rates for contingent workers. A lower bill rate isn’t the best deal if the workers you engage take longer to become productive, experience higher turnover, or require more training. The better indicator of value comes from the effective bill rate (total cost per productive hour), not the simple sticker price.
Takeaway: Scrutinize every data point. If a metric misleads, poor decisions will follow. The challenge is to identify what can replace that data, and the answer is not merely intuition.
2. Is your data revealing the story you need to see?
Each stakeholder requires unique metrics to make decisions, but this often creates confusion and conflict. Break it down:
- Track performance-relevant metrics: Metrics like time-to-fill or headcount only show part of the picture and can obscure crucial issues like retention, candidate quality, or market alignment.
- Dig deeper: I recently worked with a new client focused on shrinking time-to-fill. On paper, positions were filled quickly. However, upon closer examination, high turnover and poor retention became apparent. Reframing the story to include candidate quality and long-term retention uncovered a misalignment between hiring practices and market needs.
- Update your context: Once the client’s leadership team saw the bigger story, they adjusted their recruiting process to prioritize candidate fit and tenure. This led to improvements in retention and engagement, and it reduced rehiring costs.
Takeaway: Don’t just look at the picture your data paints; decide what picture you need to see and then gather the right data to create that view.
3. What is possible now?
Workforce decision-makers may not realize the full potential of their data today. AI-powered advances aren't just hype; they’re driving practical changes in three core areas:
- User interaction: An example of current advanced technology is the Helix interface that supports our clients. Developed to be intuitive and user-friendly, it empowers hiring managers to do more than just see data; it guides them step-by-step through their workforce program. Most recently, decision guidance features have been implemented to help many clients determine realistic job requisitions and optimal hiring strategies based on their live input. We’re actively expanding this approach to make workforce planning even more effective across our client base.
- Data integration: Gone are the days of expensive, rigid integrations. AI can now dynamically analyze disparate sources — from vendor management systems (VMS) to external talent supply data (like Brightfield or Lightcast) — delivering relevant answers in real time. Information from applicant tracking systems, HRISes, and more can be leveraged for unique, timely insights. This analytics capability is now in place for many of our clients, and the results help us determine costs, establish timelines, and solve problems quickly and effectively.
- Predictive analytics: Machine learning can forecast outcomes, guiding recruiters on task prioritization, predicting optimal bill rates, and mapping future workforce skills to inform requirements and upskilling strategies. We use this functionality to develop roadmaps for client projects such as staffing a new location, launching a service, expanding into new areas, or executing any initiative.
Takeaway: Expect your workforce data to deliver predictive, relevant intelligence tailored to your needs — not just static information. Ask more from your own technology and workforce solutions partner. The capabilities today are more advanced than many realize.
