Matthew Brown's Analyst Perspectives

Compensation data is everywhere. Internal equity metrics, market benchmarks, performance history, pay transparency requirements—there’s no shortage of data coming in from every direction. But volume does not always guarantee clarity. It is not about the data your system can collect, it is about whether that system—and the people behind it—can turn the data into decisions that actually make sense. That is what it means for your compensation tech to speak the right language. The right language connects what matters: market signals, organizational goals, workforce dynamics. But when your data is messy, outdated or fragmented, what comes out will not be helpful—it will be noise. Even the best platforms can deliver noise if no one is steering the strategy. Today’s compensation tools are expected to support many complex processes and tasks—reflect benchmarks, support internal equity, flex with evolving structures and stay on top of compliance. But let’s be honest: many teams assume the system will “just know what to do,” as if best practices come preinstalled. In case no one has told you this before, they don’t. Best practices are not universal. What works for one enterprise may not work for another without significant modifications to address the unique business challenges and needs. Getting this right takes more than software. It takes alignment, clarity and people who know precisely what problems they are solving for. A lot of teams expect their systems to define the strategy for them—they just don’t say it out loud. But you can hear it in questions like, “Does this platform come with best practices built in?” To be clear, technology is not the strategist. It is the amplifier. The disconnect between expectation and reality is where comp tech usually stumbles. Systems are expected to make sense of outdated logic and inconsistent inputs, often without anyone questioning what might be driving the setup. It is like asking a translator to interpret a conversation when no one agrees on the language being spoken. Geography-based models are a good example of this. They made sense when everyone worked in one place. But with remote work and flexible structures, those rules break down. Add pay transparency laws, market volatility and inconsistent data models and things get even messier. Meanwhile, more data keeps getting layered on—skills, engagement, performance—without a pause to consider if it connects or even matters. Compensation systems pull from everywhere: job data, performance scores, skills, budgets, benchmarks and geography. If even one stream is outdated or out of sync, the outcomes will not just be off, they will be misleading. And once trust in the data erodes, the whole process follows. Also, it is important to acknowledge that it is not just about accuracy. It is about confidence. Does your data reflect reality? Does it support fair, defensible decisions? One place where things often go sideways: pay equity models built on outdated, incorrect organization charts. When your budgeting is tied to outdated role mappings, it can recommend budgets that do not align with reality and may cause confusion, eroding trust with participants in the process. Too often, enterprises rely on systems that look like they’re working—but are running on assumptions no one has reviewed recently. That’s why clean, connected data is no longer optional. As highlighted in our Compensation Management Buyers Guide, leading platforms are evolving to include automated equity modeling and anomaly detection—but they still rely on the right inputs. You can’t outsource strategy to software. It still starts with you. Too often, compensation strategy runs on autopilot—anchored to frameworks that were inherited, not designed. They’ve worked “well enough,” so they stay. But today’s expectations have shifted, and so should the approach. The best compensation teams are not just compliant or competitive—they are curious. They ask: What matters to our people now? What are we actually trying to reward? And how should this look next year, not just next cycle? Job architecture still has a role, but it cannot continue to be the only option. Titles don’t always reflect value. Roles evolve. Skills drive impact. And rigid frameworks start to buckle under those conditions. ISG Research asserts that by 2027, two-thirds of compensation pricing processes will include skill benchmarking, not just jobs. That shift will not happen by accident. It takes strategy and flexible systems to support it. Compensation technology only works when the data behind it is clean, current and aligned. That sounds simple, but most enterprises are still wrestling with the basics. As I have written, technology is making it possible to move from fixed models to dynamic, personalized compensation strategies, but only if we rethink how to build them. Think of compensation data like building blocks. Not puzzle pieces that only fit one way, but reusable components you can combine differently as needs change. Your platform won’t make the choices for you—but it can help you build smarter, if you’re clear on what you’re building toward. Not all compensation technology is built to do the same job. And if you are not clear on what your system is actually designed to deliver, you’re going to be disappointed—no matter how advanced it claims to be. Finding the right technology involves different approaches to achieve your organization’s specific goals. Enterprises focused on budgeting, cycle workflows and giving managers structure might use a compensation planning tool. These tools often live inside larger HCM suites—but the functionality varies widely. We explored some of these tools in our Compensation Planning Buyers Guide. Enterprises seeking broader governance—eligibility, equity reviews, pay programs and complex business rules—might benefit from a compensation management platform. Some organizations could go even further with total compensation tools that combine base pay, equity, variable pay and even benefits into one view. These tools can communicate rewards more holistically, as outlined in our Total Compensation Management Buyers Guide. No platform does everything, but if your strategy is clear, it will be much easier to find the right fit and to know when your tech is helping or holding you back. So before the next cycle kicks off or a benchmarking report lands in your inbox, pause and ask: What story are we trying to tell with our compensation data? If your platform is surfacing numbers that don’t inform how you want to lead, it’s time to reassess and realign. Is your data accurate, current and connected across systems? Do you have a process to keep it that way—not just during planning season, but year-round? Then, check your expectations. Are you looking to your platform for answers it was never meant to provide? Or are you using it to elevate your strategy, not define it? And most importantly—can your systems help you explain pay in a way people believe? When your compensation tech speaks the right language, it does more than calculate. It builds trust. It reflects priorities. And it helps you lead with confidence—on purpose, not on autopilot. Regards, Matthew Brown

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