From Functional Silos To Outcome Orientated Workflows
Leaders often like to talk about outcome orientation, but their data architecture still reflects functional silos. When recruiting, learning, and performance systems don't share basic data standards, outcome-based teams can't operate. The blocker isn't org structure—it's whether systems can answer simple questions: Are we talking about the same person? Is this data current? Can we trust what we're seeing?
Most organizations discover their data makes outcome orientation impossible. Systems use different IDs for the same people. Data is weeks or months old. The same skill shows three different assessments. The future belongs to enterprises with less software and better data foundations.
Organizations claim to be outcome-oriented, but their data tells a different story.
When a team is accountable for "fully staffed, capable workforce in region X," they need to answer basic questions: Who do we have? What can they do? Where are the gaps? What's in progress?
In most organizations, this data lives in separate systems that don't talk to each other properly.
The recruiting system knows who you're hiring. The learning system knows who's in training. The performance system knows who's ready for new challenges. But these systems can't connect the dots.
Why? Because they identify people differently. Your recruiting system uses one ID. Learning uses another. Performance uses a third. Even when it's obviously the same person, the systems can't reliably match them.
So when your team asks "should we hire someone or develop internally?" they can't get a straight answer. The data exists, but it's trapped in systems that can't talk to each other.
Then there's timing. Your recruiting data is current—you know who's in the pipeline today. But your learning data is three months old—it shows courses completed last quarter, not what's happening now. Performance data is even older—last year's annual review.
You're trying to make decisions with data from three different moments in time. Good luck.
And the data itself doesn't mean the same thing across systems. "Skill level" in recruiting means something different than "skill level" in learning. Performance uses yet another definition. So when you try to compare, you're not even measuring the same thing.
Most organizations discover this the hard way. They reorganize around outcomes, create cross-functional teams, set clear goals. Then those teams spend all their time manually piecing together data instead of making decisions.
The problem isn't commitment to outcome orientation. It's that the data architecture wasn't built for it.
Outcome-based work requires data that actually works across functions:
Same person in recruiting = same person in learning = same person in performance
Data that's current enough to act on, not out of date
Definitions that mean the same thing across systems
Ability to trace where data came from and trust it
Without this, outcome-oriented teams are stuck. They can't answer basic questions without manual data archaeology. They can't make integrated decisions because the data won't integrate.
You can talk about outcome orientation all you want. But if your systems can't answer "who is this person and what can they do right now?"—you're not ready.
Here's what we think happens next: enterprises will run on less software with better underlying data.
The current approach—buy more systems, integrate later, hope for the best—creates complexity that compounds. More platforms mean more IDs to reconcile, more definitions to align, more data to keep current.
The future belongs to organizations that get the data foundations right first. Common identity. Current data. Shared definitions. Clear lineage.
With that foundation, you need fewer systems. The ones you have actually work together. Teams can make decisions instead of fighting data.
The shift from functional silos to outcome orientation isn't about reorganizing. It's about building data architecture that makes integrated work possible—then resisting the urge to bury it under another layer of software.
