The Three Types of Work Data (And Why You Only Have Two)

Enterprises are investing in the first two data layers but neglecting the third.

Every company tracks what comes in and what goes out. Customer behavior. Revenue. Costs. Product usage. Financial performance.

But almost no one measures what happens in between—how inputs actually transform into outputs.

That's the missing layer: execution data.

The Two Data Layers Enterprises Already Have

Most operational infrastructure is built around two types of data:

Input Data

What enters the business:

• CRM systems capture customer interactions, deal pipeline, and sales intent
• Product analytics track user behavior, feature adoption, and engagement patterns
• ERP systems record inventory levels, supplier data, and purchase orders

Input data tells you what's coming at the organization.

Output Data

What the business produces:

• BI dashboards show revenue, margins, and growth trajectories
• Financial reports track cost per unit, budget variance, and profitability
• Performance metrics measure NPS, customer retention, and cycle time

Output data tells you what the organization delivered.

The Problem: The Transformation Is Invisible

Here's what enterprises can't see:

A deal enters the CRM (input). Three months later, it closes (output). But what happened in between?

• How many people touched the proposal?
• Where did it stall?
• How many approval rounds did it require?
• Which handoffs caused delays?
• What rework happened before signature?

None of that is visible in input or output data.

The same gap exists everywhere work happens:

• Support tickets move from "open" to "resolved," but the escalation path is invisible
• Product ideas become roadmap items, but the decision-making workflow is unknown
• Reports get published, but no one tracks the collaboration patterns that produced them

You know what came in. You know what came out. You have no idea how the transformation happened.

That's the 95% you can't see.

Why This Matters Now

Enterprises could ignore execution data when operations were static. Processes changed slowly. Teams worked the same way for years.

AI destroyed that stability.

Workflows now shift weekly. Copilot changes how proposals get written. AI assistants alter how support tickets get triaged. Automation reshapes how reports get generated.

Without execution data, you can't answer basic questions:

• Did AI actually speed up contract approvals, or just move delays somewhere else?
• Are high performers using AI differently than average performers?
• Which workflows improved, and which ones got worse?

Input and output data can't tell you. Usage dashboards can't tell you. You need execution data.

What Execution Data Reveals

Execution data captures how work actually moves through the organization:

Workflow patterns:

• A proposal process that's documented as 5 steps actually runs as 12
• Contract approvals have 40 variants across regions, not the "standard process" leadership assumes
• High performers skip steps 3 and 7; average performers follow documentation and move slower

Collaboration structures:

• Support tickets that get resolved in 2 hours always involve the same 3 people
• Product decisions that take 6 weeks include 8 approval touchpoints; decisions that take 2 weeks have 3
• Proposals that close involve 4 handoffs; proposals that stall involve 9

Bottleneck identification:

• 60% of deal delays happen during legal review, not sales negotiation
• Report generation takes 8 hours because data lives in 4 disconnected systems
• Customer onboarding slows when Implementation hands off to Success without context

This is the data layer that explains why outputs don't match expectations and how to fix it.

Why Execution Data Didn't Exist Before

Work happens everywhere: email, Slack, Salesforce, Zoom, Excel, Google Docs. Capturing execution patterns across those systems required manual observation (process consultants shadowing teams, workshops reconstructing workflows, surveys asking people to remember).

None of that scaled. Execution data stayed anecdotal.

LLMs changed that.

Large language models can interpret intent across unstructured systems. They distinguish between productive collaboration and rework. They understand when a handoff is clean versus when it requires three clarification rounds. They can capture execution patterns automatically, at scale, in real time.

For the first time, execution data can be a continuous, reliable infrastructure layer—not a one-time consulting project.

The Complete Data Picture

Enterprises need all three layers:

Input data tells you what's coming
Output data tells you what happened
Execution data tells you how and why

Input and output data reveal that performance is below target.
Execution data reveals where the breakdown occurred and how to fix it.

When AI changes workflows, input and output metrics stay the same, but execution patterns shift dramatically. That's the signal that matters.

Where Fluency Fits

Fluency captures execution patterns across every system where work happens: email, Slack, Salesforce, everywhere. It builds a continuous view of how workflows actually run: who does what, in what sequence, with what delays and handoffs.

This is the missing data layer.

When a proposal takes 6 weeks instead of 2, Fluency shows which handoffs caused the delay. When AI is deployed, Fluency reveals whether execution patterns actually improved, not just whether people used the AI tool.

Execution data doesn't replace input and output systems. It completes them.

The Bottom Line

Enterprises invest billions in capturing inputs and measuring outputs.

But the transformation between them (the execution itself) remains invisible.

That's not a gap you can ignore anymore.

Not when workflows change weekly. Not when AI reshapes operations. Not when every executive is asking "Is this actually working?"

You can't optimize what you can't see.

Execution data makes it visible.