Why Large Data Projects Stall and How to Avoid It

This article was created with AI assistance and reviewed and revised by our

leadership and marketing team.

The Problem with Big Data Implementations Goes Beyond Numbers

Your commission plans are the heartbeat of your sales strategy. They define what matters, guide rep behavior, and ultimately determine how performance turns into pay.

So, when you invest in a solution like Spiff, the goal is clear: create transparency, build trust with your reps, and give leadership the agility to adapt as the business evolves.

But once implementation begins, friction often appears quickly:

• Data questions emerge

• Communication isn’t clear

• Stakeholders introduce new reporting requirements

As these issues compound, timelines stretch. What started as a straightforward enterprise implementation begins to feel more complex, and more unpredictable than expected.

The immediate assumption is usually that the problem is data volume.

But in reality, the challenge goes beyond numbers.

What Actually Qualifies as “Large Data”?

At Incentive Partners, large data isn’t defined solely by record count. It’s defined by complexity.

Typically, this includes organizations with:

• 300+ commissionable reps

• Multiple source systems (e.g., Salesforce, Workday, data warehouses)

• Complex relationships between those systems

As one Incentive Partners implementation expert explains:

“Warning signs include a data table with more than 1 million records, or more than 15 data tables. Alone, these are not a problem, but they indicate that we should think strategically about how data is brought into Spiff and how they are used in comp plans.”

A high volume of data is manageable. Even multiple tables are manageable.

The real challenge begins when those data sources must work together to answer key commission questions, such as when deals, invoices, payments, territories, and employee data all need to align perfectly to produce accurate payouts.

That’s where complexity, not size, becomes the defining factor.

The Hidden Problem: Data Readiness

When large-data implementations stall, the visible issues are familiar:

• Data gaps (missing or incomplete data)

• System misalignment (mismatched or inconsistent sources)

• Ownership confusion (no clear accountability)

But underneath all of this lies a more fundamental problem: the data isn’t ready.

As one Incentive Partners expert puts it, the biggest issue is often “inability to access data.” Even after purchasing Spiff licenses and starting the implementation, if the data isn’t prepared, “it basically just stops the clock on the implementation.”

That delay can be significant. Teams may spend months resolving data gaps, aligning systems, or building automation after implementation has already begun. Plans may be ready. Stakeholders may be aligned with goals. But without accessible, structured data, progress stalls.

This is where expectations break down, timelines expand, and scope creep begins.

Why Communication Matters More Than Clean Data

All that said, successful implementations don’t necessarily start with perfect data.

They start with shared clarity.

Complex implementations require coordination across Sales, Finance, HR, Revenue Operations, Payroll, and IT. Each team owns part of the process, but without alignment, those pieces don’t come together.

As one implementation expert described:

“The commission team says, ‘I don’t know anything about the data,’ and the data team says, ‘I don’t know anything about commissions.’”

That disconnect is where risk emerges.

Even clean data becomes difficult to use when no one understands how it flows between systems or how it impacts payouts. For example, employee data may live in Workday, performance data in Salesforce, and historical payouts in legacy systems. If teams aren’t aligned on where that data lives, who owns it, and how it’s used, implementation slows—and complexity compounds quickly.

The result is familiar:

• Manual overrides

• Reporting rework

• Brittle logic

• And worst-case scenario, full rebuilds late in the project

Build the Recipe Before You Start Cooking

One of the most common mistakes in large-data implementations is starting the build before fully understanding the inputs.

As one Incentive Partners expert put it:

“Starting to cook before you finish the recipe.”

Without a clear understanding of data sources, ownership, and how those inputs drive commission calculations, teams are forced to solve problems mid-build.

That leads to preventable rework, delays, and unnecessary complexity.

With the right preparation, including clear data ownership, aligned stakeholders, and a shared understanding of how the system should work, those risks can be mitigated before they impact the timeline.

The Takeaway

The difference between a smooth implementation and months of rework isn’t data volume. It’s alignment.

At Incentive Partners, our Spiff and SPM experts help organizations define their data, align stakeholders early, and build compensation systems that scale cleanly from day one, without unnecessary risk or rework. 

Schedule your complimentary Spiff Health Check to identify gaps early, streamline your implementation, and ensure you get it right the first time.