Why Accuracy, Not Just “Quality,” Matters in Spend Data

Accuracy is what ties a company’s reported data back to its actions, policies, and intentions. In the context of Spend transparency, the data submitted is often the only public-facing representation of how well a company practices what it preaches. So, the farther that data strays from the truth, the less trustworthy that company appears, regardless of its actual intentions.

While we’ll use the term “quality” here, because that’s what the industry recognizes, let’s remember: what we’re truly evaluating is how closely data reflects reality.

Data Quality Across the Spend Lifecycle

More than amounts, spend data quality is about who, what, when, where, and why, and it matters across the entire Spend lifecycle. Let’s break it down:

1. Before the activity or Before HCP interactions

This is the decision-making phase—selecting HCPs, awarding grants, or setting up clinical engagements. Accuracy here depends heavily on the master data. Is the recipient correctly identified? Are their licenses valid? Are they sanctioned?

If you’re missing key information up front, you’re setting yourself up for inaccurate reporting later. Clean, verified data at this stage ensures the right people are chosen for the right reasons, and that decisions are documented in a way that supports downstream transparency.

2. After the activity or After HCP interactions

This is where things often get messy. Once an activity occurs, Spend is recorded—sometimes by T&E tools like SAP Concur, sometimes by third-party vendors, and sometimes… in spreadsheets.
Here, the challenge lies in capturing complete and accurate context, especially for:

• Ad-Hoc Spend (e.g., meeting attendees) where recipients aren’t well known
• Known Spend (e.g., contracted speakers) where systems weren’t built to capture non-financial details

Many assume AP systems will capture everything needed for Spend reporting. They won’t. They were designed for finance, not compliance. That’s where purpose-built platforms like HCP Engagement tools come in, to ensure complete data capture, proper use of identifiers, and alignment with corporate policies.

3. Aggregation

Now comes the heavy lifting: consolidating all Spend data into a central repository. This is the last major point to assess and correct data quality.

If poor-quality or inconsistent data makes it through, fixing it later leaves a messy audit trail. Companies often rely on homegrown macros or manual reviews to clean this up, but modern compliance suites now offer smarter, scalable ways to flag issues and route them to the right teams for resolution.

4. Right before reporting

At this stage, your data should be coherent and ready to file. This is your last opportunity to run sanity checks, lock down datasets, and ensure compliance sign-off.

Think of it as the final walk around the house before you leave for vacation, double-checking that the lights are off and the stove isn’t on.