The 6C™ Framework for Smarter Asset Data QA

How to Get Asset Registers Right the First Time

Clean asset data is the foundation of better asset management decisions.

Whether you are planning maintenance, budgeting for capital works, forecasting lifecycles, or reporting for compliance, the quality of your data will either support or sabotage your efforts.

And most organisations only get one shot at getting it right.

When asset data is wrong or incomplete, the impacts last for years. Reporting becomes unreliable. Forecasting drifts off course. Maintenance decisions are based on guesswork. You rarely get extra budget to fix it, and in many cases, you won’t get another chance for a full recapture for five years or more.

That’s why we built the 6C™ Framework: a structured, repeatable way to build cleaner, smarter, more scalable asset registers from day one.

Let’s walk through it.

The 6C™ Data QA Framework

Step 1: Compile | Gather All Data Sources

Before you can fix asset data, you need to find it.

We start by gathering and consolidating all available data sources into one central system. That includes existing asset registers, field capture sheets, site notes, system exports, project reports, and historical audits.

Fragmented data creates blind spots. Consolidation is the first step toward visibility and control.

All data is loaded into Audify, our audit and QA platform, ready for cleansing.

Step 2: Cleanse | Fix the Basics Before They Snowball

Once everything is in one place, we clean up surface-level errors.

Typos, duplicate entries, brand mismatches, model number mistakes — they might seem minor, but they cause major issues at scale.

An example? It’s common to find two identical air handling units listed as “Mitsubishi Heavy Industries” and “MHI” on the same site. Same asset, two different records. Or a serial number typo where a capital “O” is entered instead of a zero.

If you’re managing 50,000 assets, those small inconsistencies ripple outward, distorting reports and complicating lifecycle models.

By fixing these issues first, we create a clean baseline where every subsequent QA step becomes faster, easier, and more accurate.

Step 3: Correct | Normalise and Align the Data

Next, we focus on normalising the dataset.

We standardise formats across manufacturers, models, components, capacities and asset hierarchies. We amend inaccuracies and correct misaligned entries that would otherwise confuse maintenance teams, auditors, or planners down the track.

Critically, we validate condition ratings to ensure consistency.

Condition scores are only useful if they are applied consistently across like-for-like assets. Our dedicated QA team manually reviews site photos throughout the audit to validate ratings and catch inconsistencies.

If two identical boilers, installed the same year, are rated a 2 and a 6 respectively, we dig in. It might reflect genuine deterioration — or it might be an input error, or simply subjective judgment creeping in.

We also check for the overuse of middle-of-the-road ratings. If everything clusters around a 4, 5 or 6, the data loses its meaning and becomes harder to act on.

Consistency matters because decisions rely on trust in the data.

Step 4: Construct | Build Missing Pieces

Not every asset is perfectly captured on site. Access restrictions, obstructions, and site conditions sometimes prevent full data capture.

Rather than leaving fields blank (or worse, guessing) we use intelligent backfill techniques.

We draw on:

• Current site audit data
• Internal system records
• Previous project benchmarks
• Our proprietary manufacturer-model (MANMOD) database

This database, built from nine years of daily audits, allows auditors to start typing a model number in Audify and auto-fill the correct model, manufacturer, component type and capacity. It eliminates typos, speeds up capture, and ensures standardised inputs across the board.

If five air conditioners are missing model numbers but two identical ones aren’t, we can confidently fill the gaps.

We also use site photos to cross-verify and recover missing information offsite — without needing to revisit the site.

The result? More complete registers without unnecessary delays or guesswork.

Step 5: Certify | Validate and Lock the Dataset

Once the dataset has been cleansed, corrected, and constructed, we move to validation.

Using Audify’s built-in rule-based QA engine, we automatically check for logical gaps and inconsistencies during capture and offsite review.

If a floor and a roof are recorded but no walls? Flagged.
If a solar inverter is captured without solar panels? Flagged.
If quantity counts seem suspiciously low in a large building? Flagged.

These rules don’t just catch errors after the fact. They catch them live, during capture, while auditors are still onsite — when it’s fastest, cheapest, and easiest to fix.

This reduces reliance on memory or manual double-checking. It systemises quality at the point of capture.

Once the data passes rule-based validation, it is locked in, ready for operational use.

Step 6: Commit | Finalise and Operationalise

The final step is about embedding the certified dataset into operational systems as the single source of truth.

Whether it feeds into maintenance platforms, lifecycle models, compliance systems or strategic planning tools, it needs to be ready to support real-world decisions immediately.

Without trust in the underlying asset data, even the best asset management strategies falter.

The 6C™ Framework ensures the asset register you inherit isn’t just clean on paper — it’s complete, consistent, and operationally ready.

Why It Matters

Asset data projects don’t get second chances.

A flawed register doesn’t just cause short-term rework. It locks teams into years of chasing inaccuracies, questioning reports, and making suboptimal decisions.

In most organisations, there isn’t extra budget to redo the work. And it could be years before another opportunity comes along.

Bad data lingers.

The 6C™ Framework is designed to break that cycle.

It gives you a way to get data right the first time — systematically, repeatably, and at scale — so you can move forward with confidence, not compromise.

Because when it comes to asset data, fixing problems later is expensive. Avoiding them is smart.

Need Help With Asset Data Capture or QA?

We work with local, state and federal government agencies, major FM providers, and leading asset teams across healthcare, education, commercial property and infrastructure.

If you are ready to improve the quality and usability of your asset data, without the guesswork, we’d love to help.

Let’s make your register your greatest asset.