What Does a Data Audit Look Like Before Implementing Revenue Intelligence?

A revenue intelligence platform is only as useful as the data it pulls from. That's not a disclaimer — it's a practical truth that determines whether you get to accurate cost-per-case reporting in 90 days or six months. The firms that invest in a pre-implementation data audit consistently get there faster.

Here's exactly what a data audit looks like before implementing a revenue intelligence platform in a personal injury firm: what you check, what you're looking for, and what to do when you find problems.

Why a Data Audit Matters

Most PI firms don't realize how fragmented their data is until they try to connect it. Marketing data lives in ad platforms. Case data lives in the CMS. Spend data lives in spreadsheets or accounting software. And within each system, the data quality is often lower than anyone assumed.

A data audit surfaces three types of problems before they become implementation blockers:

• Completeness gaps— Fields that should be filled but aren't (lead source missing on 30% of records, for example)
• Consistency gaps— Fields that are filled, but inconsistently (“Google” in some records, “Google Ads” in others, “Google PPC” in others — all meaning the same source)
• Structural gaps — Data that exists but lives in the wrong format (settlement amounts in case notes, not structured fields)

Step 1: Audit Your Case Management System

Your CMS is the primary data source for any revenue intelligence platform. Start here.

Lead Source Field Audit

Pull all leads from the past 12 months and check the lead source field. Look for:

• % of records with a blank source field — Any blank is a gap. Above 5% is a significant data quality problem.
• Number of unique source values — If you have 40 vendors but 200 unique source values in your CMS, you have a consistency problem. People are entering source names in free text rather than selecting from a controlled list.
• Values that need consolidation— List every unique source value and group them by what they actually mean. “Google,” “Google Ads,” “Google PPC,” “Google-Brand,” “Goog” — these all likely mean the same thing but will be reported as separate sources.

Disposition Field Audit

Pull all leads and check whether disposition (signed, rejected, withdrawn, pending) is recorded for each:

• What % of leads from more than 90 days ago still show “pending”? That's stale data.
• Are rejection reasons captured? How many unique rejection reason values exist?
• Does the disposition field update in real time when intake makes a decision, or is it a manual step that's often skipped?

Settlement Data Audit

This is where most firms find the largest gap. Check:

• Does your CMS have a structured settlement amount field, or does settlement data live in documents and notes?
• Of your cases closed in the past 24 months, what % have a settlement amount recorded in a queryable field?
• Is settlement date recorded as a structured date field, not just noted in a case log?

If settlement data isn't in structured fields, you can't calculate average settlement per source — which is the metric that distinguishes a vendor who produces many cases from one who produces profitable cases. This is often a phased data entry project rather than an immediate fix.

Step 2: Audit Your Marketing Spend Data

Cost per case requires spend data. Where does yours live, and how reliable is it?

• Ad platforms (Google, Facebook): Pull 12 months of spend by campaign. Confirm campaigns are named consistently so spend can be allocated to the right source category.
• Lead vendor invoices: Do you have monthly invoices by vendor for the past 12 months? Are they consistent — same vendor names, same categorization? Gaps in invoice history create gaps in historical cost-per-case calculation.
• Agency retainers: If you use an SEO agency, PR firm, or marketing agency, is that spend recorded somewhere that can be allocated by month?
• TV and radio: Are production costs separated from media spend? Are markets separated so you can calculate cost per case by market?

Step 3: Audit Your Attribution Setup

Even if your CMS has great data, it's only valuable if leads are attributed correctly when they arrive.

• Web form attribution:Are UTM parameters set up on your paid ad landing pages? Does your web form pass UTM parameters to your CMS? If not, web-sourced leads may all show as “Direct” or “Web” regardless of which ad campaign generated them.
• CallRail configuration: Is each tracking number associated with a specific source? Does CallRail pass caller source to your CMS via integration, or are calls entered manually by intake staff?
• Lead vendor API feeds: For vendors that send leads via API, does the source field populate automatically with the vendor name? Or does it require manual entry by intake staff?

Step 4: Document What You Find

Create a simple audit summary document that covers:

• Lead source completeness: % of records with source data, list of consolidation needed
• Disposition completeness: % of records with current, accurate disposition
• Settlement data availability: structured vs. unstructured, coverage %
• Spend data availability: vendors covered, months of history
• Attribution gaps: web form UTMs, call tracking configuration issues

This document becomes the implementation roadmap. Issues are categorized as: fix before go-live, fix in Phase 2, or accept as known limitation.

What Good Enough Looks Like

You don't need perfect data to start. You need data that's clean enough to produce directionally accurate cost-per-case reporting on your major sources.

A firm with 85% lead source completeness, consistent disposition fields, and 12 months of vendor spend data can get to useful cost-per-case reporting on day one. They'll clean up the remaining 15% and add settlement data over time — but the first 90 days still produce the 15-20% ROI improvement that comes from identifying underperformers and reallocating budget.

A firm with 50% lead source completeness and spend data in 12 different spreadsheets needs to do more prep work — but the audit tells them exactly what that work is, so they can move efficiently.

The data audit is not a barrier to implementation. It's a prerequisite for getting value quickly instead of slowly.

Want a structured pre-implementation data readiness review for your firm? Book a demoand we'll walk through your specific CMS, spend tracking setup, and attribution configuration to identify what's ready and what needs attention before go-live.