Run a Splotch Health Check & Untangle the Hairball in 90 Minutes

Why the “hairball” keeps tripping up projects

If you’ve ever been stuck in the middle of an ERP or CRM rollout, you’ve probably seen it: the dreaded hairball. Fields that look the same but aren’t, objects copied in three different places, and integrations that nobody remembers writing. By the time you hit verification, the whole system feels tangled, and every fix takes twice as long.

We built the Splotch Health Check to tackle exactly that moment: right after code mapping, right before verification with Sourcerer (more on this later). In 90 minutes, you can spot the biggest risks, get everyone aligned, and walk away with a clear plan. We’ve seen teams cut weeks of discovery down to hours using this approach.

As Charlie, an implementation lead, told us: “This is insanely valuable. I would pay a lot of money. [You can see] What is the current state. What is the ideal solution. What is the impact of that solution? Show them the diff!”

When to hit pause and run a health check

We’ve learned to watch for a few tell-tale signs that it’s time to stop and untangle:

• Your code mapping is done, but there are still three different ways to capture the same business fact.
  
  
• Reports don’t line up:  finance and sales are pulling different numbers for the same metric.
  
  
• You’re already spotting duplicate fields during sampling.
  
  
• A migration or release is looming, and you don’t want surprises downstream.
  
  

What you’ll need in your back pocket

• The code mapping output (field names, objects, customizations).
  
  
• A metadata extract or CSV with field names, types, last modified dates.
  
  
• A decision-maker in the room (product owner, architect, or champion).
  
  
• A scoped domain: sales objects, order-to-cash, or support tickets. Don’t try to solve the whole system in one go.
  
  

The 90-minute process

We’ve run this dozens of times with clients, and the rhythm is always the same: run, interpret, triage. Here’s how we do it.

Step 1: Run a quick scan (20 minutes)

Pull in your metadata extract, normalize field names, and group clusters of duplicates. We’re not looking for perfection — just enough signal to see where duplicates or overlaps are hiding.

What usually jumps out:

• Fields that look different but mean the same thing (e.g., BillTo vs CustomerBillingAddress).
  
  
• The same field copied across objects, like billing address on both Account and Invoice.
  
  

Step 2: Make sense of the clusters (30 minutes)

This is where we translate the techy stuff into business language:

• Write a one-line summary for each cluster: what it’s capturing, where, and how much it’s used.
  
  
• Tag it: duplicate field, duplicate object, conflicting formula, automation overlap, integration shadow.
  
  
• Add quick metrics: percent populated, last updated.
  
  

This doesn’t have to be exhaustive. If 80% of the data sits in one field and 5% in another, you’ve already got your answer.

Step 3: Triage with a decision matrix (40 minutes)

Here’s the part stakeholders love. For each cluster, decide if it’s a quick win, a tactical fix, or a deeper project:

• Quick wins: Simple, high impact. Example: merge three billing fields into one.
  
  
• Tactical fixes: Moderate effort. Needs alignment. Example: migrate data and tweak integration middleware.
  
  
• Deep fixes: Heavy lifts. Needs change control. Example: refactor a multi-object data model.
  
  

For each one, note down: the owner, action, effort level, and whether it should flow into Sourcerer verification.

Walkthrough: how one duplicate cluster gets solved

Let’s say we’re in a sales org. We scan and find four fields capturing customer tier: Customer_Tier__c, AccountSegment, Acct_Tier, and TierLabel.

• Run: Normalized to tier, cluster size 4. Customer_Tier__c is on 72% of Accounts. The others barely register.
  
  
• Interpret: The summary: “Customer tier is captured inconsistently; canonical source should be Customer_Tier__c.” Category: duplicate field. Metrics: 72% vs 8% vs 25% populated.
  
  
• Triage: Decision: make Account the source of truth, migrate Opportunity data into a read-only reference. Effort: ~1 day. Hand off to Sourcerer with explicit test cases: Account tier propagates to Opportunity display.
  
  

That’s it. In under an hour, what looked like a messy cluster becomes a clear, testable fix.

Best practices we swear by (and traps to avoid)

Do this:

1. Keep it scoped. One domain per session.
   
   
2. Stick to quick metrics — good enough beats perfect.
   
   
3. Document decisions, not just fixes. Saves headaches later.
   
   
4. Lock down integrity checks before migration.
   
   

Avoid this:

• Over-normalizing and collapsing different business facts.
  
  
• Forgetting about integrations — what looks unused may power middleware.
  
  
• Skipping stakeholder sign-off. Changes won’t stick without it.
  
  

Danielle, a Netsuite architect, told us: “The system and the documentation gets stale very quickly and no one is updating that.” That’s exactly why we capture the decisions in writing.

From health check to Sourcerer

The beauty of this step is how neatly it feeds into Sourcerer Verify Logic. You walk away with:

• A canonical field list: the single source of truth.
  
  
• A migration map: keep, merge, deprecate, transform.
  
  
• Verification cases: inputs and expected outputs.
  
  

When you hand these to Sourcerer, verification stops being guesswork. Sourcerer checks the exact scenarios you flagged, cutting the feedback loop from days to hours.

Wrapping up

After 90 minutes, you should have:

• A one-page health-check summary with decisions and owners.
  
  
• A migration map for quick wins and tactical fixes.
  
  
• A Sourcerer-ready verification deck.
  
  

The health check is pragmatic, fast, and, in our experience, the highest leverage move you can make between mapping and verification.

Want to see it live? Book a technical demo, and we’ll run a scoped health check with you. You’ll walk away with a one-page report you can drop straight into your next sprint.

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