We all know that dirty data is a silent killer of marketing automation efficiency. Whether you’re trying to sync leads to your CRM, segment for a campaign, or just get reliable reporting, inconsistent or incomplete data can derail the best-laid plans.
At one point, I decided to tackle this challenge head-on by building what I call my “Data Washing Machine” (DWM) in Marketo – a modular, centralized, and semi-automated program designed to clean and standardize lead data across multiple workspaces. I’ll be honest: I probably overengineered it. But the end result has been a reliable and scalable solution that I now rely on daily.
The Vision
I gave myself following design goals and constraints – my program needs to be:
- Fast: It needs to run quickly and efficiently. No bottlenecks.
- Modular: Each function should be separated, easy to expand or modify.
- Centralized: It needs to work across multiple Marketo workspaces.
- Access: It must run automatically in the background, but also be triggerable (on-demand).
Four Pillars of Clean Data
I organized the logic into four core categories. This framework helped me structure the work and keep things scalable.
1. Enrich: This is the foundation: identify key fields that are empty and populate them based on available information. For example, if a record is missing value on Language and is from Germany, we will prepopulate language preferences with German.
2. Enrich & Correct: An expansion of Enrich. These operations ensure derivative fields stay in sync as the record evolves. A common example: our “Region” field is derived from “Country.” If someone first submits a form with Country=Norway and later one with Country=Germany, we update the region accordingly to reflect the most accurate region (Nordic -> Germanic).
3. Standardize: This step converts free-text chaos into consistent values – turning all the “US”, “USA”, and “United States of America” variants into a single, CRM-desired “United States”.
4. Clear: Some fields outlive their usefulness. For instance, certain fields could be relevant only for a lead stage, they can be cleared upon conversion to a Salesforce Contact.
The Execution Layer
I chose to decouple logic as much as possible. For each data operation on a specific field, I created a dedicated Smart List (to filter relevant records) and a dedicated Smart Campaign (to execute the update).
Example: Enrich/Correct Region
- Smart List: Includes logic like: (Country = Germany OR Austria AND Region ≠ Germanic) AND (Country = France OR Monaco AND Region ≠ France)…
- Smart Campaign: If Country = Germany/Austria → set Region = Germanic; If Country = France/Monaco → set Region = France; and so on…
This design makes the logic visible, maintainable, and reusable. Some operations, like standardizing the “State” field or normalizing “Country” names (we use Google’s address API, which produces many variants), required dozens or even 130+ conditional steps.
The beauty? You can start small – just focus e.g. on the fields that block your CRM integration – and expand over time.
Automating the Machine
Once the logic was in place, I turned to automation. I implemented two entry points:
- Automated: 24 scheduled Smart Campaigns running hourly to handle core fields. (contain our logic Smart lists)
- On-Demand: A custom boolean field called “Data – Needs Cleaning”. Flipping this flag triggers a campaign that resets it and runs a full cleaning sequence.
This gave us flexibility: we minimized heavy triggered logic (which can strain the system), while still allowing users or programs to “request” cleaning on demand. The on-demand entry point also became a centralized launchpad, accessible from any workspace.
Lessons Learned (and Pain Points)
Even the best-engineered system has quirks. A few key challenges I encountered:
- Test fast and on a small scale.
- Cross-workspace triggering required the custom “Data – Needs Cleaning” field.
- Prioritization became essential. I eventually tiered operations, so high-priority fields update instantly, and others are executed within an hour.
- User onboarding and education took continuous effort. Reminding teams of the system’s value and quirks (e.g., necessary wait steps) was key.
- Activity logs became cluttered. Even if five fields were evaluated, often only one was actually changed. Good for audit trail, but overwhelming to troubleshoot.
Final Thoughts
No matter how clean your forms or how disciplined your users, your Marketo instance will eventually need a structured data program. You won’t catch every inconsistency at the source, but with the right approach, you can minimize the impact and keep your integration and segmentation efforts healthy.
If you’re starting from scratch, don’t overengineer it – at least not immediately 🙂 Focus on the critical fields that are breaking things. Build the logic modularly, keep it visible, and grow over time. You’ll thank yourself later.