Data Contracts: How to Fix Most Broken MarTech Workflows
They're the 'have you turned your computer off & on again? of MarTech
The Hidden Cause of Most MarTech Failures
MarTech stacks break all the time.
It’s usually a human’s fault.
Searching for scapegoats is usually a bad use of time; searching for root causes isn’t.
And one of the most common reasons for issues is that data structures change without warning—A developer renames a field, a SaaS upgrade modifies a data format, or a third-party vendor stops including a key field in their feed.
Suddenly, things stop working and your team is scrambling to find out why.
Even worse, sometimes these errors happen silently!
Especially if all the individual tools are working, it’s hard to know there’s a problem.
This lights-on-no-one-home issue is exactly what data contracts prevent.
Data contracts are not just for developers.
Data contracts help marketing teams avoid expensive mistakes, keep workflows running smoothly, and reduce the number of times a simple error leads to a broken campaign or downed site and lost revenue.
What is a Data Contract?
A data contract is just an agreement about how different MarTech tools share and structure data.
It’s like a restaurant menu:
The menu tells customers (your marketing tools) what they can order (what data is available).
If a dish suddenly changes its name or gets removed from the menu, but no one updates the printed menu, customers get frustrated.
They’ll try to order what’s on the menu, but the kitchen will bring the new dish out.
For example, if a Subway kept up the $5 footlong ads, but charged me $10 while I was there—I’d be surprised and storm out.
Likewise, if your CRM renames "email" to "contact_email" and your email marketing tool is still expecting "email," your entire workflow breaks.
A data contract ensures teams and tools stay on the same page, so changes don’t cause unexpected failures.
What do data contracts look like?
Anatomy of a Good Data Contract
Key elements of a data contract:
Field name and type (e.g.,
customer_id: STRING)Format (e.g.,
date_of_purchase: YYYY-MM-DD)Constraints (e.g.,
email: must be valid email format)Versioning (e.g., tracking changes over time)
This is generally best used as a future proofing practice so that you can clearly do analysis with an eye towards when data series might be discontinuous. For example, if purchase date has been an ongoing back-and-forth between two teams, then record the different series!
Ownership (e.g., which team is responsible?)
Example Data Contract Table:
They can look overwhelming, but don’t worry about starting at the finish line.1
Real-World Example: A Legacy Data Format Disaster
One client had a data pipeline that relied on legacy text files.
A third-party vendor provided customer data, then had an internal app had to ingest data into their internal system, which was then processed and distributed across their MarTech stack.
Then, the preprocessing app team pushed an update. They switched from text files to JSON.
Suddenly, all downstream applications started throwing errors. The entire system went down for hours, with multiple teams scrambling to debug the issue.
What went wrong?
The downstream development teams weren’t aware of the format change.
There was no data contract specifying that this pipeline expected text files and couldn’t process JSON.
If the contract had existed, the format change would have been caught before deployment—saving time, money, and stress.
Balancing Agility vs. Structure: When to Introduce Data Contracts
Some teams worry that formal documentation will slow them down2. But the reality is, the bigger your company, the more costly missing documentation becomes.
When should you formalize data contracts?
If your entire MarTech team can still eat one pizza for company lunch, informal agreements might work.
But once no single pod of developers knows everything about the tech stack, it’s time for actual documentation.
❌ No, "self-documenting code" is NOT a real solution! Whoever came up with the idea of self-documenting code should be in a straightjacket
Why Fixing Errors Early is Worth It
A broken workflow caught early is 100x cheaper to fix than one that fails in production.
Fixing an issue in:
✅ Development = 1x cost
⚠️ QA Testing = 10x cost
🚨 Production = 100x cost (lost revenue, broken campaigns, compliance issues)
Example:
If a data format mismatch breaks an ad campaign, that’s real lost money while teams scramble to fix it in production.
With basic data contracts, you catch issues in dev (1x cost) instead of prod (100x cost).
How to Introduce Data Contracts Without Overhauling Everything
You don’t need a full-blown contract system to get value from data contracts. Start small:
Create an architecture diagram (or a simple text document) listing:
What systems exist in your MarTech stack
Examples: Salesforce, SurveyMonkey, Braze
What data moves between them
Examples: Customer email, date of birth, array of previous contacts, purchases last 30 days
Who owns each data source
Assign ownership:
Who is responsible for maintaining data integrity?
This should be a single person ideally—a single team is acceptable.
If a change is needed, who should be notified?
This can be a larger group for notification purposes
Set up a simple approval process:
Before changing field names, data formats, or structures, document the impact on downstream tools.
Even an email listserv for data change approvals is better than nothing.
This helps non-technical teams know who to ask before making changes. Even without listing specific fields, this one document prevents silos and surprises.
Final Takeaways for Non-Technical Marketers
💡 You don’t need to be a developer to care about data contracts.
If you’ve ever had a campaign break because of missing data, a renamed field, or a broken integration, a simple data contract could have saved you.
✔ They prevent hidden errors from wrecking marketing campaigns.
✔ They reduce wasted ad spend and automation failures.
✔ They make adding new tools / auditing old ones much easier
✔ They make troubleshooting faster
The cost of NOT having them is higher than the effort of implementing them.
Try mapping your MarTech data flows today—it’s a small step that could save you tons of time in headaches later.
Feel free to reach out if you have questions on how to best approach your company’s tech stack.
Data Contracts Can Look Overwhelming (But Aren't)
Initial perception: They seem complex—full of specifications and rules.
Reality: They simplify troubleshooting and make teams more autonomous.
Analogy: Think of them like Google Maps for your data—it tells you where everything is, so you don’t get lost when something changes.
It really seems like this is a bimodal distribution with only the extremes. Some teams are process-oriented and LOVE detailed docs—some find documentation anathema. The right balance is usually in between, but it depends on the company.
For example, NASA is notorious for extremely exacting docs and standards on their code—for good reason! But that level of pre-planning would be paralyzing for a B2C low-code website startup.