7 Dimensions of the Data Health Check in Marketo: More Than a Snapshot, a Compass

Think of the Data Health Check as a medical check-up for your database. It tells you whether your Marketo data is complete, correctly written, up to date, and useful. Because if your data is bad, your campaigns fail, your reports mislead, and your leads get lost along the way. In short: without healthy data, marketing crumbles.

To simplify things, the Data Health Check is broken down into 7 dimensions, like 7 key questions you ask each lead:

• Is it duplicated?
• Does it have all the necessary fields?
• Is it correctly written?
• Is it real or fake?
• Can I reach this person through any channel?
• When did they register?
• Are they still active or disengaged?

With those 7 answers, you can clean your database, automate with more precision, and ensure you’re working with actionable information — not noise.

And the most important part: this isn’t a snapshot — it’s a compass. It’s not about checking your data once and forgetting it. It’s about having a tool that continuously guides you, one you can automate and use to make decisions every day. Because data quality is not a destination — it’s a journey.

Before diving into each one, here’s a high-level map of the 7 variables that define your database’s health in Marketo:

1. Uniqueness – How many duplicate records do you have? Do you know what’s causing them?
2. Completeness – Do your leads contain the minimum info needed to work with them? Can you enrich them?
3. Normalization – Are the values formatted correctly to enable segmentation and automation?
4. Accuracy – Are the data points real, or do they just “look valid”?
5. Contactability – Can you actually reach these leads? Through which channels?
6. Recency – How fresh is the data?
7. Engagement – Is the lead alive or just taking up space?

Throughout this article, we’ll explore real-world examples of how these dimensions not only help you clean your data — they also help you convert more and waste less.

What does it measure?
It identifies duplicate records — but goes way beyond Marketo’s default “System Smart List” functions.

Why it’s different:
Marketo deduplicates primarily by email. If the email is different, it assumes it’s a different lead. But in reality, we all know people use different emails, especially when interacting across various touchpoints (webinars, events, websites).

Our approach:
We analyze combinations like:

• Full name + phone
• Full name + company
• Phone numbers with/without country codes

Real example:
Two different records: Fernando Santos, one using a personal email and one a corporate email. Both have the same mobile number — only one includes the international prefix. Our process identifies that it’s the same person and recommends unifying the records.

We also identify the source of duplicates (CRM, form, external integration) and propose ways to prevent them, not just correct them.

🛠 Bonus: When detecting duplicates, we can choose which data to keep: the most recent? The most complete? A custom mix?

What does it measure?
The percentage of relevant fields that are completed for each lead.

What makes it special?
It’s not just about measuring completeness — we identify which fields can be enriched automatically using existing data or APIs.

Examples:
🧠 Simple enrichment: Using internal logic rules — e.g., if we see the domain “@telefonica.es,” we assign “Telefónica” as the company and “Spain” as the country.

🚀 Advanced enrichment:
With Self-Service Flow Steps from Flowsteps.io, we can run powerful enrichment processes, like:

• LinkedIn Enrichment: Find a lead’s LinkedIn profile based on their email and enrich dozens of fields.
• Enrich with Clearbit: Enrich job title, company, location, and industry.
• OpenAI Job Title Role Classification: Enrich lead role based on job title with OpenAI. See a live demo of how this works in this webinar from minute 7.

What does it measure?
Whether data fields follow the expected format and values.

Why it matters:
Poorly written values lead to segmentation errors, failed automations, bad reports, and incorrect personalization.

Example:
A “Country” field with values like “España,” “SPAIN,” “Espana,” or “es.” The system won’t recognize them as the same country. Result: poorly segmented campaigns and leads excluded by mistake.

Our approach:

• Validate fields against master lists.
• Verify formats for phone, email, gender, industry, department, etc.
• Recommend automatic corrections and set up custom rules.

What does it measure?
Whether data entries are valid and real.

Key differentiator:
It’s not enough that a field is filled — what matters is whether it’s accurate and usable.

Script examples:
Phone numbers like “666666666” or emails like “peterpan@test.com” or “prueba123@fakemail.com” are technically valid, but worthless.

What we do:

• Validate formats and authenticity (e.g., proper length, valid domain).
• Detect fake patterns like “asdf@asdf.com” or names like “Mickey Mouse.”
• Recommend removing or replacing fields.

Important note: Many leads look complete but are actually fake. It’s better to leave a field blank than to fill it with garbage.

What does it measure?
The number of valid channels you have to reach each lead.

Why it matters:
If you can’t reach a lead, they might as well not exist.

Advanced layer:
We integrate with ZeroBounce for email validation and Twilio for phone verification — all manageable from Flowsteps.io.

Real case:
In a database of ~80,000 contacts:

• Nearly 10,000 couldn’t be reached via email.
• More than 50% had invalid phone numbers due to poor formatting (+40,000 contacts).
• 4,000+ only had Gmail or Hotmail addresses — no corporate emails, which were critical for this client.

Result: Only ~35,000 leads were actually useful. The rest inflated metrics and license costs.

What does it measure?
The age of the record and the time-based evolution of your database.

Why it matters:
Data expires. People change jobs, emails, and locations. Yesterday’s MQL could be irrelevant today.

Example:
A lead created in 2019 with no activity since 2020 — is it worth continuing to contact them or paying for them?

We also cross-reference recency with other metrics:

• Are your newer leads more or less complete than before?
• Are new forms capturing junk data?

📈 This allows you not only to clean, but also to improve your acquisition strategy and analyze your data sources.

What does it measure?
Recent activity from leads (opens, clicks, form submissions, web visits, etc.).

Why it matters:
Quantity doesn’t equal quality. What matters is who’s actually engaging.

Example:
A lead might have been in your database for years with perfectly completed fields — but hasn’t opened a single email in 18 months. Is that really a valuable lead?

What we do:

• Segment by activity period.
• Classify by engagement level.
• Recommend actions: reactivation, removal, or slow nurturing.

Each dimension produces an individual score per lead and an aggregate score for the full database.

This allows you to:

1. Get an objective, instant diagnosis
2. Measure the impact of improvements
3. Monitor over time
4. Perform advanced segmentation (e.g., “leads with accuracy >80 and engagement >60”)

Thanks to Flowsteps.io, you can implement all the Self-Service Flow Steps and start measuring your database quality in under two hours.

This enables processes like:

• Running the Data Health Check periodically inside Marketo as part of a Smart Campaign
• Storing scores and recommendations in custom fields
• Using those scores as criteria in campaigns, alerts, or dashboards

Example:
Send campaigns only to leads with an overall quality score >70. Or trigger alerts when a new lead source starts lowering normalization or accuracy.

We understand it’s not a simple process — but we can help you every step of the way by connecting you with one of our specialists.

The Data Health Check is not just an audit. It’s a strategic, automatable, and measurable practice that transforms how you manage your data. It goes beyond a mere “status report” — it becomes a compass for all your marketing decisions.

Because in the end, our goal isn’t just to fill forms, databases, or CRMs. It’s to generate real conversations that turn into opportunities and sales. And that can only happen with clean, structured, trustworthy, and dynamic data.

“Every data point that doesn’t add value, destroys it.”
That phrase sums it all up.

So, do you know how healthy your database really is?