If you've ever sat in a GTM meeting and heard someone throw out “our data is bad,” you’re not alone. It's a phrase that gets tossed around all the time — vague, frustrating, and dangerously unhelpful. Because here’s the thing: saying your data is “bad” doesn’t actually get you any closer to solving the problem.
Angela Cirrone, Senior Director of Marketing Technology & Operations at Workiva, hears it all the time too:
“The phrase ‘Our data is bad’ comes up frequently within GTM teams.”
But instead of letting it slide, Angela starts asking better questions — questions that challenge teams to diagnose the real issues instead of blaming the data.
It's not the data — it's what's behind it
Most teams blame the data. Angela challenges them to dig deeper:
“What specifically makes our data ‘bad’? Is it the accuracy, consistency, structure, freshness—or perhaps the sheer lack of it?”
She doesn’t let GTM teams stay at the surface. Because more often than not, data quality issues stem from:
Misaligned or undefined processes
Unclear ownership across departments
Gaps in visibility between GTM teams
Until your team aligns on what good data looks like and who owns what, no AI enrichment or tooling layer will make a difference. And the chaos in the CRM will keep growing.
Diagnose before you invest
Before you can fix your data, your teams need to agree on what "good" even means.
What “good” data actually looks like (and how to align around it)
Attribute | Definition | Why It Matters |
|---|---|---|
Accuracy | Data reflects real, current information (e.g. correct title or company) | Prevents wasted outreach and bouncebacks |
Completeness | Key fields are consistently captured (mobile, email, persona, firmographics) | Powers scoring, routing, segmentation |
Consistency | Fields are formatted the same way across records and teams | Keeps filters, reports, and syncs working cleanly |
Freshness | Data is updated frequently to reflect job moves, company changes, etc. | Helps reps trust what they’re working with |
Relevance | Data captured is essential — not just “more” | Keeps CRM lean and enables faster GTM workflows |
Use this to guide alignment between sales, marketing, and ops teams. Everyone doesn’t need everything — but they do need to agree on the non-negotiables.
Most companies default to quick fixes: buy more data, tack on another enrichment tool, or layer in automation. But as Angela Cirrone points out, those are surface-level solutions. The real work starts with asking better questions.
Instead of labeling data as simply “bad,” Angela recommends stepping back and digging into the actual challenges: How are we defining data quality across teams? Where are the biggest gaps in decision-making caused by bad data? Who is truly accountable for improving and governing that data over time?
“Effective sales and marketing alignment hinges on a shared understanding and accountability for data management across the entire GTM organization.”
Without that foundation, you're just scaling dysfunction.
Don’t let automation scale the mess
AI enrichment tools are everywhere. But without clarity and governance, they just multiply the mess. Angela’s team doesn’t chase automation until they’ve aligned on the fundamentals.
That’s how you move from fire drills to flywheels.
“By reframing the conversation this way, we shift from frustration and ambiguity to clarity, ownership, and actionable improvements.”
A conversation starter for your next data review
Use this checklist to evaluate whether your team is focused on surface-level symptoms — or tackling the real issue:
Have we defined data quality standards across sales, marketing, and ops?
Do we have clear field ownership and documentation in the CRM?
Are we treating AI and enrichment tools as solutions — or as bandaids?
Do we know which teams are responsible for fixing different types of errors?
Are we proactively surfacing data issues with real workflows — or reacting once it’s too late?
Final thought: Don’t settle for “good enough” data
Angela’s perspective is a reminder that data quality isn’t just an ops problem. It’s a team alignment problem. One that needs real definitions, real accountability, and real collaboration.
If your team is stuck saying “our data is bad,” try pausing to ask the better questions. You might just find the clarity you've been missing.
If you've ever sat in a GTM meeting and heard someone throw out “our data is bad,” you’re not alone. It's a phrase that gets tossed around all the time — vague, frustrating, and dangerously unhelpful. Because here’s the thing: saying your data is “bad” doesn’t actually get you any closer to solving the problem.
Angela Cirrone, Senior Director of Marketing Technology & Operations at Workiva, hears it all the time too:
“The phrase ‘Our data is bad’ comes up frequently within GTM teams.”
But instead of letting it slide, Angela starts asking better questions — questions that challenge teams to diagnose the real issues instead of blaming the data.
It's not the data — it's what's behind it
Most teams blame the data. Angela challenges them to dig deeper:
“What specifically makes our data ‘bad’? Is it the accuracy, consistency, structure, freshness—or perhaps the sheer lack of it?”
She doesn’t let GTM teams stay at the surface. Because more often than not, data quality issues stem from:
Misaligned or undefined processes
Unclear ownership across departments
Gaps in visibility between GTM teams
Until your team aligns on what good data looks like and who owns what, no AI enrichment or tooling layer will make a difference. And the chaos in the CRM will keep growing.
Diagnose before you invest
Before you can fix your data, your teams need to agree on what "good" even means.
What “good” data actually looks like (and how to align around it)
Attribute | Definition | Why It Matters |
|---|---|---|
Accuracy | Data reflects real, current information (e.g. correct title or company) | Prevents wasted outreach and bouncebacks |
Completeness | Key fields are consistently captured (mobile, email, persona, firmographics) | Powers scoring, routing, segmentation |
Consistency | Fields are formatted the same way across records and teams | Keeps filters, reports, and syncs working cleanly |
Freshness | Data is updated frequently to reflect job moves, company changes, etc. | Helps reps trust what they’re working with |
Relevance | Data captured is essential — not just “more” | Keeps CRM lean and enables faster GTM workflows |
Use this to guide alignment between sales, marketing, and ops teams. Everyone doesn’t need everything — but they do need to agree on the non-negotiables.
Most companies default to quick fixes: buy more data, tack on another enrichment tool, or layer in automation. But as Angela Cirrone points out, those are surface-level solutions. The real work starts with asking better questions.
Instead of labeling data as simply “bad,” Angela recommends stepping back and digging into the actual challenges: How are we defining data quality across teams? Where are the biggest gaps in decision-making caused by bad data? Who is truly accountable for improving and governing that data over time?
“Effective sales and marketing alignment hinges on a shared understanding and accountability for data management across the entire GTM organization.”
Without that foundation, you're just scaling dysfunction.
Don’t let automation scale the mess
AI enrichment tools are everywhere. But without clarity and governance, they just multiply the mess. Angela’s team doesn’t chase automation until they’ve aligned on the fundamentals.
That’s how you move from fire drills to flywheels.
“By reframing the conversation this way, we shift from frustration and ambiguity to clarity, ownership, and actionable improvements.”
A conversation starter for your next data review
Use this checklist to evaluate whether your team is focused on surface-level symptoms — or tackling the real issue:
Have we defined data quality standards across sales, marketing, and ops?
Do we have clear field ownership and documentation in the CRM?
Are we treating AI and enrichment tools as solutions — or as bandaids?
Do we know which teams are responsible for fixing different types of errors?
Are we proactively surfacing data issues with real workflows — or reacting once it’s too late?
Final thought: Don’t settle for “good enough” data
Angela’s perspective is a reminder that data quality isn’t just an ops problem. It’s a team alignment problem. One that needs real definitions, real accountability, and real collaboration.
If your team is stuck saying “our data is bad,” try pausing to ask the better questions. You might just find the clarity you've been missing.
If you've ever sat in a GTM meeting and heard someone throw out “our data is bad,” you’re not alone. It's a phrase that gets tossed around all the time — vague, frustrating, and dangerously unhelpful. Because here’s the thing: saying your data is “bad” doesn’t actually get you any closer to solving the problem.
Angela Cirrone, Senior Director of Marketing Technology & Operations at Workiva, hears it all the time too:
“The phrase ‘Our data is bad’ comes up frequently within GTM teams.”
But instead of letting it slide, Angela starts asking better questions — questions that challenge teams to diagnose the real issues instead of blaming the data.
It's not the data — it's what's behind it
Most teams blame the data. Angela challenges them to dig deeper:
“What specifically makes our data ‘bad’? Is it the accuracy, consistency, structure, freshness—or perhaps the sheer lack of it?”
She doesn’t let GTM teams stay at the surface. Because more often than not, data quality issues stem from:
Misaligned or undefined processes
Unclear ownership across departments
Gaps in visibility between GTM teams
Until your team aligns on what good data looks like and who owns what, no AI enrichment or tooling layer will make a difference. And the chaos in the CRM will keep growing.
Diagnose before you invest
Before you can fix your data, your teams need to agree on what "good" even means.
What “good” data actually looks like (and how to align around it)
Attribute | Definition | Why It Matters |
|---|---|---|
Accuracy | Data reflects real, current information (e.g. correct title or company) | Prevents wasted outreach and bouncebacks |
Completeness | Key fields are consistently captured (mobile, email, persona, firmographics) | Powers scoring, routing, segmentation |
Consistency | Fields are formatted the same way across records and teams | Keeps filters, reports, and syncs working cleanly |
Freshness | Data is updated frequently to reflect job moves, company changes, etc. | Helps reps trust what they’re working with |
Relevance | Data captured is essential — not just “more” | Keeps CRM lean and enables faster GTM workflows |
Use this to guide alignment between sales, marketing, and ops teams. Everyone doesn’t need everything — but they do need to agree on the non-negotiables.
Most companies default to quick fixes: buy more data, tack on another enrichment tool, or layer in automation. But as Angela Cirrone points out, those are surface-level solutions. The real work starts with asking better questions.
Instead of labeling data as simply “bad,” Angela recommends stepping back and digging into the actual challenges: How are we defining data quality across teams? Where are the biggest gaps in decision-making caused by bad data? Who is truly accountable for improving and governing that data over time?
“Effective sales and marketing alignment hinges on a shared understanding and accountability for data management across the entire GTM organization.”
Without that foundation, you're just scaling dysfunction.
Don’t let automation scale the mess
AI enrichment tools are everywhere. But without clarity and governance, they just multiply the mess. Angela’s team doesn’t chase automation until they’ve aligned on the fundamentals.
That’s how you move from fire drills to flywheels.
“By reframing the conversation this way, we shift from frustration and ambiguity to clarity, ownership, and actionable improvements.”
A conversation starter for your next data review
Use this checklist to evaluate whether your team is focused on surface-level symptoms — or tackling the real issue:
Have we defined data quality standards across sales, marketing, and ops?
Do we have clear field ownership and documentation in the CRM?
Are we treating AI and enrichment tools as solutions — or as bandaids?
Do we know which teams are responsible for fixing different types of errors?
Are we proactively surfacing data issues with real workflows — or reacting once it’s too late?
Final thought: Don’t settle for “good enough” data
Angela’s perspective is a reminder that data quality isn’t just an ops problem. It’s a team alignment problem. One that needs real definitions, real accountability, and real collaboration.
If your team is stuck saying “our data is bad,” try pausing to ask the better questions. You might just find the clarity you've been missing.
Author
Mark Bedard
Chief Executive Officer
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