Business leaders need reliable information to make smart decisions. But let's be honest — "data quality" has always been a murky concept.
What does "good enough" data actually look like?
What's stopping teams from trusting and acting on their data?
And how can we overcome resistance and conflict by eliminating "data quality" as an excuse to dismiss valuable insights?
If this feels a little too relatable, you're not alone. We've all seen it: leaders defaulting to their gut instincts over data, brushing off insights with a dismissive, "That doesn't feel right."
Imagine a world where data is trusted by the executive team and decisions are made with confidence, not skepticism. That's the future we're working toward.
That's why Openprise teamed up with RevOps Co-op and MarketingOps to tackle the big questions:
- What does "data quality" really mean, and how do teams define it?
- What's holding people back from trusting their data — is it purely a technical issue, or are there deeper challenges?
- What sets high-performing organizations with trusted data apart from those struggling with poor data quality?
We know poor data quality is a problem — it leads to questionable decisions, paralyzes go-to-market teams, and creates friction between departments. But this year's State of RevOps Survey goes deeper: it reveals why these issues persist, how leadership often (unknowingly) fuels them, and what steps can be taken to break the cycle.
The data is clear: it's time to stop tolerating bad data and start tackling data quality issues head-on.
Why data quality feels impossible (and what's fueling the struggle)
A staggering 99% of survey respondents admit to struggling with technical data challenges. These include:
- 80% missing or incomplete data
- 75% duplicate records
- 59% non-standardized data
- 52% inconsistent data across systems
- 49% disconnected or siloed data
- 31% outdated or unavailable data
Even with efforts to consolidate SaaS tools in 2022, most businesses still averaged 91 technologies in their RevTech stacks. Managing data across this sprawling ecosystem presents ongoing challenges, such as:
- Juggling multiple enrichment vendors with different data standards
- Merging data from incompatible schemas and identifiers
- Syncing systems without overwriting fresher, more accurate data
- Cleaning up stale or outdated data due to customer churn, company closures, acquisitions, or staff turnover
And then there's the wildcard: the very tools designed to manage customer data often make the problem worse.
Take CRMs, for example. Many of the biggest players segment people into two separate, unrelated objects (leads vs. contacts). Add to that their data governance features, which allow administrators to restrict access and visibility, and you've got a recipe for chaos — duplicate records, confusion about who's actually a customer, and an inconsistent customer experience.
One survey respondent summarized it perfectly: "The most frustrating aspect of managing data within our organization is the multitude of systems housing critical information. While some of these systems are integrated, others are not, leading to significant discrepancies."
The CRM data governance gap underneath every technical problem
Look closely at the technical failure rates above and a pattern emerges. The symptoms — duplicate records, missing fields, non-standardized values, siloed data — are not independent problems. They are the predictable downstream effects of absent CRM data governance.
CRM data governance is the set of policies, ownership structures, standards, and enforcement mechanisms that determine how data enters, moves through, and gets maintained in your CRM and connected systems. In practice, functional CRM data governance answers four questions that most RevOps teams have never formally addressed:
- Who owns each data domain? (Who is accountable when company name formats are inconsistent, or when job title fields are blank on 40% of records?)
- What does "correct" look like for each field? (What is the approved format for country names, phone numbers, revenue ranges, or industry categories?)
- How are standards enforced? (Is enforcement happening at the point of data entry, through automated validation, or not at all?)
- How is quality monitored over time? (Is there a process for detecting when data degrades, or does the team only find out when a campaign underperforms or a report looks wrong?)
Most organizations experiencing the technical symptoms in the survey above cannot answer all four of these questions. That's not a technology gap — it's a governance gap. The tools to enforce standards, validate records, and normalize field values exist. What's missing is the ownership structure and cross-functional agreement that tells those tools what to enforce and who is accountable when they don't.
This distinction matters because it changes where the fix needs to come from. Technical problems require technical solutions. Governance gaps require leadership decisions.
Leaders are a big part of the data quality problem
The most compelling insight from this year's survey is that data quality is as much a leadership issue as it is a technical one.
A staggering 59% of survey respondents are caught in the crossfire of technical and leadership-related data quality problems. Even more striking, an additional 36% say leadership issues are the only factor holding their organization back. This isn't just a technical issue — it's a leadership issue.
Here's where leadership falls short in organizations with poor data quality:
- 79% don't have a standard definition of what "data quality" even means
- 55% say adoption of key systems is not enforced by leadership
- 48% report that their leadership team doesn't understand what is and isn't possible from a technical perspective
Leaders often fail to prioritize data quality, dismissing it as a low-value initiative or assuming it's purely a technical issue. This lack of support leads to cascading problems — unaddressed bad data, conflict between teams, and lost revenue opportunities.
As one respondent explained, "The leadership team does not see an issue with data quality and is not putting incentives out for users to adhere to data quality standards. Also, there is no budget for tools or FTEs to deal with this problem."
The result? Operations teams are left to clean up the mess without the budget, tools, or leadership backing to drive lasting change.
Each of the three statistics above maps directly to a CRM data governance failure: no shared definition is a standards failure, unenforced adoption is an accountability failure, and leadership misunderstanding of technical capability is a governance design failure. When all three are present simultaneously — as they are in a significant share of the organizations surveyed — no amount of technical tooling will fix the underlying problem.
Rimini Street's experience is a concrete illustration. Their ops team had accumulated years of data problems through one-off projects that each introduced new fields, new automation, and new logic without a coordinating governance structure. A team of 23 data analysts was spending the majority of its time not on strategic work, but chasing down data errors, fielding ad-hoc list requests, and manually correcting inconsistencies that a functioning governance model would have prevented. The fix required more than better tooling. It required establishing formal governance infrastructure: after deploying Openprise to automate data quality processes and replace multiple point solutions, Rimini Street formed a Data Governance Committee specifically to own and evolve data quality standards going forward. The operational results were immediate:
- 108 hours per week saved in manual data effort
- 40 ops tickets reduced weekly
- Multiple point solutions consolidated into a single governed platform
Without Openprise, we would need to hire approximately 15 additional FTEs to manually do the work.
The governance committee is what sustains those gains. Without it, the same accumulation of ungoverned data processes would simply rebuild over time.
B2B GTM teams: stop settling for less — here's how to fix data quality together
The truth is, fixing data quality isn't something operations teams can do alone. It requires leadership buy-in, cross-functional collaboration, and a clear roadmap for improvement. Here's how to get your leaders to lean harder into data quality initiatives:
- Build a compelling business case. Leadership cares about ROI, so frame the data quality problem in terms of dollars and impact. Highlight how poor data is costing your business — whether it's in missed revenue opportunities, inefficiencies, or internal conflict — and show the measurable benefits of solving it.
- Get leaders to define and own data quality. Work with leadership to establish a standard definition of data quality for your organization. When leaders co-create the definition, they're more likely to enforce it across teams. Ownership starts with participation.
- Prioritize hygiene initiatives by data type. Not all data is created equal. Identify which data types have the most significant impact on your revenue engine — whether it's customer contact details, sales opportunities, or product usage data — and prioritize those for cleanup and maintenance.
- Communicate wins to build momentum. Keep leadership engaged by sharing quick wins and measurable progress. Show how improved data quality is driving better decision-making, reducing inefficiencies, or enhancing the customer experience. Data quality improvements are a journey, but tangible results will keep leaders invested.
- Become a data detective (and educator). Plan to spend time each week identifying the root causes of data "myths" and legitimate issues. Use this as an opportunity to educate your teams on how problems have been addressed, reducing confusion and building trust in the data.
Tackling data quality challenges takes consistent effort and leadership participation — but the payoff is worth it.
What CRM data governance looks like when it works
The five steps above describe how to build internal support for data quality. But what does functional CRM data governance actually look like as an operational structure? Based on what distinguishes high-performing RevOps organizations from those still fighting the same problems year over year, four pillars consistently appear.
Shared definitions, documented and enforced. The 79% of organizations without a standard definition of data quality aren't just lacking agreement — they're lacking documentation. Functional CRM data governance produces a written data dictionary: what each field means, what values are acceptable, what format is required, and who is responsible for maintaining it. This document doesn't need to be exhaustive on day one, but it needs to exist and be enforced.
Named ownership at the field and domain level. Someone needs to own the state of company name formatting. Someone needs to own the lead source taxonomy. Someone needs to own the territory assignment logic. Governance without named owners produces the same outcome as no governance at all — nobody acts when something degrades because nobody is accountable.
Automated enforcement at the point of entry. The most efficient governance is prevention rather than correction. Validation rules, normalization scripts, and deduplication checks that run when a record enters the system catch problems before they compound. This is where a data orchestration platform does work that a CRM alone cannot — applying consistent rules across multiple data sources, enrichment vendors, and integration points without requiring manual review.
Continuous monitoring with defined thresholds. Governance without measurement is a policy document, not a practice. High-performing RevOps teams track data quality KPIs — field completion rates, duplicate rates, normalization compliance rates — on a regular cadence and set thresholds that trigger action when quality degrades. The 2025 State of RevOps Survey found that organizations with better data quality are significantly more likely to use a designated platform that's automatically integrated with their tools (49% vs. 33%). That integration is what enables monitoring at scale.
The goal of CRM data governance isn't to achieve perfect data. It's to build a system where data quality is maintained continuously as a background function rather than restored periodically as an emergency project.
Get the full picture
Want to dive deeper into the findings and solutions? Download the 2025 State of RevOps Survey: Data quality's impact on GTM execution to uncover:
- What's really holding organizations back from trusted, actionable data
- How to overcome technical and leadership-related data quality challenges
- Proven strategies from teams that have successfully solved these problems
Stop settling for "good enough" and take the first step toward trusted, impactful data. Download the report now and start building the foundation for better decision-making.
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