What executives look for in a data quality platform
Data quality strategy now functions as a governance and risk discipline, with executives weighing metrics, ROI accountability and data trust as indicators of enterprise reliability.
For decades, data management professionals have argued that their work should be seen as a strategic issue rather than a technical housekeeping task. Once dismissed as old-fashioned, that argument has resurfaced at the executive level in 2026, with the analyst firm BARC identifying data quality -- closely followed by security – as a top priority.
An accompanying article from BARC CEO Carsten Bange observes, "What began as excitement over a breakthrough promising more automation, productivity, and intelligence has matured into a more balanced discussion -- one that carefully weighs costs against benefits."
As a result, data quality is no longer viewed solely as an IT responsibility but as an enterprise-wide governance commitment. The stakes have risen since AI systems can hallucinate or reproduce unwanted bias, enabling errors to spread much faster than with earlier technologies. Executives are relearning the lesson of garbage in, garbage out, with generative AI amplifying failures at scale.
To manage this risk, executives expect data quality to be measured, monitored and audited reliably and consistently, and they require governance frameworks that keep pace with AI adoption. This increases scrutiny on the tools and platforms that support data governance, analytics, AI deployment and performance management.
From backroom to boardroom
Data quality was once the province of database administrators and IT ops teams. As practitioners, they generally worked with six dimensions: accuracy, validity, completeness, uniqueness, consistency and timeliness. These measures assumed that data recorded objective business facts and that quality meant ensuring those records were complete and consistent.
These dimensions remain important as technical measures, but machine learning introduces a different kind of dependency. AI models don't simply store data; they learn patterns from it, which can encode bias or assumptions as facts. As a result, executives concerned with deploying AI ethically and responsibly also must consider whether data sets are transparent, fair, secure and representative.
This shift explains why data quality has moved from a technical concern to an executive-level concern. Quality now reflects not only correctness, but what organizations permit systems to learn and reproduce. Governance, therefore, extends beyond technical policies and controls to include accountability for outcomes.
At the same time, executive evaluation remains grounded in business value. Ethical risk and governance maturity increasingly factor into platform decisions, alongside ROI and operational performance.
ROI and total cost of ownership
Before approving any substantial platform investments, executives demand quantifiable justification. The evaluation question has shifted from platform price to exposure risk: What does poor data quality cost today, and how much of that risk does the platform reduce?
The platforms that win budget approval demonstrate how this investment protects the reliability of decision-making and the organization's reputation.
The costs of investing in data quality platforms are visible and upfront, including licensing fees, implementation effort and training. But the costs of poor data quality are distributed across the business, making them harder to trace. Customer attrition might rise if support teams rely on outdated or incorrect records, but that might not surface as a data quality problem. Similarly, budgets and forecasts built on flawed data might lead to poor spend management, which is rarely traced back to data.
The benefits of data quality are often incremental and preventive. Slightly better decisions across thousands of interactions, or problems avoided because they were caught early, are difficult to quantify, but central to executive evaluation.
As a result, many organizations do not tackle data quality strategically until a crisis arises, which forces the issue. Instead, organizations typically adopt one of three postures toward data quality:
Do nothing and treat poor quality as a cost of doing business and accept the inherent risk.
Rely on reactive remediation, correcting errors only after problems surface. This reactivity is often too late to prevent damage.
Use proactive remediation, which focuses on identifying the data records most critical to the business, defining quality rules for those records, and monitoring compliance to find issues early. Executives today increasingly favor this approach.
Proactive approaches require upfront investment in tools and processes, but do not require universal coverage of every data element. Targeted monitoring of high-value datasets can reduce cascading errors across analytics and AI pipelines, shifting effort from manual cleanup to prevention.
When executives evaluate the total cost of ownership of platforms, they weigh identifiable platform costs against the potential costs of inaction. Platforms that support this evaluation by making incidents, remediation efforts and governance exposure visible are more likely to receive approval. This is a valid approach, but it requires discipline. It requires tracking the rate of data quality incidents, remediation time and compliance violations, and estimating the cost of those incidents. Most organizations don't do this systematically.
Interoperability and observability
The selection of a data quality platform depends on how well it integrates with existing systems across complex cloud and hybrid environments. Executives evaluating platforms look for interoperability that reduces fragmentation rather than adding another isolated tool to the stack.
Open standards offer one practical response to this sprawl of tools across the network. For example, OpenTelemetry provides a vendor-neutral framework for instrumentation. Organizations can use a distributed tracing architecture to consolidate logs, metrics, traces and quality monitoring into a single view, making it easier for operations teams and executives to oversee coherent reporting.
The key question is whether a platform consolidates observability across the data landscape or if it introduces additional complexity. Platforms that improve coherence across monitoring, reporting and governance are more likely to align with executive expectations.
The vendor landscape and selection criteria
Traditional vendor rankings by analysts often fail to reflect the complexity of the data quality landscape. As a result, CDOs and other executives tend to structure their comparisons around categories of capabilities rather than vendor position.
Rather than comparing products directly, leaders assess whether a platform supports core governance, quality and observability functions across the data lifecycle. The emphasis is on how capabilities align with existing architectures and executive priorities, not on category labels.
In practice, executive evaluation criteria tend to cluster around four questions:
Structural fit. How well does the platform integrate with existing infrastructure, including ERP or CRM systems, data warehouses, and other business applications, including legacy systems?
Scalability. Cloud-based platforms offer flexible resources without large upfront infrastructure investments. Can the platform adapt to changing data volumes and usage patterns without introducing operational friction or unexpected costs?
Strategic alignment. Does the platform support the company's current business priorities, analytics strategies and governance objectives?
Regulatory readiness. Can the platform demonstrate compliance with evolving requirements such as the EU AI Act, GDPR, CCPA, and industry-specific standards?
Executives who have committed to major cloud vendors such as Microsoft, Amazon, Google or Oracle must also consider whether to follow a single-vendor approach for data quality and observability, or to choose best-of-breed tools from independent vendors. The right choice depends on organizational context and existing investments.
Quality assurance and data trust metrics
Enterprise data quality frameworks still rely on a set of well-established technical measures. These six dimensions of data quality provide a baseline for evaluating platform capabilities and monitoring operational consistency:
Accuracy. The degree to which data reflects real-world conditions
Validity. Conformance to defined formats and rules.
Completeness. Presence of required values.
Uniqueness. Absence of duplicate records.
Consistency. Alignment across systems and sources.
Timeliness. Currency of data relative to business needs.
While these dimensions are easy to define and measure, executive evaluation increasingly requires data trust. Data trust, however, is not established solely through metrics. A single high-profile failure can destroy years of credibility because people remember failures more than successes. If analysts routinely doubt their data, they will build workarounds, often using spreadsheets that undermine governance.
As a result, executives increasingly look for qualitative signals of trust alongside formal metrics. How do people feel about the data? Are they confident? Do they rely on the official reports, or do they maintain those shadow spreadsheets? Qualitative surveys aim to make visible what accounting systems miss by acknowledging that formal metrics can be misleading and that users' informal sentiment is critical.
Conclusion
Good data quality is a business advantage. Organizations with clean, reliable data make decisions with greater confidence. Tying measures, reports and dashboards to quality outcomes, such as improvements in decision-making or reductions in customer complaints traceable to errors in data, translates the abstract goals of governance into business language that boards understand.
Research from BARC reinforces this point. Leading organizations that balance governance and innovation invest in quality, literacy, and accountability as foundations to scale AI responsibly. Laggards remain tightly focused on compliance and operations, missing the bigger picture and the opportunity to turn data into business value.
Executive evaluation of data quality platforms reflects these priorities. Measurable returns, alignment with regulations and verifiable trust drive many decisions about data architecture. The platforms that win budget approval demonstrate how this investment protects the reliability of decision-making and the organization's reputation. Management boards increasingly hold executives accountable for proving that case.
Donald Farmer is a data strategist with 30+ years of experience, including as a product team leader at Microsoft and Qlik. He advises global clients on data, analytics, AI and innovation strategy, with expertise spanning from tech giants to startups. He lives in an experimental woodland home near Seattle.
