The Importance of Data Quality

Data quality describes your organization’s data health — the degree to which that data is accurate, complete, timely, consistently formatted, and compliant with internal and external regulations. Data quality also relates directly to a data set’s fitness to serve an intended purpose or use case, whether that be for sales, marketing, finance, corporate compliance, or another purpose.

For businesses, data is an asset. Data quality is a practice, and a continual one, as well as a necessity. Prioritizing data quality results in data that can be trusted, which allows its users to have confidence that decisions based on that data are the right decisions. 

Addressing the quality of your organization’s data is a crucial first step toward the success of many business initiatives. High-quality data can lend businesses a competitive edge by reducing risk and increasing efficiency and productivity, which leads to better-informed decisions. In addition, companies that commit to data quality are better able to use their knowledge of customers, prospects, and vendors to influence top- and bottom-line results.

A recent commissioned study conducted by Forrester Consulting on behalf of Dun & Bradstreet found that, for 52% of the global data management leaders we surveyed, ensuring high-quality data was a key priority for the coming year. In the same study, 41% of the respondents agreed with the statement: “We have missed opportunities due to poor data.”

Quality data delivers significant benefits across multiple business areas:

Finance, Procurement, and Compliance Use Cases

In finance, accurate and consistent data helps ensure reliable reporting, precise forecasting, and informed investment decisions, reducing the risk of costly errors or regulatory penalties. For corporate compliance, maintaining high data standards helps organizations meet legal and industry requirements, avoid fines, and demonstrate transparency to stakeholders. In procurement, clean and complete data enables better supplier evaluations and contract management, ultimately improving operational efficiency and controlling costs.

Sales and Marketing Use Cases

Data quality is inextricably linked with sales and marketing performance. Data-driven marketers play a strategic role in profiling and targeting audiences and handing promising leads to the right salespeople. In addition, a 360-degree view of a customer across divisions, geographies, and corporate family tree relationships allows sales to more effectively manage their accounts and realize their full potential to upsell, cross-sell, and coordinate appropriate service levels.

High-quality data also improves customer experiences and supports customer responsiveness by enabling businesses to anticipate needs, personalize interactions, and resolve issues more quickly and accurately. For service reps and front-line salespeople, not being customer responsive is not an option. Improved data quality helps them be better at their jobs by eliminating time-consuming, non-value-added activities caused by incorrect data.

AI Adoption Use Cases

For the critical priority of artificial intelligence (AI) adoption, data quality is the foundation for success. AI models rely on large volumes of accurate, well-structured data to learn and perform effectively. Therefore, high-quality data enhances the performance of AI tools, enabling them to deliver actionable insights, automate processes with confidence, and drive innovation across the organization. Investing in data quality not only strengthens current operations; it’s necessary for realizing the full potential of advanced technologies like AI.

Poor-quality data actively undermines AI initiatives. It can lead to biased predictions, unreliable outputs, and diminished trust in AI-driven decisions. Its repercussions can go so far as to weaken an organization’s competitive standing and create customer distrust. 

A key point that’s often overlooked as companies increasingly rely on AI-driven systems is that decision making is becoming more and more abstracted from the raw data. AI models process vast amounts of information behind the scenes, often in ways that are not easily interpretable by humans. This creates a “black box” effect where stakeholders see the outputs — predictions, recommendations, or automated actions — but have limited visibility into the underlying data, its quality, or how it influenced those results. If the data feeding these models is inaccurate, incomplete, or biased, the AI will amplify those flaws, leading to poor decisions that may go unnoticed until they cause significant harm.

Essentially, the more organizations delegate judgment to AI without robust data governance and monitoring, the greater the risk that bad data silently drives strategic, operational, or compliance failures.

Second only to its people, data may be an organization’s most valuable asset. But data will only be valuable and useful if it’s of high quality. Bad data gets in the way and can be a liability.

Current published data from Gartner states that poor data quality may cost organizations at least $12.9 million per year on average. These costs stem from wasted resources, operational inefficiencies, regulatory risks, and missed business opportunities. Poor-quality data is often the underlying cause of lost sales, fines for improper compliance, and loss of customer trust.

Bad data quality can have significant business consequences totaling hundreds of millions of dollars in losses. Recent examples are easy to find: the transportation company that lost revenue by using incorrect fare data; the airline losses due to thousands of flight cancellations caused by bad data errors; the bank that incurred regulatory penalties because its compliance systems were undermined by faulty data.

The costs associated with bad data only escalate as tech platforms and their associated data proliferate. Bad data usually means off-target analytics, which can in turn generate misguided business strategies. Too often, businesses want to make IT the culprit for failed strategies and costly disruptions, when the truth is that they should have been investing in the data before the tech stack.

A strategic investment in data quality pays off in multiple use cases across the enterprise. First, good-quality data can be used, processed, and analyzed more easily, offering insights to help an organization make better decisions. Second, good-quality data means organizations can extract more value from their data. Third, good-quality data is trusted data, and trusted data is used more frequently by the teams that are tasked with using it.

Data quality management strives to improve organizational efficiency and productivity while reducing the risks and costs associated with poor-quality data. Data quality management lets data management teams focus on more productive tasks rather than spending their time cleaning up data sets. They can spend their valuable time helping business users and data analysts take advantage of the organization’s data assets in practice. At the same time, they can proactively promote data quality best practices to minimize occurrence of data errors in the first place.  

Dun & Bradstreet measures data quality using eight dimensions: accuracy, completeness, consistency, conformity, uniqueness, timeliness, integrity, and coverage. Data that incorporates these dimensions is much more likely to be trustworthy and fit for use in analytics, operations, strategic planning, and general decision making.

While all eight of these dimensions are important to assess or measure data quality, your organization might need to emphasize some more than others to support specific use cases.

Accuracy

Data must be correct. You don’t want your enterprise teams wasting time dialing the wrong phone numbers, duplicating purchases, perpetuating contractual errors, and otherwise squandering bandwidth and resources on data that provides no value. Data accuracy is typically measured and confirmed against an authoritative, trusted, and verifiable source, such as D&B’s Data Cloud for business data.

Completeness

Completeness describes how well the data delivers all the required values to be effective for a particular use case. For example, if the data is to be used for sales outreach, is there a contact name, email address, and/or phone number? Are all elements of the address present? Keeping pace with all the data required for all your use cases is no easy feat.

Consistency

Data values in different systems or data sets should match. For example, phone numbers should match as well as be in the same consistent format whether you’re pulling them from your CRM data, an Excel spreadsheet, or your vendor master database.

Conformity

Also described as validity, conformity means that your data is collected according to defined rules and parameters and conforms to the organization’s standard data format. If the organization doesn’t have a standard format, one needs to be implemented.

Coverage

Coverage is the measurement of the size of your data sets against external reference populations. For example, how much of the addressable marketplace is represented within your customer and prospect lists?

Integrity

For data to tell a reliable story, the data elements must fit together correctly. Integrity is a measure of the proper relationships between those data elements. If the sum of asset fields in a balance sheet doesn’t equal the Total Assets field, then users won’t trust the financial data.

Timeliness

Data should be updated as needed ­— even in real time — to ensure it meets user requirements for accuracy. Ongoing business volatility and complexity means that data is constantly changing. Industry sources state that B2B or CRM data is decaying at rates ranging from 25% to 70% annually. 

Uniqueness

Is there just one record — a single source of truth — or are there duplicates? Duplicate records lead to confusion and wasted resources trying to identify the truth, especially if the records lack completeness, consistency, and accuracy.

The amount, speed, and types of data coming into an organization can overwhelm existing data management processes and controls. When data flows in rapidly from multiple sources — such as customer interactions, IoT devices, social media, and third-party systems — organizations often prioritize speed over accuracy. This can result in incomplete records, duplicate entries, inconsistent formats, and unverified information slipping into core systems. Over time, these issues compound, creating a data environment pervaded by inaccuracies and inconsistencies.

Incomplete and Missing Data

You have incomplete and missing data when customer attributes are unknown or left out of the customer record. For example, when a contact record is missing the title value, the segmentation critical for account-based marketing outreach is difficult to do. Even more basic, the lack of identifying attributes such as business name, country, and telephone number makes the simplest outreach time-consuming. Incomplete data makes it difficult to nurture leads and compromises revenue operations (RevOps) efforts.

Stale Data

Data is a lot like food — if data is left untouched for too long, it will go bad. Today’s organizational landscapes change constantly. Whether divestitures, mergers and acquisitions, or going out of business, you risk costly errors or wasting valuable time if you don’t keep up with these insights.

Incorrect Data

Two probable scenarios contribute to this type of bad data in your organization: stale data that doesn’t get refreshed, and inaccurate data entry. Data gathering can be challenging for the latter when no standards exist for data input qualification. Even with the best intentions, mistakes can happen.

Duplicate Data

Duplicate data is considered harmful when it serves no business purpose and reduces the effectiveness of the decision-making process. In addition, though often unintentional, duplicate data confuses users and provides misleading results when aggregated without knowledge of its presence.

Implement strong data governance practices, regularly cleanse and validate data, and ensure consistent standards across all systems. Investing in automation and monitoring tools helps maintain accuracy and prevent decay over time, while enrichment processes add missing details to make data more complete and actionable.

Institute Data Standards

Create an explicit definition of what level of quality is acceptable for each dimension. Standards define a baseline for the acceptability of the results of these actions and then enable teams to go beyond minimally acceptable data quality. Data quality standards should also drive key performance indicators to show how the data improves quarter over quarter and year over year.

Develop Regular Data Quality Measurement Processes

“Trust but verify”; conduct periodic testing to ensure the data lives up to the defined standards. Those processes should be automated to enable analysts to shift their efforts from monitoring to triage and remediation of issues.

Enrich and Refresh

Data fundamentally describes transactions, people, places, things, events, other data, and more. As those attributes change, data also shifts and changes — the reason we must refresh data and enrich it with additional insights. Acquiring new data is only one part of the solution. Keeping it relevant as needed is the other part. Strategies for enriching and refreshing data on demand or periodically become more pertinent to organizations that depend on data for decision making.

Establish an Archive Strategy

As the overall trend toward lower data storage costs continues, data will live longer than ever. However, this comes with the price of managing and accessing the data. Old and immaterial data can easily pollute an environment like your CRM or accounts receivable system. Provide context on material data and archive the rest in a manner that allows you to access or reinstate it when needed.

When people talk about data quality, they’re often really asking what checks are in place to make sure the data holds up in practice. These validations translate abstract quality dimensions into enforceable rules.

At a foundational level, schema and data type validation ensures data conforms to its expected structure. Required fields must be present, and values must be stored in the correct format, such as numeric identifiers remaining numeric or date fields following a standard date format. Records that violate these rules are flagged before they move downstream.

Range and boundary checks add real-world constraints to data. An age value should never be negative or unrealistically high, and a percentage should always fall between zero and one hundred. These validations help catch obvious errors early and prevent skewed metrics or faulty logic later on.

Format and pattern validation focuses on consistency. Email addresses must follow a recognizable pattern, phone numbers must contain the correct number of digits, and timestamps must adhere to an agreed standard. Even small formatting issues can cause integrations to fail or data to be misinterpreted.

Beyond individual fields, relational checks ensure datasets align with one another. Referential integrity validation confirms that relationships are intact, such as every order referencing a valid customer. Uniqueness checks similarly guard against duplicate records, preventing issues like double counting or redundant outreach.

Timeliness validation addresses whether data arrives when expected. For example, a daily reporting table should include records from the most recent business day. If data is stale or missing, alerts can surface the issue before decisions are made on incomplete information.

Finally, business rule validation encodes operational logic directly into quality checks. A closed support ticket must include a resolution date, or a canceled subscription should not produce future invoices. These rules ensure data reflects how the business actually operates, not just how systems store information.

Ultimately, data quality is the responsibility of everyone in the organization who works with data. 

Data quality is a business issue, not an IT issue. Companies that are leaders in data quality place the responsibility of data quality sponsorship at the C-level. The senior champion and a cross-functional group of line and IT experts create the most effective team to sell, frame, and drive a data quality initiative.

Customer-facing line functions guarantee that data and process decisions support business delivery. IT, and often Operations, play a consultative role in recommending options to help the implementation team meet its data quality goals. Broad representation ensures that improvements address the biggest ROI opportunities across the organization, including credit management, vendor management, sales, marketing, and business development.

Using a data quality framework will help you understand how your data should perform and identify opportunities for data transformation. 
Through this continuous cycle, we can develop new data policies, new data standards, and new data guidelines.

Data policies are high-level directives that define requirements for protecting corporate values, assets, and intellectual property. They serve as the foundation for related standards, processes, procedures, and guidelines.

Data standards translate these policies into specific practices and benchmarks that ensure compliance. Every standard should stem from a policy, forming the next step in the organization’s governance framework.

Data guidelines provide practical advice, tips, and best practices to support the implementation of policies and standards. While optional, they typically outline proven methods and parameters that help teams apply governance effectively.
This diagram illustrates the continuous loop of Dun & Bradstreet’s data quality framework, with key roles for people, processes, and tools in controlling, measuring, and monitoring data quality:

Data governance establishes the structure for agreed-upon definitions, standards, and accountability around data quality. Because perceptions of data quality often vary by role and use case, governance creates alignment and enforces consistency — most critically at the point of entry — so that data entering the system meets organizational requirements. This structured approach reduces ambiguity and risk while helping ensure that everyone operates from a common understanding.

Data hygiene is the ongoing process of maintaining data quality by keeping data accurate, consistent, and up to date. It’s preventive and proactive ­— like regularly validating contact details, removing duplicates, and monitoring for decay — so that data stays healthy over time.

Data cleansing is a corrective process; this is where quality issues in a data set are fixed, including removing errors, filling in missing values, and standardizing formats to conform to data governance requirements. During cleansing, data may be enriched to correct and complete records such as contact information, firmographic details, or technographic data points. Enrichment typically involves integrating first-party data with external data sources (also known as third-party data) to fill in gaps.

Particularly where AI priorities are concerned, all these practices are essential; governance provides the rules and oversight that prevent flawed data from entering systems, while hygiene and cleansing ensure that historical and incoming data meet those standards. Data governance, hygiene, and cleansing create the trustworthy data foundation that advanced analytics and AI tools require to perform effectively.

To sum up: Defining what data quality means for your organization is more than a technical exercise; it’s an urgent business imperative. Without clear benchmarks for accuracy, completeness, and consistency, organizations expose themselves to strategic risks that threaten revenue, compliance, and reputation. Poor-quality data doesn’t just sit idle; AI systems amplify its flaws, leading to misguided decisions that can ripple across the business. That’s why data quality must be owned at the highest levels, ideally with a chief data officer and data governance board or steering committee driving data standards and accountability. The cost of ignoring it today will only multiply tomorrow, so early action is essential to safeguard performance and ensure trustworthy AI outcomes.

Many companies benefit from partnering with external experts in master data management; these experts bring specialized tools, methodologies, and industry benchmarks to help accelerate improvements and ensure data remains fit for purpose, especially as demands from analytics and AI continue to grow.