Data quality has always been challenging. Messy, incomplete, or inconsistent data has long slowed down strategies and weakened decision-making. But today, the stakes are even higher.
Business research found that half of those surveyed about data quality reported at least 25 percent of their revenue was subjected to data quality issues.
As businesses race to adopt AI-supported tools for automation, analytics, and personalization, the cracks in their data foundations are being exposed like never before. AI doesn’t fix bad data; it amplifies it.
Without trusted, consistent, and complete information, even the smartest AI models will deliver inaccurate insights, misfire customer experiences, and create costly inefficiencies. In this new era, solving data quality is urgent.
“There are two things that will make your team look like amateurs: someone pinging you on Slack, telling you your data is bad/wrong/weird, or—worse—you and your team not even knowing about it,” Tim Webster, a lead data engineer, said. “That last part keeps me up at night.”
Maintaining high data quality doesn’t happen accidentally; it requires focus, structure, and ongoing attention across key areas. To build truly trusted data, organizations need to address seven critical factors: accuracy, completeness, consistency, conformity, uniqueness, timeliness, and integrity.
These elements create a strong foundation for smarter strategies, better customer experiences, and scalable growth. Understanding and strengthening each one can transform your data from a liability into a true competitive advantage.
7 Core Features of Trusted Data Quality
Building trust in your data starts with mastering the fundamentals. These seven core features of trusted data quality—accuracy, completeness, consistency, conformity, uniqueness, timeliness, and integrity—form the backbone of every reliable system, strategy, and decision. When each is strong, your data becomes a true asset that powers growth with confidence.
1. Accuracy: Why “Mostly Right” Is Still Totally Wrong
For authentic data quality, “almost right” is still wrong.
A recent business survey found that 65 percent of leaders admitted that nobody in their organization understands all of the data the company collects and how to access it. Moreover, 58 percent say strategic business decisions are routinely based on bad or inconsistent data the majority of the time–if not all of the time. Inaccurate data erodes trust, causes missed opportunities, and inflates costs without warning.
2. Completeness: You Can’t Build a Campaign Foundation Without Complete Data
Missing data leaves gaps in your go-to-market strategy, impacts personalization for better CX, and interferes with data-driven insight for capacity and territory planning strategies.
According to a recently published Office of the CFO 2024 report, almost 90 percent of CFOs surveyed said they are “making decisions based on inaccurate or incomplete data on a monthly basis.”
There is no substitute for complete, accurate data. Ensuring that all required fields are consistently captured gives you a full, confident view of your customers and prospects, helping your actions land smarter and stronger.
3. Consistency: Why Cross-System Chaos Is Killing Your Efficiency
When customer data looks different in every system, chaos is guaranteed. Inconsistent data breaks automation, skews analytics, and confuses teams. For example, are the details identical if a customer is listed in Salesforce and HubSpot?
Lack of consistency creates reconciliation nightmares and breaks automation flows. However, the level of consistency and the measurement of the complete picture are up to you.
“Organizations must define what constitutes complete data for their specific needs and establish processes to capture all required information,” Dharmendra Kapoor, an author and chartered accountant, said.
4. Timeliness: Data Has an Expiration Date
In fast-moving industries, old data about customers, leads, or markets can be just as damaging as wrong data.
A study published by London-based NTT DATA found that 80% of organizations blame inadequate or outdated technology for interfering with organizational progress and innovation efforts. Moreover, the study found that 94% of C-suite executives believe “legacy infrastructure is greatly hindering their business agility.”
Stale information leads to missed opportunities, wasted effort, and decisions that no longer match reality.
5. Validity: The Secret Dangers Lurking in Your Database
Does the data follow the correct format, rules, and standards? For example, phone numbers must have valid area codes; zip codes must match real locations.
Hidden in your database are silent killers: invalid records and duplicate entries. These small-seeming problems can destroy customer trust, wreck integrations, and inflate costs.
6. Uniqueness: Remove Duplicate Records
Did you know that 80 percent of new records coming from integrations are duplicates? This is according to RevGenius. Across all sources, more than 45% of records are duplicates. “You and your team members are reaching out to those leads to find out they’re already in conversation with a sales colleague,” Gijs Hovens wrote. “You’ve wasted precious sales time and made an unprofessional impression on your lead.”
More data isn’t automatically better; it’s often just more noise. Collecting irrelevant information clutters your systems, slows down insights, and drains your resources.
7. Relevance: Why More Data Isn’t Always Better
It’s easy to assume that gathering every possible data point will make your business smarter — but without relevance, all that extra information becomes noise. Irrelevant data clutters systems, slows down processing, and makes it harder for teams to focus on what actually drives results. Even perfectly accurate data can be a liability if it doesn’t directly serve your goals, customers, or go-to-market strategy.
Relevance is what turns raw information into real value. High-quality data should be useful, actionable, and aligned to the specific purpose it was collected for, not just sitting in a database because it might be needed someday.
By focusing on relevance, organizations can simplify their data environments, sharpen their insights, and empower their teams to move faster with greater confidence. Quality isn’t about having more—it’s about having the “right” data at the right time.
Every customer interaction, strategic decision, and automated process depends on having accurate, complete, consistent, timely, valid, unique, and relevant data. When you invest in these seven core features, you don’t just clean up your databases — you empower your teams, strengthen your go-to-market strategy, and position your organization for faster, smarter, and more sustainable growth. The future belongs to businesses that can trust their data — and the time to build that foundation is now.
