Your sales team closes a major deal, but the customer record shows three different company names across your CRM, billing system, and support platform. When you try to analyze customer lifetime value, you can't even agree on basic facts like when they became a customer or how much they've spent. These are data quality problems, and they’re probably costing you more than just the hours spent reconciling conflicting reports.
Poor data quality actively sabotages your decision-making when you need it most: the numbers you present get questioned, the insights you surface get dismissed, and your team wastes time debating which version of the truth to believe.
The good news is that data quality problems often follow predictable patterns, and fixing them usually doesn't require a complete overhaul of your systems. This guide breaks down the meaning of data quality, how to measure it, and the practical steps you can take to build trust in your numbers.
What is data quality?
Data quality refers to how well your data serves its intended function, measured by accuracy, completeness, consistency, timeliness, uniqueness, and validity. High-quality data must be "fit for purpose," as the Data Management Association (DAMA) defines in their Body of Knowledge, meaning it reliably supports decision-making, powers AI models, and generates reports that stakeholders can trust without question.
When your data falls short, the ripple effects show up everywhere. If your marketing team can't trust conversion rates, you risk overspending on campaigns that aren’t winning—or leaving opportunities on the table. When your AI models train on flawed data, they produce unreliable predictions, and flawed data is much more common than you might expect.
As data quality expert Olga Maydanchik discovered in her work, "45% of the data I cleaned had errors," and for a mid-size company, proper data cleanup can save "approximately a quarter of a billion dollars," she explained on an episode of The Data Chief.
"The stakes have never been higher because decisions happen faster than ever, and bad data compounds quickly when it feeds into automated systems. That’s why it helps to ground your team in a clear, shared definition of what “good” means for your business before you start fixing anything."
How to measure the six dimensions of data quality
Understanding what good data looks like starts with breaking it down into measurable components. These six dimensions give you a framework for evaluating and improving your data systematically using actionable data quality metrics.
1. Accuracy
Your data correctly reflects real-world facts. Your customer's email address actually reaches them, and their purchase history matches what they bought.
How to test it: Send test emails to a sample of customer addresses to verify deliverability, or cross-reference transaction records against source systems like payment processors to confirm purchase amounts match.
2. Completeness
Your data contains all the required fields and values needed for meaningful analysis. Every customer record has the contact information you need to reach them.
How to test it: Calculate your completeness rate by dividing non-null records by total records and multiplying by 100. If you have 870 customer records with email addresses out of 1,000 total records, your completeness rate is 87%.
3. Consistency
The same information appears identically across all your systems. A customer's address looks the same in your CRM, billing system, and shipping database.
How to test it: Compare matching records across your systems and calculate what percentage align. Pull the same customer record from different platforms and check if key fields like company name, address, and contact details match exactly.
4. Timeliness
Your data arrives when you need it for decisions. Sales teams get lead information before making calls, not hours later when the prospect has moved on.
How to test it: Track the time lag between when an event occurs and when it appears in your system. Set up alerts that notify you when data freshness falls behind your service level agreements.
5. Uniqueness
No duplicate records muddy your analysis. Each customer appears once in your database, preventing double-counting in your metrics.
How to test it: Calculate your uniqueness rate by dividing unique records by total records and multiplying by 100. Run queries looking for duplicate customer IDs, email addresses, or other identifiers that should be one-per-record.
6. Validity
Your data follows the correct format and business rules. Email addresses contain "@" symbols, dates follow proper formats, and phone numbers have the right number of digits.
How to test it: Calculate your validity rate by dividing valid records by total records and multiplying by 100. Set up format validation rules that flag entries failing to match expected patterns, like emails without "@" symbols or dates in the wrong format.
Track your progress with data quality scorecards
Roll these metrics into data quality scorecards that track your most important datasets over time. This gives you a baseline and helps you set specific targets for improvement.
Start by identifying your critical datasets—customer records, transaction data, product catalogs—and measure them weekly or monthly, depending on how frequently they change. Be sure to review your scorecards in regular team meetings to spot trends, celebrate wins when metrics improve, and prioritize fixes for datasets falling below your targets.
This consistent visibility turns abstract quality goals into concrete progress you can track.
|
Metric |
Target |
Current |
Status |
|
Customer Email Completeness |
95% |
87% |
Needs Work |
|
Product ID Uniqueness |
100% |
99.8% |
Good |
|
Order Date Validity |
98% |
96% |
Fair |
Ready to see your data quality metrics in action?
Start your free trial to monitor your most important datasets with automated scorecards.
Quality checks on data that catch problems early
Quality checks on data work like spell-check for your datasets. They catch errors before they spread through your analysis and influence decisions you didn’t mean to make.
Start with these foundational checks:
Null value checks: Flag records missing information in required fields like customer IDs or order amounts
Uniqueness checks: Spot duplicate entries that could skew your counts and calculations
Referential integrity checks: Make sure foreign keys match primary keys so you don't end up with orphaned records
Format validation: Verify dates, emails, and phone numbers follow expected patterns
Business rule validation: Apply logic that reflects how your business actually works, for example, ship dates should always come after order dates.
Modern platforms automate these checks instead of forcing you to run them manually. For example, ThoughtSpot's AI-augmented dashboards automatically surface anomalies and outliers in your KPIs, alerting you to potential data quality issues without requiring you to build complex monitoring systems.
As a rule of thumb, run these tests as close to the data source as you can. Catching a formatting error in your CRM is much easier than fixing it after it's spread through five downstream systems. By validating data at entry points, you stop errors before they multiply and save yourself and your team from costly cleanup work later.
What does good data look like in practice?
Good data doesn’t mean perfect data; it's about data reliability and whether it supports the decisions you need to make. Here's what that means in practical terms:
Clear ownership: Someone specific is responsible for maintaining and improving each dataset
Proper documentation: You know where the data comes from, why business context matters, and how it's calculated
Easy access: Your colleagues who need the data can find it without jumping through hoops or waiting for IT tickets
Appropriate security: Access follows clear rules that protect sensitive information while allowing the right people to use it
Directional accuracy: Even if not 100% perfect, it's consistent enough to spot trends and make confident decisions
As marketing analytics expert Michelle Jacobs shared on an episode of The Data Chief,
"The reality is no data is going to be a hundred percent perfect. But as long as you're consistent about how you're gathering it, what you're doing with it, then you can use that to make some accurate assumptions about the data and decisions."
Common data quality issues and how to fix them
Real-world data quality problems often follow predictable patterns. Here are the most frequent culprits and practical fixes:
1. Bot traffic distorting web metrics
Automated crawlers and spam bots inflate your visitor counts and skew conversion rates, making it impossible to understand your real audience behavior. You might think your latest campaign drove thousands of new visitors, only to discover most were bots.
How to fix it: Implement bot detection filters at your data collection layer and validate traffic sources early in your pipeline. Set up rules that flag suspicious patterns like identical user agents, impossible click speeds, or traffic from known bot IP ranges.
2. Duplicate records from API integrations
When connected systems don’t sync cleanly, you end up with multiple records for the same customer or transaction. Sales thinks they hit quota, but finance can’t reconcile revenue because the same deal shows up three times in three different systems.
How to fix it: Enforce unique identifiers and standardize data formats at the point of entry. Create matching rules that catch duplicates based on multiple fields—not just email addresses, but combinations of name, company, and phone number.
3. Stale data breaking live decisions
Data that's hours or days old makes time-sensitive decisions impossible. Your support team escalates a customer issue based on yesterday's data, unaware that the problem was already resolved this morning.
How to fix it: Set up freshness monitoring with automated alerts when data falls behind your service level agreements. Define clear expectations for how current each dataset needs to be, real-time for operational dashboards, hourly for sales metrics, daily for financial reports, and build monitoring that notifies you when pipelines fall behind schedule.
4. Inconsistent naming conventions across teams
Teams often describe the same thing in completely different ways. Marketing calls them “leads,” sales calls them “prospects,” and your CRM lists them as “opportunities.” When you try to map the full customer journey, nothing lines up.
How to fix it: Establish a common business glossary that defines standard terms everyone must use. Implement a semantic layer to create a single source of truth for metrics and business logic—ensuring both humans and AI systems interpret data consistently. Document terms centrally and build validation rules that reject non-standard values at data entry, forcing consistency from the start.
5. Manual data entry errors
When humans type information into forms and spreadsheets, mistakes happen: transposed digits, misspelled names, wrong categories selected from dropdowns. A single typo in a customer's email address means they never receive your invoices, and a misplaced decimal point in pricing data could cost you thousands.
How to fix it: Implement input validation that catches obvious mistakes in real-time, like email addresses without "@" symbols or phone numbers with too few digits. Add dropdown menus and auto-complete features wherever possible to eliminate free-text fields that invite inconsistency.
6. Schema changes breaking downstream processes
A field name or data type changes in a source system without warning, and suddenly, dashboards across your organization break. Your exec team shows up to the weekly review only to see blank charts and error messages.
How to fix it: Implement schema monitoring that detects changes automatically and alerts your data team before they break production systems. Create contracts between data producers and consumers that require advance notice and testing before any structural changes go live.
Building a practical data quality improvement framework
Improving data quality requires more than just running checks; it sits within broader data management best practices that address people, processes, and technology together. Here’s where to start as you build a framework for continuously leveling up your data quality:
1. Establish clear data ownership
Assign data stewards to your most important datasets. These domain experts, typically from the business side, become responsible for defining quality standards, clarifying context, and keeping the data accurate as things change.. Data stewards act as the bridge between technical teams and business users, translating requirements into actionable quality rules while ensuring the data remains fit for purpose as business needs evolve.
2. Implement lightweight governance
Balance governance with agility by focusing on what truly matters. As CDO Jim Tyo explains on an episode of The Data Chief, effective governance means "understanding the risk profile of your data" and applying controls proportionally. Rather than implementing heavy-handed restrictions across all datasets, identify your high-risk data and govern it appropriately while keeping lighter controls on lower-risk information. This service-oriented model makes it easier for people to get what they need without slowing them down, while still protecting the business where it counts.
3. Create a single source of truth
When your teams define "revenue" or "active users" differently, you get inconsistent reports and endless debates. A centralized semantic layer provides unified business logic that everyone can trust. ThoughtSpot Analyst Studio includes this semantic layer functionality, allowing your data team to define metrics once so that everyone across your organization uses the same calculations. This eliminates the confusion that comes from having different departments work with conflicting definitions and calculations.
4. Automate monitoring and alerts
You can't manually watch every data pipeline 24/7, and you probably don’t want to, either. Set up automated systems that notify you when schema changes occur, data freshness falls behind schedule, or anomalies appear in your key metrics. Modern data observability tools catch issues like sudden drops in record counts, unexpected null values, or failed transformations before they cascade into broken dashboards. Configure alerts that match your team's response capacity: critical issues like missing customer data should trigger immediate notifications, while minor quality dips can roll up into daily digest emails.
5. Connect your teams to live, governed data
Static dashboards and stale data extracts create a false choice between access and control, but the better approach gives your teams direct access to live, governed datasets. Frontify experienced this firsthand: month-long report delays killed trust and stalled decisions. After switching to ThoughtSpot as their single source of truth, insights that took a month now arrive in 30 minutes, and 40% of employees actively explore data while building their data literacy.
Why ThoughtSpot is built for data quality
Data quality isn't a project you complete so much as an ongoing capability that compounds over time, and that’s especially true in the age of augmented analytics. Poor data quality directly inflates AI hallucinations and undermines retrieval-augmented generation (RAG) systems by feeding them inaccurate context. When your AI searches flawed data to answer questions, it confidently returns wrong answers based on bad inputs.
ThoughtSpot eliminates the false choice between data quality and user access by connecting everyone to live, governed data through an Agentic Semantic Layer. When someone searches for "revenue" or "customer churn," they get the exact calculation your data team defined—not some ad-hoc formula that might be wrong.
The platform connects directly to your cloud data warehouse, querying live data instead of relying on potentially stale extracts, while Spotter helps users ask better questions within the guardrails your team has established.
To keep AI reliable, prioritize validity and freshness checks that catch errors before they reach your models, and build feedback loops where users flag incorrect responses. Interactive Liveboards give you live monitoring of your key quality metrics with the ability to drill down when something looks off. Because everything connects to live data, you catch quality issues faster and fix them before they impact important decisions, turning data quality from a constant struggle into a competitive advantage.
📺 See what it takes to make AI accurate and trustworthy—get Snowflake’s playbook in this webinar.
Put your data quality strategy into action
Data quality isn't a project you complete so much as an ongoing capability that compounds over time. When your teams trust the numbers, they stop debating whether the data is right and start focusing on what it means for your business.
A modern analytics platform helps you build this trust by connecting everyone to a single, governed source of truth. With live data connections, automated quality monitoring, and AI-powered insights, ThoughtSpot lets your team maintain high standards without slowing down decision-making.
Ready to turn your data into your most reliable business asset? Start your free trial to see how ThoughtSpot helps you maintain data quality at scale.
Frequently asked questions
What are the most important quality checks on data for new implementations?
Start with null value checks, uniqueness validation, and format verification. These catch the majority of common data quality issues and provide immediate value without requiring complex setup or domain expertise.
How does poor data quality specifically impact AI and machine learning models?
Poor data quality directly causes AI hallucinations, biased predictions, and unreliable model outputs. AI models trained on inaccurate or incomplete data will amplify these flaws, making them untrustworthy for business decisions and potentially causing significant financial or reputational damage.
Can you achieve perfect data quality in enterprise environments?
No, and you shouldn't try. As Vertafore's Chad Hawkinson explains on an episode of The Data Chief, "If you wait until data is perfect, you will never actually engage on a data and analytics project." Focus on making your data fit for its intended purpose and directionally accurate enough to support confident decision-making.
