[GTM glossary](/glossary.md) Data quality Data & Enrichment # Data quality In short Data quality is the degree to which data is accurate, complete, consistent, current, and unique enough to be used for its purpose. High-quality data lets a team make decisions and take action without second-guessing it. In GTM, quality is judged by outcomes: emails that deliver, phone numbers that connect, accounts routed to the right rep, and reports leadership trusts. Quality is the broad goal, while hygiene and cleansing are the ongoing tasks that maintain it. ## The dimensions of data quality * **Accuracy:** Does the value match reality? Is this person still at this company? * **Completeness:** Are the fields you need filled in, such as industry, title, and email? * **Consistency:** Is the same thing written the same way everywhere, like one format for country names? * **Timeliness:** Is the record recent enough to trust? * **Uniqueness:** Does each person or company appear once? --- ## How to measure data quality Pick a few metrics tied to real work and track them monthly. Say a team has 10,000 contacts and finds that 2,000 lack a phone number and 800 have bounced emails. That gives a clear completeness rate for phones and a clear problem to fix. The numbers here are only an example. * Email bounce rate on recent sends. * Percent of records with each required field filled. * Count of suspected duplicates. * Share of records updated or verified in the last six months. --- ## How to improve data quality ### Fix it at the point of entry Use required fields, picklists, and validation on forms and CRM imports so bad data never gets in. ### Standardize and deduplicate Normalize formats and merge duplicates on a regular schedule so reports and routing work. ### Refresh on a schedule People change jobs and companies change size. Re-verify key records, prioritizing accounts in active pursuit. ### Assign an owner Data quality slips when everyone owns it. Give RevOps or a named person the job of rules, audits, and reporting. --- ## Common mistakes * Running a one-time cleanup and never repeating it. * Measuring only record counts, not whether the data is usable. * Buying more data to fix a problem caused by poor entry rules. --- ## Data quality FAQs **What is data quality in sales and marketing?** Data quality in sales and marketing is how reliable and usable your contact and account records are. High-quality data is accurate, complete, consistent, current, and free of duplicates, so emails deliver, routing works, and reports reflect what is really happening. **How do you measure data quality in a CRM?** Track a few practical metrics: email bounce rate, percent of records with key fields filled, count of duplicates, and share of records verified recently. Review them on a regular schedule and set targets so you can see whether quality is improving. **What are the main dimensions of data quality?** The commonly used dimensions are accuracy, completeness, consistency, timeliness, and uniqueness. Some frameworks add validity, meaning the value follows the expected format. Together they describe whether data is fit for the job you need it to do. **What is the difference between data quality and data hygiene?** Data quality is the state of your data, meaning how fit for use it is. Data hygiene is the set of recurring tasks, like removing duplicates and fixing formats, that you perform to keep quality high over time. Share term --- ## More related phrases * [Fill rate](/glossary/fill-rate.md) * [B2B data](/glossary/b2b-data.md) * [Data freshness](/glossary/data-freshness.md) ## Similar phrases * [Data accuracy](/glossary/data-accuracy.md) Data accuracy is how closely the information in your records matches reality, such as whether an email, job title, or company size is correct, and it determines whether outreach reaches real buyers. * [Data decay](/glossary/data-decay.md) Data decay is the gradual loss of accuracy in contact and company records as people change jobs, companies change details, and information goes out of date over time. * [Data hygiene](/glossary/data-hygiene.md) Data hygiene is the ongoing practice of keeping CRM and marketing data clean, accurate, consistent, and usable through regular checks, standards, and ownership. * [Data cleansing](/glossary/data-cleansing.md) Data cleansing is the process of finding and fixing incorrect, duplicate, incomplete, or badly formatted records in a database so that the data can be trusted for outreach, reporting, and routing. * [Data standardization](/glossary/data-standardization.md) Data standardization converts data into one agreed format, such as consistent country names, date styles, and job title spellings, so records can be compared, filtered, and combined reliably.