{
  "id": 6775151,
  "title": "CRM Data Management: Best Practices for Cleaner Revenue Data",
  "url": "https://urgent.news/2026/09/11/crm-data-management-best-practices-for-cleaner-revenue-data",
  "topic": "finance",
  "section": "Finance & Markets",
  "published": "2026-09-11T08:13:07.000Z",
  "source": {
    "name": "TechRepublic",
    "slug": "techrepublic",
    "url": "https://www.techrepublic.com/article/crm-data-management/"
  },
  "original_language": "en",
  "account": "Maintaining clean and usable CRM data is essential for accurate revenue management. This involves implementing standards, enrichment, governance, validation, and continuous quality monitoring. CRM data encompasses customers, accounts, contacts, and sales pipelines, ensuring consistency and usability across a company. Inaccurate data can impact lead routing, sales outreach, segmentation, forecasting, and automation workflows. Companies like ZoomInfo help enhance CRM data quality by enriching records with crucial prospecting and segmentation information.\n\nTo improve CRM data quality, organizations must establish clear standards for validation, deduplication, ownership, governance, and ongoing maintenance. This guide outlines steps to audit data quality, rectify common issues, set governance standards, and maintain data accuracy over time. CRM data management includes policies, processes, and tools for managing customer relationship management system information.\n\nVarious terms describe different aspects of this process, including data cleansing, standardization, validation, enrichment, governance, and monitoring. While a database may appear clean, inconsistencies in field definitions, integrations overwriting data, or unclear ownership can still cause problems. CRM data influences decisions throughout the customer and revenue lifecycle, affecting sales team activities, forecasting, and marketing efforts. For instance, outdated contact information can lead reps to engage the wrong individuals, while missing firmographic data impacts lead scoring and account segmentation.\n\nKey data quality dimensions include accuracy, completeness, consistency, freshness, validity, and uniqueness. The acceptable standard varies depending on the field's usage. Data quality audits measure existing errors, identify their causes, and prioritize fixes based on business impact. Organizations should focus on fields critical to workflows, reports, decisions, or compliance requirements. Each important field should have a defined purpose, accepted values, format, source, owner, and update frequency.\n\nCommon data quality issues include missing critical fields, duplicate records, invalid or outdated information, unassigned records, email bounces, incomplete opportunity data, and integration errors. Causes often stem from manual entry, inconsistent imports, weak validation, conflicting field mappings, broken integrations, or unclear ownership. Instead of attempting to clean the entire database simultaneously, prioritize active opportunities, target accounts, leads, customers, and recently engaged contacts. Prior to cleaning records, clarify what information the organization requires and how it should be managed. Document data origins, ownership, change frequency, and update procedures. Avoid making fields mandatory without a clear operational purpose; instead, focus on critical fields that support workflows and decisions.",
  "summary": "Keep CRM data accurate, complete, and usable with better standards, enrichment, governance, validation, and ongoing quality monitoring. The post CRM Data Management: Best Practices for Cleaner Revenue Data appeared first on TechRepublic .",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}