What is Data Hygiene?

Gartner estimates that poor data quality costs organizations an average of $12.9 million annually. In B2B sales specifically, dirty data manifests as bounced emails (damaging sender reputation), wrong phone numbers (wasting call hours), duplicate records (creating confused customer experiences), and inaccurate pipeline data (producing unreliable forecasts).

According to Salesforce, 91% of CRM data is incomplete and 70% deteriorates annually. The decay is relentless: contacts change jobs (the average tenure in B2B roles is 2.5 years), companies get acquired, phone numbers change, and email addresses expire. Without active hygiene processes, your database becomes progressively less useful with each passing month.

Data hygiene also has a direct impact on AI and automation effectiveness. Machine learning models trained on dirty data produce unreliable predictions. Lead scoring models with wrong titles or outdated company sizes misroute leads. AI personalization engines referencing stale information generate embarrassing outreach. The entire modern sales tech stack depends on the foundation of clean data.

Data hygiene encompasses several distinct but interconnected processes.

**Deduplication** identifies and merges records that represent the same entity. Duplicate contacts are created when the same person enters the system through multiple channels (web form, event, import, manual creation) with slight variations. Dedup systems use fuzzy matching across multiple fields (name + email + company) to identify probable duplicates and either auto-merge them or flag them for manual review.

**Standardization** normalizes data into consistent formats. This includes standardizing job titles (mapping "VP Sales," "Vice President of Sales," and "VP, Sales" to a single canonical form), normalizing company names (with or without "Inc."), formatting phone numbers (international dialing codes, extensions), and applying consistent industry classifications.

**Validation** confirms that data values are accurate and current. Email validation checks deliverability without sending messages. Phone validation confirms that numbers are active. Address validation standardizes against postal databases. Field-level validation enforces formats (revenue must be numeric, dates must be valid).

**Enrichment and gap-filling** appends missing data from external sources. If a contact record has an email but no phone, enrichment attempts to fill the phone field. If a company record has a name but no revenue range, enrichment appends it from business databases.

**Decay monitoring** sets up automated processes to detect and address data degradation. This includes bounce monitoring (automatically flagging contacts whose emails bounce), job change detection (flagging contacts who have left their company), and scheduled re-verification (quarterly checks on active prospect lists). The most effective organizations treat data hygiene as a continuous background process, not a periodic cleanup event.

Autobound supports data hygiene by continuously enriching prospect records with fresh signal data. When a contact changes roles, a company raises funding, or a technology stack shifts, these updates flow through the Signal Engine into the prospect profile. This real-time enrichment helps maintain the accuracy of the data that powers AI-personalized outreach, reducing the embarrassment of referencing outdated information in sales messages.