Your CRM data is 23.9% complete.

A Salesforce data quality audit measures the completeness, accuracy, and uniqueness of data in your Salesforce org. It identifies empty fields that should be populated, duplicate records, fields marked required but not enforced, and the downstream reports or processes that break when data is missing. Clientell's audit grades every object A through F and runs in 10 minutes via read-only OAuth.

Above 70% is the threshold most reports and agents need to function reliably. Above 85% is healthy. Below 50% means the field is functionally absent from operational use. The industry-average completeness across the 1,000+ orgs we scan is 23.9%.

Above 2% duplicate rate on a critical object (Account, Lead, Contact) is too many. Above 5% indicates a structural data-entry problem. Median 4-year-old mid-market org has approximately 3.5% duplicate Accounts and 4.8% duplicate Contacts, both above the healthy threshold.

Fuzzy matching across email (normalized), phone (digits only), name (Levenshtein distance), and address (USPS-normalized). For each candidate pair, the audit applies confidence scoring and surfaces the pair with a recommended merge action. The matching is conservative: we surface possible duplicates for human review, not auto-merge.

Lead routing flows depend on Rating to assign leads to the right team. Lead scoring models depend on Rating as a feature. Agentforce lead-qualification agents depend on Rating to decide which leads to engage. When Rating is blank on 55% of records (the median), all three systems silently degrade. We see this as the single most common 'why are our agents acting weird' root cause.

Yes. Every custom field on every object is included in the audit, scored on completeness, and rated for utilization (is it on a page layout, referenced in a report, used in a Validation Rule, or queried by Apex). Dead custom fields (zero usage) are flagged for safe removal.

Cloudingo and DemandTools are excellent dedupe-and-cleanup tools that you run after the data problem exists. Our audit identifies the structural problem (missing Validation Rules, page layout requirements that bypass API, low-utility custom fields) so you can prevent the data problem from recurring after cleanup. Pair the two: run our audit to map the problem, use Cloudingo or DemandTools to execute the cleanup at scale, then apply the Validation Rules to prevent recurrence.

Yes, partially. The audit detects: format violations (email without @ sign, phone without digits), out-of-range values (Account.NumberOfEmployees set to 0 or negative), stale data (LastActivityDate older than threshold), and value drift (custom picklist values like 'Closed Won' vs 'ClosedWon'). It does not validate semantic accuracy ('did this prospect actually become a customer?'), which requires domain knowledge no tool has.

Yes. Agentforce agents and other AI assistants are trained to produce an answer. When the field they need is empty, they often invent a plausible value rather than ask. This is the most common cause of agent reliability issues we see. Below 70% completeness on agent-touched fields triggers the hallucination pattern consistently.

Data Quality is one of the four dimensions in the Agentforce Readiness Audit (weighted 30%, the largest). The Data Quality Audit drills into the dimension specifically with per-object grades and field-level heatmaps. The Agentforce Readiness Audit packages Data Quality together with Process Clarity, Permission Hygiene, and Automation Overlap into one weighted score. Use both: Readiness for the launch decision, Data Quality for the cleanup execution.