CRM data quality

CRM data quality means customer records are reliable enough for the work people need to do with them. Start with that work: answering an enquiry, handing over an account, progressing a deal or contacting the right person. A field can be filled in and still be wrong for the decision at hand.

Key Statistics on CRM Data Quality in Australia

Define a usable record

For each task, ask what a colleague must identify, trust and act on. An open opportunity might need the correct customer link, an accountable owner, the current customer position and a next action. A contact used to arrange a meeting needs a dependable contact route. Requirements depend on the task.

Use these questions to define a few checks for your most important records. A target of every field being complete can encourage guesses and collect information nobody needs.

Set standards and responsibilities

Turn the checks into standards that explain what accuracy, consistency and completeness mean for the customer work in scope. A governance framework can set out the policies and roles for managing quality, including who oversees the standards and follows up when they are not met.

Help staff apply those standards consistently through training on data entry and management practices. Making quality part of everyday work can reduce avoidable errors at the point information is recorded.

Find the cause before correcting the symptom

Look at how an error entered the CRM. A duplicate may come from an import that failed to recognise an existing record. A blank owner may result from an ambiguous name in a source file. Correct the affected records, then review the entry rule, mapping or handover that allowed the problem to recur.

Keep uncertain cases visible to a named reviewer. An email address or company domain can help find a possible match, but neither settles every identity question. Do not merge people because their records look similar or assign an account because its name resembles an existing one.

Match checks to risk and purpose

For entities covered by the Australian Privacy Principles, the OAIC says reasonable steps depend on the circumstances. Relevant factors include the sensitivity of the information, the entity’s size, resources and business model, the possible adverse consequences for an individual, and what is practicable in time and cost.

The level of checking should reflect those factors rather than apply an identical process to every record. The OAIC notes that more rigorous steps may be required for sensitive information or where poor quality could have more serious consequences.

Handle correction requests consistently

For APP entities, APP 13 applies when the entity is satisfied that personal information is incorrect for a purpose for which it is held, or when an individual requests a correction. This provides a clear trigger for treating a quality concern as an exception requiring assessment and action.

If an APP entity refuses a correction request, APP 13 requires written notice to the individual that gives the reasons and available complaint mechanisms. It also requires reasonable steps to associate a statement with information the entity refuses to correct, and prohibits charging an individual for requesting or making a correction or associating a statement.

Set a practical review routine

Choose a record population and the decision it supports, such as open customer work due for handover. Review a sample and known exceptions. Record the issue, affected record, owner and resolution. Check whether corrected records remain correct after the next relevant import or integration run.

Use measures with clear denominators. For example, report the share of open opportunities in the reviewed population with an active owner, rather than an unexplained CRM completeness score. Track unresolved exceptions as well as corrections. Keep the population and rule alongside each result so a narrower search is not mistaken for an improvement.

For organisations subject to the Australian Privacy Principles, APP 10 requires reasonable steps concerning the quality of personal information collected and, having regard to purpose, personal information used or disclosed. APP 13 also sets out correction duties in specified circumstances. Review personal information for the task it serves; adding detail is not automatically an improvement.

Review quality at relevant points

For APP entities, quality checks matter at two points in the information-handling cycle: when personal information is collected, and when it is used or disclosed. Regular reviews at other times may also help ensure information is accurate, up-to-date, complete and relevant when it is used or disclosed.

Use review findings to improve the standards and processes staff rely on, not only to correct individual records. Data quality audits and ongoing monitoring can help identify recurring issues and assess whether the agreed standards are being followed.

Make updates easier

Where customers can help keep their information accurate, make it easy for them to provide updates. This can complement internal checks, while leaving the organisation responsible for taking reasonable steps to ensure the quality of personal information it collects, uses or discloses.