As a heavy user of CRM systems for many years including salesforce.com (which I rate highly), I believe that high quality data is the key to user adoption. First and foremost, CRM is all about the data! If high quality data is entered and data quality processes are in place to maintain the data, then the CRM implementation will be a success.

The first step towards improving data quality is a data quality audit/assessment which identifies data quality issues associated with the key data fields which drive your processes. Let us assume a data quality audit results in a score of 65% – this means that your CRM processes will be at best only 65% effective due to the quality of the data. So if the purpose of the CRM application is to increase revenues via cross-selling and attract and retain customers, then management needs to be very aware of the impact low quality data has on the effectiveness of the CRM processes. Data quality metrics are based on defining data quality dimensions including completeness, conformity, consistency, duplicates and accuracy, and applying these dimensions to the data fields. For example a customer or prospect record may include the name, address, email and telephone number fields. Each field can be audited using several dimensions. The results are aggregated, resulting in the overall score e.g. in this case 65% – which is for most organizations unacceptable.

Ideally a data steward should be responsible for the data within the CRM systems and should monitor the data quality trends. On a weekly basis, low quality data is highlighted and the data record owners should clean the data or populate the data fields depending on the issue.

Effective CRM systems must integrate with other systems – how else can you know the products your customer has purchased and generate a single view of your customer’s activities? As soon as you integrate data from multiple systems, new data quality issues are exposed as data flows between systems. This issue highlights some very interesting points. Firstly, for successful CRM, data quality needs to apply to customer data and product data. Secondly, data quality functionality needs to be tightly integrated with data integration processes so that high quality data is delivered to the CRM system.

So in summary, to improve adoption and effectiveness of your CRM systems, start with a data quality audit. Then decide which fields are crucial and how best to standardize data from several systems. Implement automated preventative cleansing processes at the point of data entry or at the point of integration with additional systems. Finally, monitor data quality trends within the system on an ongoing basis.