Legacy data can sometimes be very messy. Spending the time to analyze legacy data allows companies to understand the results of a wide variety of users having logged customer interactions within a legacy CRM system over the course of many years.
A CRM requirements analysis should always include a review of legacy data sources. This analysis should validate what users are currently asking for in terms of key data fields for profiling companies and contacts. It should then be determined whether or not users have maintained data properly over time.
If there are data quality issues within legacy databases, such as inconsistent field values or the absence of values in certain fields, the requirements phase of a new CRM implementation is an ideal time to better understand the reasons for poor data quality and to take corrective action.
Here are some questions to ask:
- What, if any, type of field validation in an new CRM will enhance the quality and completeness of data?
- What are the “must have” and the “nice to have” fields?
- Do legacy fields with a high data population align with what the users are asking for in a new system?
- What are the distinct values in various fields and how do these align with users are asking for going forward?
- How does the legacy data align with the reporting requirements that have been defined for the new CRM system?
- Does all legacy information map well to the new data model or does certain data need to be transformed or normalized?
Data Rules and Validation in a New CRM System
The greater sophistication of current CRM systems provides a number of mechanisms for ensuring clean data moving forward.
For example, there can be business rules that drive required field entry. Certain fields can be conditionally required based on other field selections.
Contemporary applications can guide users through pick list selections via the implementation of hierarchical pick lists, in which the value selected in one field drives the list of possible values in another field.
A new CRM system is an opportunity to implement more appropriate data types. Legacy numeric data that was in text fields can be transformed into numeric fields in order to drive scoring or calculations.
Once the data elements for various objects (Contacts, Companies, Activities, Notes, Opportunities, Cases and custom objects) are defined in the new CRM system, and the fields that will be migrated over to the new CRM application are identified, this information can be aligned with the use cases for the new CRM. Then, the question becomes, does the number of use cases need to be expanded, or should the data migration be scaled back based on the set of use cases that are relevant to the current business model?
With proper analysis and planning, a new CRM system will provide much more consistent data, which will enable an organization to better profile its prospects and customers and to produce more accurate and consistent management reports. There will also be a more efficient and consistent user experience.