CRM , Data Quality

CRM Data Quality Dimensions

Author: Carlos Martínez
September 23, 2022

Your CRM data often includes different types of errors that can be classified into groups called Dimensions. Dimensions of data quality simplifies the measurement of the quality levels within your data. These dimensions are categorized according to the context. Some of them require continuous management review (i.e. data governance) and others are perfectly suited for defining rules used for continuous data quality monitoring.


Let's review some of the main dimensions to consider in your CRM data management plan.


Validity refers to how well your data conforms your defined business rules.

There are many different types of validation that can be built into your CRM fields (properties):


  • Data-Type. Values in a field must be of a particular data type (string, numeric, etc).
  • Range. Values fall within a certain range.
  • Mandatory. A field can't be empty.
  • Regular Expression. Data in these fields must follow a certain pattern (Zip codes).
  • Cross-field. Conditions to be met across multiple fields. As an example, a contact's lifecycle stage can't be "Lead" if the contact is associated to a won deal.


Just because your CRM data is valid does not necessarily mean that it is accurate. In example, a Zip Code with one number off due to a typo, seems to be a legitimate code until your account assignment workflow runs.


We may say data accuracy refers to how well your data represents the “real-life” objects they are intended to model and determines how your teams can (or can not) use the data.


Data accuracy can be measured by how the values agree with a reference by implementing an automatic or manual verification process. In any CRM, data verification plays a key role as it is critical for speaking directly to your customers’ biggest concerns and engaging them in a proper way, improving their experience throughout the customer lifecycle.


Consistency & Standardization

Consistency does not necessarily imply correctness and be careful not to confuse it with accuracy. Consistency may be defined within different contexts, e.g. across records from different platforms CRM <> ERP, or between records in your CRM database, such as job titles (CRO vs Chief Revenue Officer), which refers to data values in one record being consistent with the same values in another record.

Ensuring consistency is important for making decisions based on correct information as well as to create targeted campaigns. For instance, if you were trying to send a campaign targeting customers that hold “CRO” position, that job title should be expressed in your CRM using only that term in order to avoid leaving customers out of the campaign.

Lead scoring and segmentation are also effected by inconsistencies. Reporting and forecasts may be inaccurate. Workflows have to be fixed, slowing processes down.



Completeness indicates that certain attributes must be assigned values and it can be mandatory, optional or inapplicable attributes.


Missing data is missing context and it may be critical for your performance. It impacts your ability to report, and may mean that you have gaps in your automation workflows.

About Author
Carlos Martínez

Head of Solutions and Growth


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