A Data Quality Dashboard Is Essential for CRM Data Management


  • Your Customer Relationship Management software is only as good as its data. If your data is dirty, your application is ineffective. Garbage in, garbage out.
  • Many companies experience a data quality realization paradox. Most CRM users recognize and live with bad data, but most executives overestimate the quality of their data. Dashboards display data quality truth to achieve consensus.
  • Relevant, reliable and useful information is a prerequisite to all downstream process and reporting built on data. A 360-degree customer view, customer insights, role-based dashboards, information reporting and artificial intelligence are useless if the data is not sound.
Johnny Grow Revenue Growth Consulting

You can't manage what you can't measure.

Once you have created a CRM data quality program, dashboards are essential tools to bring visibility to progress, detect variances and facilitate continuous data improvement.

A CRM data dashboard should display the most essential data quality metrics. Below is a dashboard example we routinely use in CRM implementations.

Customer Data Quality Dashboard

Data accuracy, completeness, timeliness and consistency are generally the prerequisites for CRM data management. Other helpful data dashboard dimensions include quality scores for the most utilized data, duplicate record frequency and sources, highest discrepancy rate data values and sources (with routing to data stewards), high value missing data and unused data (for potential archive).

Displaying CRM data policy compliance in a Leaderboard is also helpful in motivating users and providing educational opportunities to data stewards.

Below is another CRM data dashboard created in Salesforce.

Salesforce Data Quality Analysis

The above CRM data management dashboard displays a single measure of data quality but for several objects and transactions. This view begins to show where data quality needs the most attention. In this case, and in most cases, Leads, Opportunities and Activities tend to report the lowest quality scores.

Below is another dashboard created in Salesforce. It's a high-level dashboard focused on data quality but also advises missing data.

Salesforce Data Quality Dashboard

See the CRM data management dashboards to measure and improve data accuracy, completeness, timeliness and consistency.

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Each of these CRM data quality dashboards drill-down to uncover subsequent details such as individual data field variances, types of variances and the sources of the variances.

When creating data dashboards there are two essential questions to answer: What data metrics should be measured and what constitutes an error or deviation?

Data Quality Measures

There are at least 8 dimensions to manage data quality:

  1. Accuracy: Which means the data is without errors. Methods to verify data accuracy include using fuzzy logic match queries (finding duplicate accounts by comparing similar lead names, addresses, websites, telephones and/or contacts); running reports to measure data standardization accuracy (i.e., the Lead, Account or Contact name cannot be null or include data policy standardization rules such as punctuation, abbreviations, apostrophes, case sensitivity variances or illegal characters), running reports to measure deviations (i.e., email bounces) or comparison to third party sources (D&B, Reuters).
  2. Completeness: This measures the percentage of Lead, Account and Contact records with data values. It's the ratio of null fields to total fields. It can be semi-automated by measuring the number of missing required and high value fields. CRM software mass update tools can quickly remedy many of the missing data values.
  3. Value: This metric shows the relative importance of each data element as designated by the data owner. Focusing on high value fields first allows a phased approach to data quality improvement programs.
  4. Utilization: This measures the frequency of data reads and updates. We normally display the results in a list of the lowest and highest utilized data. Low value data may be moved to a data lake or archived in order to increase application ease of use.
  5. Consistency: This is the rate at which data is the same among shared systems. It's sometimes called a match rate. Data such as customer information is shared with ERP software and other systems. Queries which look for inaccurate data formatting and duplicate records will flag consistency errors.
  6. Age: Sometimes called timeliness, this is the time elapsed since the date last modified. This metric is useful in identifying data at rest and non-essential data that may be archived.
  7. Structure: This measures the accuracy of prescribed data value standardization, naming conventions and formatting styles. It's generally the single biggest source of dirty data and causes frequent errors in queries, reports and dashboards. A CRM data quality policy is needed to provide the rules for proper data entry and identify the calculated variations to be detected for this metric.
  8. Duplication: This measure shows the rate of redundant data. It should first focus on duplicate Lead, Account and Contact records as these errors are usually the second largest source of poor data. CRM software includes tools to identify, merge and purge most but not all duplicate records.

Two additional measures for consideration include:

  1. Auditability. The percentage of data sourcing and CRUD transactions traced to the users or processes that created them. This aids data discovery as well as managing multiple data versions.
  2. Metadata compliance: The percentage of (high value) data attributes with business definitions or semantics. This becomes more important when you manage system integrations, software customizations or data synchronizations such as with Master Data Management (MDM).

CRM Dirty Data Defined

Dashboards should bring dirty data to the forefront. But many companies are unclear on what constitutes dirty data. Here are the four most common examples.

  • Invalid data – which is incorrect data that may be improperly named, inconsistently formatted or duplicated. CRM software can configure valid data rules at data entry to minimize this problem.
  • Duplicate data – these are normally Lead, Account and Contact records that have been created more than once under slightly different spellings or formatting.
  • Incomplete data – this shows up as sparsely populated Lead, Account and Contact records but is also quite common on sale opportunity and marketing campaign records. It contributes to incomplete customer intelligence and downstream reporting. This can be remedied with a combination of required fields at data entry and automated data enrichment programs.
  • Obsolete data – this is data with no value or use and should be deprecated. The data may be valid but it has become outdated, superseded or irrelevant.