Like any asset, your CRM system depends on the quality of its inputs to deliver value.
Instead of streamlining operations, bad data misguides decisions with inaccurate assumptions and flawed insights.
The downstream costs cascade through wasted budgets, frustrated customers, dwindling adoption, loss of competitive ground and reputational damage.
But with proper oversight and governance, your CRM data can transform from a liability to a trusted asset.
This guide shares our best practices to protect data quality without getting overwhelmed. You’ll learn how to align personnel, processes and tools to ensure CRM data integrity, and give users and management confidence.
Read on to discover how to save your CRM investment from the pitfall of poor data quality.
Causes of Poor CRM Data Quality
We first need to recognise why poor data quality occurs. Frequent causes include:
Inadequate data-entry procedures: Without clear guidelines or rules for how data should be entered and managed, it’s more likely that errors will occur.
Lack of training: If people handling data aren’t adequately trained, they will be more prone to errors, leading to poor data quality.
Incomplete and inconsistent data: When information isn’t captured in CRM or records are incomplete, tracking and managing customer interactions is more challenging. This impacts service quality and compromises reporting when CRM isn’t a single source of truth.
Lack of data quality checks: Designated CRM admins should actively check data quality to identify and correct errors. If these routine checks aren’t in place, errors may go undetected, and data quality standards can quickly slip.
Outdated or obsolete data: Over time, customer data becomes outdated. If this data is not regularly cleaned, updated or removed, it can lead to incorrect insights and decision-making.
Complex data structures: Overtly complex data structures that don’t map to user workflows can often cause struggles with adoption and data discipline. In some instances, complex structures are unavoidable. However, in many cases, a more straightforward solution can often be found to support better data quality.
Lack of resources: Poor data management will also occur when organisations don’t have sufficient resources to invest in data management best practices.
Rubbish in, rubbish out: It may be a cliché, but a frequent cause of poor data in a new CRM is still caused by migrating unclean data. A new CRM tool such as Dynamics 365 offers tremendous benefits but it can’t magically transform suspect data.
Recommendations to Protect CRM Data Quality
Championing CRM data quality requires continuous, concerted effort across policies, system design, automation, analytics, and education.
Here are our key areas for CRM admins to achieve success:
Formalise Data Entry Standards
Document consistent data entry guidelines aligned to your workflows, providing end-user reference details.
Establishing centralised oversight for approving changes will help to enforce uniformity. For instance, if there is a requirement to add a new option to a dropdown list, it should be defined to manage these requests. As a result, it should avoid people to make impromptu design changes.
Optimise System Design and Inputs
Streamline forms with mandatory fields, implement validation checks and surface capabilities like duplicate finding on entry.
Prioritising user experience in data capture is crucial to ensure data quality and adoption. Identify improvements to improve form layouts to make it easier for everyone to populate records. This should avoid overwhelming people with confusing layouts and irrelevant fields.
Only Import Cleansed Data
If your source data is of questionable quality, avoid importing issues into a new CRM system. As we’ve highlighted above – rubbish in means rubbish out.
As data owners, your team will understand its nuances and contexts and will likely be best positioned to remediate quality gaps. While partners can provide oversight and tools to uphold governance, the heavy lifting for information integrity starts with internal data stewards.
Construct Role-Based Forms
Balance information visibility and entry burden for different teams interacting with the same records. Following the previous point, consider role-based forms that display only relevant details to guide better data discipline.
For instance, individual sales teams may want to see different information on customer account records, so enabling multiple form views will provide streamlined and personalised views for each.
Evaluate Productivity Tools
Identify plugins and integration that can auto-populate records from other data sources to minimise manual efforts.
Sales Copilot for Dynamics 365 is one recent example, enabling sellers to easily create new CRM contacts directly from an Outlook message. This can even extract contact information from an email signature to reduce manual data entry.
Another example could be connecting CRM with lookup resources such as Loqate to ensure address details are accurately captured.
Enforce Validation Rules
Configure platform validations on field formats, incomplete details, out-of-range inputs, and custom requirements. These safeguards will catch errors upon submission before they persist.
Activate Deduplication Safeguards
Configure CRM duplicate detection, fuzzy matching, and merge capabilities to consolidate records.
Ensure end-users are given guidance when potential duplicates are discovered and what to do when records should be merged.
Develop Data Quality Tracking Views
CRM admins should have access to reports and dashboard views that will help to detect anomalies in recent records, like incomplete details or potential duplicates, for rapid remediation.
A CRM partner, such as ServerSys, can assist in creating these recurring resources that help administrators keep on top of their data management.
Formalise Data Retention Cadences
Systematically review and archive obsolete CRM records in line with your data retention policies to preserve quality and optimise storage.
While some records must be retained for regulatory and compliance purposes, older data can potentially be removed from your live CRM system. Automated processes can be configured to identify matching records in line with defined retention rules, enabling CRM admins to safely delete or archive as appropriate.
CRM Training and Support
Pursue training and use expert resources to uplift your data management capabilities. Engaging with a partner will help to fill knowledge gaps and provide ongoing support to make data quality analysis and improvement a coordinated, continuous process.
Next Steps
ServerSys can help you address these issues by deploying a strategy to assess your governance gaps, prioritise fixes, and provide the solutions so you can rely on the data you depend on for success. Contact us to start the conversation.
