Should CRM Users Be Measured on Data Quality KPIs?
Most Organisations agree that high-quality data is essential for getting value from Dynamics 365. Accurate customer information supports better reporting, improved customer experiences, more reliable forecasting, and increasingly more effective AI-driven insights. Yet many Organisations continue to struggle with incomplete records, duplicate data, missing activities, and inconsistent data entry practices. This raises an interesting question: Should CRM users be measured on data quality KPIs? Consider a situation many Organisations have experienced. A sales team is expected to maintain customer records, update opportunities, and log key customer interactions in Dynamics 365. However, users are primarily measured on revenue, pipeline growth, and sales performance. As a result, CRM updates are often treated as a secondary task. During a quarterly sales review, leadership discovers that several opportunities forecasted as active were closed weeks earlier, while others had not been updated since the previous reporting cycle. Customer records are missing key information, activities have not been logged consistently, and reporting accuracy begins to suffer. The issue is often viewed as a reporting problem, but in reality, it starts with the quality and consistency of the data being maintained in Dynamics 365. To address these challenges, some Organisations introduce data quality metrics such as: Record completeness Duplicate record reduction Activity logging compliance Opportunity update accuracy Customer data validation rates Supporters argue that what gets measured gets managed, and that data quality should be considered part of everyone's responsibility. Others believe that introducing data quality KPIs may create an additional administrative burden, reduce user adoption, and shift focus away from core business objectives. There is also the question of whether users should carry the full responsibility. Modern Dynamics 365 environments include validation rules, duplicate detection, business process flows, Power Automate workflows, and governance frameworks that can help improve data quality. Some Organisations, therefore, argue that technology and governance should do more of the heavy lifting rather than relying solely on user behaviour. From your experience: Should CRM users be measured on data quality KPIs? Have data quality metrics improved CRM adoption or data accuracy in your Organisation? What KPIs have been most effective? Is data quality primarily a user responsibility, or should technology and governance frameworks carry most of the burden? Have you found a balance that improves data quality without creating additional friction for users? I'm interested in hearing how different Organisations balance user accountability, adoption, and data quality within Dynamics 365 environments.Who Should Be Accountable for Data Quality in Dynamics 365: IT or the Business?
Data quality remains one of the most common challenges in Dynamics 365 environments, regardless of industry or organisation size. When customer records are incomplete, duplicate data exists, or reporting becomes unreliable, the conversation often turns to ownership and accountability. Consider a simple example: A sales team creates customer records in Dynamics 365, while customer service updates contact details and finance systems synchronize billing information through integrations. Over time, duplicate records appear, customer information becomes inconsistent, and management reports start showing conflicting results. When this happens, who is accountable? Are the business users entering the data? Is the IT team managing the platform? The integration owners? Or should there be dedicated data stewards responsible for maintaining data quality standards? Some argue that data quality is primarily a business responsibility because users create and maintain most of the information stored in Dynamics 365. Others believe IT teams should take greater ownership through governance frameworks, validation rules, integrations, monitoring, and automated controls. In practice, many organisations struggle to find the right balance. When data issues arise, responsibility can become unclear, making it difficult to drive long-term improvements. From your experience: Who should ultimately be accountable for data quality in Dynamics 365? Should ownership sit with business teams, IT, dedicated data stewards, or a shared governance model? What approaches have worked well in your organisation? Have you seen a particular governance model deliver better results? I'm interested in hearing different perspectives and learning how others are addressing this challenge.Is Power Automate Becoming the New Technical Debt in Dynamics 365 Projects?
Power Automate has transformed how organisations build automation within Dynamics 365 and the Power Platform. Teams can automate processes quickly, reduce manual effort, and deliver business value without extensive custom development. At the same time, I have noticed an interesting challenge in some organizations as Power Platform adoption matures. Over time, hundreds of flows can be created by different teams, often with varying levels of governance, documentation, and ownership. Business logic may become distributed across multiple automations, making troubleshooting, maintenance, and long-term support more complex. On the other hand, many organisations have successfully scaled Power Automate by implementing strong governance practices and automation standards. I'm interested in hearing different perspectives from the community. Have you seen Power Automate become difficult to manage at scale, or has it reduced technical debt in your organization? What governance, architecture, or operational practices have worked best for balancing innovation with maintainability?
