How to Implement Dynamics 365 Data Governance: Framework, Best Practices, & Data Quality Controls

A renowned real-time 3-D gaming development platform suffered a staggering loss of approximately $110 million and a drop of 35% in its shares due to a data quality incident in 2022. Their tool ingested bad data from a large customer, corrupting the training data set for its predictive ML algorithms. 

While organizations race to modernize their ERP and leverage the newly launched AI Agents and Copilot in Microsoft Dynamics 365 Finance & Supply Chain Management (D365 F&SCM), they forget that 95% of AI’s success depends on data quality. Focusing on data quality management is not an option anymore but a necessity. 

In this blog, we will focus on data quality governance, from the impact of poor data quality to a beginner’s guide to help you build your data governance framework.

Improve data governance in Dynamics 365 with STAEDEAN’s no-code embedded Data Management Solutions for integration, migration, EDI, security, and master data management.

The business impact of poor data quality in Dynamics 365

If you do not have data quality rules and processes in place, employees can import or create duplicate records, enter data with spelling mistakes, or inconsistencies in Microsoft Dynamics 365. 

Below, we share some examples of the impact of poor data in the ERP that can hamper your business.

1. Financial impacts: Mistakes and inconsistencies in data entry in the ERP can negatively impact revenue. For example, if you have no conditions set for the credit rating field in Dynamics 365 with a fixed range for each rating, your employees can give a vendor a ‘good’ rating when they should be given a ‘bad’ rating, and vice versa. 

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3. Poor decision: Poor data quality impacts decision-making. For example, a branch of pharmacies enters incorrect data in the inventory journal. This could lead to overstocking or understocking due to inaccurate inventory data in the ERP.

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5. Customer experience issues: Missing data and errors can result in a negative customer experience. For example, you are moving customer records from the Customer Relationship Management system (CRM) to D365 F&SCM, and some data is missing. If you need to send out a delivery to the customer, this will cause confusion and delays.

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7. Operational inefficiencies: Poor data quality can create inefficiencies in business operations. For example, an employee makes an update in the PLM system but misses updating or integrating with the ERP system. If the product record is shared without being validated with other systems, it could result in the production of a faulty product.

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9. Reporting and analytics failures: If there are Dynamics 365 data issues and errors, any reports or forecasts will not be accurate and your team will be spending more time in data cleansing. For example, if a sales representative enters incorrect information in multiple orders due to incorrect pricing data, the total sales figure would be incorrect.

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11. Compliance and security risks: Data privacy regulations require certain policies and checks on the storage, usage, and access of private data, such as customer data. You will be violating regulations and paying hefty fines if there are no:

• data quality checks

• a change management system

• a secure way to access, manage, and distribute that data

• audit management processes

Related reading: How Can You Secure Your Master Data in Microsoft Dynamics 365 ERP?

Common data quality challenges in Dynamics 365

Below are some common data quality challenges Dynamics 365 users face during different stages of the data lifecycle.

1. Duplicate records: A very common Dynamics 365 data quality challenge, is duplicate records due to employees either importing records from other systems or making errors while saving the record with the incorrect spelling. This leads to confusion and can cause delays.

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3. Inconsistent data entry: When you do not have data quality rules and definitions followed across teams, data can be entered in different formats across regions. An example is the way different date formats, or phone numbers are entered in different locations. 

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5. Missing data: When fields for crucial data are not marked mandatory, such as contact details or financial data, and are left blank while creating a record it can cause issues for business processes. For example, when contact information or the address is missing, deliveries can be delayed.

Related reading: Scenarios to Manage Master Data if You Use Dynamics 365

1. Data relationships issues: Dynamics 365 follows a data hierarchy. Data relationships issues occur when records that should be linked, such as contacts and accounts, are not properly mapped in a sequence. For example, if a supplier contact is not linked to the correct account, you cannot reach out to them to place an order.
   
   

2. Integration synchronization problems: When data between Dynamics 365 and other business systems, such as ERP or Warehouse Management System (WMS), is not synchronized, it results in data discrepancies. An example would be incorrect inventory data in the WMS, resulting in stockouts or overstocking.

Addressing these common challenges in Dynamics 365 is crucial for maintaining data quality. Implementing a data governance framework using the right data governance tools is important to improve data quality.
Building a data governance framework for Dynamics 365

Establishing a structured and defined way of accessing, using, processing, distributing, and securing the data organization-wide becomes paramount by the means of a data governance framework because:

• Businesses rely on reporting and analytics to make informed business decisions based on the organization’s data that is spread across systems with differing business rules and logic.
• Organizations are creating a LOT of data and want to use it as a core asset. By 2028, global data creation is projected to grow to more than 394 zettabytes (Statista)
•  The introduction of AI agents and CoPilot in Microsoft Dynamics requires organizations to focus on data quality for them to derive value and automate processes.
• When data access is not clearly defined, sensitive data could be exposed to AI algorithms that could also pose a security risk.

A data governance framework is a document that defines business terms, policies, procedures, and rules for all business units for creating, collecting, storing, and distributing data in an organization. 

It also defines the roles and responsibilities of the data team responsible for maintaining data governance across the organization. 

A. Core components of a data governance framework

1. A glossary: defines the different types of data and the teams involved.
   
   

2. Data governance team/pyramid: explains the roles, responsibilities, and structure of the data governance team.

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   Data catalogs: help find, organize, and classify data.

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   Data governance plan: explains rules and protocols for data distribution, data safety, data access, and data quality governance.

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   Data security and compliance guidelines: are defined keeping in mind government laws, industry regulations, protection from data theft, fraud, and misuse.

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   Data storage guidelines: ensure optimum data storage, categorization, access, and retrieval.

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   Data standards: help maintain the consistency of data between teams and systems.

We share the top 3 data governance frameworks in this blog post.

B. Data ownership and accountability

Effective data governance in Dynamics 365 starts with clearly defined roles and responsibilities. Assigning data ownership ensures that individuals are accountable for maintaining data governance and quality.

1. Key Roles in Data Governance

We outline these responsibilities in the data governance pyramid in more detail in our blog: What are the Roles and Responsibilities in Data Governance?

2. Dynamics 365 security roles and governance alignment

Dynamics 365 offers a role-based security model that complements governance by controlling access to data. They offer roles, duties, privileges, and permissions. 

The security roles impact licensing and cost of the software. So, getting this right from go-live is important. A lot of organizations make the mistake of giving all their employees System Administrator access, which has access to all your organization’s data. What is worse is that it also comes at the highest license costs and should be given very selectively.

It is also important to understand Segregation of Duties (SoD) rules, and SoD conflicts, and monitor security roles even post data migration, as team members move within or outside the company. 

3. Responsibility matrix (RACI Template)

The RACI (Responsible, Accountable, Consulted, Informed) responsibility matrix can be tailored to your data governance structure. This matrix helps clarify which roles are engaged in key governance activities within Microsoft Dynamics 365. 

Data Governance RACI Matrix for Dynamics 365

C. Data quality standards and policies

To enforce data governance in Dynamics 365, organizations must establish clear rules for how data is entered, stored, accessed, and maintained.

Defining data quality rules

You can define data quality rules in Dynamics 365 using our Data Quality Solution. We share a few examples below, and more examples in this blog.

1. Validation rules: Make a field mandatory

As an example, this rule could be applied to any record to make the field mandatory by adding certain conditions using our Data Quality solution which is part of our Master Data Management Solution.

2.  Duplicate checks: for fields to ensure information is not repeated.

You can add duplicate checks to ensure information is not repeated for the exact same values in two fields.

3. Action rules: add transformation lists for any field

While using a transformation list, you can define the source value in one field and target value for another field to ensure users have to select a value from a pre-defined list and cannot add their own value. 

So, when one enters the source value for the first field, the target value gets automatically populated for the second field based on a pre-defined list. 

Implementing data quality controls in Dynamics 365

We share recommended tools and processes to implement your data governance strategy in the section below.

A. Native Dynamics 365 tools for data quality

• Duplicate detection rules: At STAEDEAN, we offer a Data Quality solution that offers duplicate detection rules for phonetic searches and other more detailed searches in comparison to native Dynamics 365 tools. 

• Business rules and field validation: Using our Data Quality solution, you can configure these using conditions for a field to be populated based on pre-defined conditions for an associated field. 

• Field-level security: Our Master Data Management solution can help you set up field-level security for sensitive data. You can lock fields for editing or assign view-only access to specific team members.

• Audit history: We offer an audit log feature that tracks the history of all the data changes made using our workflow functionality.

• Required fields and field masking: Our Master Data Management Solution allows you to make fields mandatory in workflows, and also as a data quality rule. Additionally, using the Dynamic Field Security feature, you can hide fields that house sensitive data or limit access.

B. Process automation for data quality

Configure/customize data quality processes to maintain consistency and reduce manual errors during data entry and imports:

• Data entry workflows: Design workflows that allow you share specific fields with users through a regulated data entry process, with timelines.

• Approval processes: Add an approval step in the data entry workflows and also connect to (paid) web services to authenticate critical data externally.

• Regular data quality checks: Schedule periodic data quality assessments to identify and rectify issues proactively.

• Change management for master data: Ensure there is a standardized and secure process to update master data records.

• Active data quality rules: Add data quality rules that work on data entry and data imports, improving the quality of your data from the creation stage.

Each of these processes can be configured using STAEDEAN’s Master Data Management Solution.

Best practices for Dynamics 365 data governance

Keep these tried and tested best practices in mind while implementing your data governance strategy.

• Start with your most critical data records: Start with the most important data first such as customers, products, and financial records to implement data governance rules.

• Implement preventative controls before detective ones: We recommend a proactive rather than reactive approach. Enforce strict data quality rules at the point of entry and save hours spent on data cleansing afterwards.

• Use business rules to enforce data standards: Implement business rules and definitions to standardize data entry across the organization, ensuring consistency.

• Create a cadence of data quality assessments: Scheduling periodic assessments allows you to evaluate data quality metrics, address issues, and improve processes.

• Implement clear data entry procedures: Provide training and documentation to ensure users understand data entry guidelines and definitions.

• Design a data quality dashboard in Power BI: Utilize Power BI to create dashboards that visualize data quality metrics, enabling stakeholders to monitor and address issues effectively.

• Schedule monthly data reviews with stakeholders: Hold regular meetings with key stakeholders in the data governance pyramid to review data quality reports and discuss improvements.

Above are just a few data governance best practices, you can read more Data Governance Best Practices for Dynamics 365 F&SCM in our blog.

Measuring and maintaining data quality in Dynamics 365

To assess the success of your data governance framework and refine your processes, establish checkpoints at different stages. Below, we share some examples of what you can do.

• Key Data Quality Metrics to Track: Track number of records with errors, number of duplicate records, last updated date of records. 

• Creating a Power BI Dashboard for Data Quality: Develop a Power BI dashboard that aggregates data quality metrics from Dynamics 365. This dashboard can include trend lines showing improvements or declines in data quality metrics.

• Regular Audit and Review Processes: Establish a routine for auditing data quality, including monthly reviews of data quality metrics, quarterly audits and annual assessment of data governance policies and procedures.

• User Training and Reinforcement: Conduct regular training sessions to educate users on data governance policies and best practices. Reinforce these concepts through recorded workshops, regular communications, and a dedicated channel.

Once you implement these data quality metrics, it is also important to adopt a cycle of continuous improvement.

Conclusion: An ideal fit for Dynamics 365

Implementing a data governance framework in Dynamics 365 takes time, trial and error, and is a long-term commitment. 

The benefits of a well-established and robust data governance framework will soon start rolling in when you see smoother operations and can make smarter business decisions.

STAEDEAN’s no-code/low-code embedded Data Management Solutions, built using Microsoft business logic, can help you maintain data quality, manage master data management, integration, and compliance. 

They could be the ideal fit for your data governance vision. Using our solutions, with the right data governance strategy, you can foster a culture that prioritizes data governance and can use data as a core asset for business growth.