Author: Crystal Ahrens

To avoid problems stemming from inaccurate, incomplete, inconsistent, and untimely data, businesses must prioritize data quality metrics. These issues can result in lost revenue and raise concerns about regulatory compliance. Therefore, it is super important for businesses to understand the significance of data quality metrics and their measurement. This lesson is often learned the hard way, after the D365FO implementation, when your post goes live, and you’re confronted with poor data that could have been prevented.

In this post, we’ll dive into the key data quality metrics that should be part of your data migration and overall data management strategy. These metrics include accuracy, completeness, consistency, and timeliness. We’ll discuss how these metrics are measured and the impact of having low-quality data.

Accuracy:

Accuracy is the most important data quality metric; inaccurate data could lead to flawed decision-making and business operations. Accuracy refers to how closely the data reflects reality and should align with the business rules and defined parameters.

For instance, the KPI for data accuracy could be the Error Rate. This KPI measures the percentage of data entries that contain errors compared to the total number of entries. A high error rate implies low data accuracy, which demands immediate attention. Another KPI might be the Accuracy Rate of Predictive Models. This metric evaluates the accuracy of predictions made by data models against actual outcomes. A high accuracy rate suggests that the data used in the predictive models is accurate, leading to more reliable forecasts.

Measuring accuracy involves comparing the data against its actual source, analyzing any discrepancies and correcting them. It is also essential to identify the root cause of inaccuracies and address them

Data that is incomplete or missing critical information is as bad as inaccurate data. Completeness refers to having all the necessary information needed for the data to be considered valuable and trustworthy.

Measuring completeness entails validating that all expected information is present and ensuring that any gaps are filled. Completeness checks also include checking for duplicates and eliminating them.

Combining Azure DevOps and the Data Validation Workspace creates a comprehensive approach to maintaining data accuracy in D365FO. Azure DevOps provides a suite of development tools for planning, tracking, and collaborating on work throughout the implementation process. Its integration with D365FO enables businesses to automate data validation, identifying errors and inconsistencies early on. On the other hand, the Data Validation Workspace in D365FO serves as a robust data governance tool, facilitating data management and quality maintenance. It allows businesses to establish data validation rules aligned with their specific needs, ensuring accuracy and integrity. By leveraging Azure DevOps and the Data Validation Workspace together, businesses can establish a robust system for data validation, guaranteeing consistent, accurate, and timely data. This is crucial for effective decision-making and seamless business operations.

Solution delivery teams and System integrators play a key role in facilitating the transition to Azure DevOps and the Data Validation Workspace. It is vital to provide comprehensive training to empower business users in effectively utilizing these tools. The training should focus on practical usage, elucidating the process of setting validation rules, tracking errors, and fostering collaboration.

It is crucial to explain why these tools are used. Azure DevOps and the Data Validation Workspace improve data quality, reduce errors, and streamline operations, leading to better decision-making. By highlighting this, users can understand the value these tools bring.

Additionally, it is important to establish clear expectations. The solution delivery and System integrator team should communicate that adopting these tools is not an end in itself, but a way to maintain high data quality standards. Users should understand that the ultimate goal is to ensure accurate, consistent, complete, and timely data. This enables businesses to derive maximum value from their data, enhancing efficiency and competitiveness and not have business interruptions on Day 1.

Consistency:

Consistency relates to how the data is defined and presented. It includes validating that the data adheres to the defined standards and formatting rules.

Measuring consistency involves comparing the data against standardized naming conventions and formatting specifications, flagging any inconsistencies, and ensuring that they are corrected.

Consistency Check Reports in D365FO:

Consistency Checks in D365FO serve as an integral part of data quality management in the system, ensuring the uniformity and reliability of the data. The Consistency Check reports in D365FO allow users to identify any discrepancies or inconsistencies in their data in terms of formatting, terminology, and other specified standards.

After the data migration process, it is essential to run these Consistency Check reports. Running these checks at this point allows you to validate that the migrated data aligns with the predefined standards in D365FO. This step is a much-needed step as it can highlight any errors or inconsistencies introduced during the migration process, enabling you to take corrective actions promptly.

Running Consistency Check reports after data migration not only ensures that your data complies with the established rules and standards but also helps maintain the overall integrity of the system. By identifying and rectifying any discrepancies early, you can avoid potential issues that could impact business processes and decision-making in the future. Therefore, utilizing Consistency Check reports in D365FO is a key component of an effective data quality strategy.

Timeliness:

Timeliness relates to the speed at which the data is captured and the time it takes to update it. The value of data often declines as time passes, and if the data is not updated periodically, it could become irrelevant or useless.

Measuring timeliness involves setting expectations for how quickly the data should be updated and comparing the actual update timelines. It is crucial to ensure that data is updated promptly while still ensuring quality.

The measurement of timeliness in D365FO data migration refers to the assessment of the speed of data transfer and its subsequent update in the new system. It is crucial as delayed data migration can result in operational inefficiencies, potentially impacting the business decisions that rely on this data.

One KPI for measuring timeliness could be the ‘Data Migration Speed’, which is the rate at which data is transferred from the source system to D365FO. This KPI can be calculated by dividing the total volume of migrated data by the total time taken for the migration process. For instance, if 100GB of data was migrated in 2 hours, the Data Migration Speed would be 50GB per hour. This KPI provides an effective measure of the efficiency of the data migration process and helps identify bottlenecks that might be slowing down the migration, enabling timely corrective actions.

By closely monitoring these key performance indicators (KPIs), businesses can ensure that their data migration in D365FO is executed in a timely and efficient manner. This, in turn, enhances the effectiveness of cutover planning, resulting in a smoother transition.

In conclusion effective decision-making and business operations heavily rely on data quality metrics like accuracy, completeness, consistency, and timeliness. These metrics go beyond comparing 100 records in and 100 records out. Measuring these metrics ensures that your data remains trustworthy, reliable, and valuable. By using data quality tools and techniques, businesses can quickly identify and address issues, minimize errors, and enhance overall data quality. Investing in data quality metrics empowers organizations to trust the data in their new system and ensures smoother operations.