How do you handle data gaps in performance monitoring for restructuring?

1 Identify the data gaps

The first step in identifying data gaps in your performance monitoring framework is to review the data sources, systems, and processes used to collect, store, and report the data for each performance indicator and metric. Additionally, it is important to compare the data requirements and definitions with the actual data availability and quality. Common types of data gaps include missing data that is not collected or reported for certain periods, segments, or dimensions; inconsistent data that is collected or reported differently across different sources, systems, or processes leading to discrepancies or errors; and irrelevant data that does not align with the current objectives, scope, or context of the restructuring project or reflect the changes or impacts of the restructuring initiatives.

2 Analyze the data gaps

The second step is to analyze and assess the impact of the identified data gaps on your performance monitoring framework. You should evaluate the severity of the data gap in terms of affecting the accuracy, validity, or completeness of the performance indicator or metric. Additionally, consider how often the data gap occurs and for how long it persists. It is also important to investigate the root cause of the data gap, which could be related to the data source, system, or process, or to the data collection or reporting method. Lastly, determine the best way to address or resolve the data gap and any resources, costs, or risks involved.

3 Address the data gaps

The third step is to address the data gaps that you have analyzed, and implement the solutions that you have identified. Depending on the type, severity, frequency, and cause of the data gap, you can fill it by collecting or reporting the missing or inconsistent data, adjust it by correcting or reconciling the data, or exclude it by removing or ignoring the data. This could involve using alternative or additional data sources, systems, or processes; standardization, normalization, or transformation techniques; filtering, masking, or imputation techniques; or updating the data requirements or definitions.

4 Monitor the data gaps

The fourth step is to monitor the data gaps that you have addressed and evaluate the effectiveness of the solutions you have implemented. You can measure and track indicators of data quality and reliability such as completeness, consistency, and relevance. Completeness refers to how much of the required data is available and reported for each performance indicator and metric. Consistency evaluates how much of the available and reported data is coherent across different sources, systems, or processes. Relevance assesses how much of the available and reported data is aligned with the current objectives, scope, and context of the restructuring project. By following these four steps, you can handle data gaps in performance monitoring for restructuring and ensure your performance indicators and metrics are accurate, valid, and complete.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?