Separation of a few common hitches with efficiency in the use of power by Copilot; here are some of these important issues with their approaches to avoid them:
Over-Reliance on Suggestions from Copilot: The most common problem is dependence on the auto-suggestions that pop up from Copilot without taking into account the actual data or logic. This can result in incorrect representations or interpretations.
Avoidance: Critical review of the suggestions given by Copilot. Though this tool may make the work easier, its benefits will come only from understanding the data, the report context, and the Copilot recommendations.
Inconsistent DAX-or M-Code Generation:
Another pitfall is the generation of DAX or M code, as that code is neither well-optimized nor purpose-built. Sometimes, the generated DAX or M does not follow best practices or may result in performance degradation when working with larger datasets.
Avoidance: Ensure that the code generated by Copilot is reviewed for efficiency. If using DAX, check the performance and optimize complex calculations. It is also key to test the generated code in different scenarios to confirm its accuracy and efficiency.
Misconceptions Regarding the Limitations of Copilot:
Sometimes, the Copilot is expected to perform all reports, including very elaborate customizations. Copilot is not necessarily built to take care of very complex reporting requirements or specific use cases.
How to Avoid:
Know Copilot's capabilities and limitations. In any area where very specialized customization is required, manual fine-tuning of reports and queries will have to be exercised. Know where Copilot excels and where manual intervention is inevitable.
Data Privacy and Security Issues:
Copilot uses artificial intelligence and machine learning to provide insights, recommendations, suggestions, and other features. Sometimes, users might be concerned about the privacy of their data as they are dealing with sensitive information.
How to Avoid:
Make sure that you configure proper security roles, apply row-level security (RLS), and observe the data privacy policies of your organization. Exposure of sensitive data through Copilot suggestions must be avoided inadvertently.
When users understand these challenges and apply best practices, such as validating outputs by Copilot, enabling code optimization, and leveraging Copilot features within its strengths, the experience usually improves, and Power BI Copilot offers greater value.
