Step 7: Develop a Quality Management Plan¶
Once your HPXML program is launched, you can take advantage of the standardized data you have been collecting. This could be enhanced reporting, integration with real estate databases, or any of the other activities identified in the Overview section of this document.
To ensure success with these activities, the collected data must accurately reflect the characteristics of the homes participating in the program. It is important to develop a data quality management plan that allows for continuous evaluation of the program to maximize accuracy.
In addition to standard quality assurance activities, the following practices can be integrated into a program plan:
Complete a Regular Data Review¶
Consider scheduling a regular data review at least once a year, or more frequently, if possible. This is a basic audit of your data to verify that you are capturing all of the required data, to screen for data anomalies, and to ensure your validation systems are working as expected. After these meetings, consider communicating findings to all trade allies and software vendors to facilitate continuous improvement.
QA Trend Analysis¶
Consider working with your program software vendor to add trend analysis capability into your reporting systems. Even with an automated validation system, it is possible to game the data and report only what will pass the validation protocol. Your project-level QA strategy should provide some verification activities to reduce the potential of this. However, it is often not cost-efficient to schedule third-party verification of every home. Looking for data trends can assist in identifying potential issues. For example, if a large portion of jobs are reporting an installed condition that is almost identical to manufacturer specifications, you could focus QA on these jobs to further verify data accuracy.
Integrated Measurement and Evaluation¶
With a rise in automated billing analysis software, automated metering and connected thermostats, there is an emerging ability to verify performance data in a more dynamic fashion. This can help quickly identify potential issues with data quality and help focus on continuous process improvements.
Regardless of the strategy you choose for your program, it is important to complete a comprehensive evaluation of your data before you begin to export data to market, especially if you intend to use the data to guide financial investments.