The opportunity to use actual performance data to set QI priorities and improve care quality is only possible now, because we have entered the era of big data in healthcare. Unlike at any point in the past, the widespread use of administrative and EMR systems allows us to assess quality-of-care over time and across institutions. This exciting opportunity offers great potential to transform healthcare, but caution is warranted. The completeness of administrative and EMR-based datasets can be suboptimal, their accuracy can be suspect, and standardization can be lacking. Asking healthcare providers to enter more data into these systems to aid quality measurement adds burden to already busy workloads and could increase burnout. We must find ways to collect the data we need to measure and improve quality in an accurate and timely manner without creating excessive demands on those who use clinical information systems to deliver healthcare daily. Novel technologies, such as natural language processing and machine learning, can help gather data from the array of unstructured information already present in the EMR, but issues with missingness and non-standardization remain. Also, it is important to note that administrative and EMR systems support process-based quality measurement much better than they support outcomes-based quality measurement, because the outcomes that are important to cancer patients (e.g., quality-of-life, recurrence, survival) are often missing from or hard to find in these systems. We must do more to gather outcomes that are relevant to patients, especially through increased use of validated patient reported outcomes, so that we can improve what matters most to patients.