Data Monitoring

===What is Data Monitoring? Data monitoring is the process of reviewing the information entered into the database for accuracy and completeness. The data can also be evaluated to ensure the entered information will accomplish the goals of the registry or clinical study. Data monitoring is performed proactively, either continuously or on a set schedule. The goal of data monitoring is to ensure high-quality data. Data monitoring can catch data that are inconsistent with other participants and therefore may need verification. It can also detect trends or patterns that differ from information collected in the past. Interventional clinical trials are most often monitored by a Data Monitoring Committee (DMC).

The Need for Data Monitoring

Data monitoring allows an organization to proactively maintain a high, consistent standard of data quality. By checking data routinely as it is stored within applications, organizations can avoid the resource-intensive pre-processing of data before it is moved. With data monitoring, data is quality checked at creation time rather than before a move.

Attributes of Data Monitoring

Simply put, monitoring data is the act of having procedures, technologies and benchmarks in place for tracking the quality and usefulness of data.

The first step to monitoring data is establishing data quality metrics or criteria that are tied to specific business objectives. After establishing the groundwork, you will compare the results over time, allowing for improvement and deeper understanding of how your data can best be used.

Some critical attributes of data quality that are frequently monitored by organizations include:

• Completeness
• Uniformity
• Accuracy
• Uniqueness

Data quality monitoring helps you reveal problem areas where the most inaccuracies are observed, track unusual or abnormal behaviors, and identify where you should focus your data quality initiatives.

Benefits of Data Monitoring

• A holistic view of the data ecosystem is created and maintained, providing increased visibility.
• Agility and speed are increased across an organization because data can be used immediately.
• Areas, where inaccuracies are most commonly found, can be identified and monitored to find the causes of the issues.
• Challenges associated with the propagation of erroneous or inconsistent data are eliminated by checking data at the time of its creation.
• Closely monitored data enables completeness, consistency, and accuracy.
• Connections can be established between data from disparate sources across an organization.
• Data can more easily be standardized.
• Established data quality metrics can be tracked and reported on to provide insights into adherence to standards and goals.
• New data quality parameters can be added to data monitoring processes to keep pace with changing priorities or concerns within an organization.
• Proactive checking of data against rules helps maintain high-quality, consistent data.
• Time and money are saved by eliminating manual data checks.
• Time spent preparing data for use is minimized, because its data monitoring facilitates ongoing data quality.