Data quality monitoring entails auditing the collected data to ascertain its validity and reliability before it is used to inform organizational decisions. The process ensures data generated, used, and stored by an organization meets set quality standards. The standards include data accuracy, reliability, comprehensiveness, and promptness. Data quality monitoring is usually undertaken by software programmed to document key performance indicators. From the recorded data, analysts generate reports used by executives to review previous goals and establish new ones. Thus, the software conducting analysis must meet the following criteria:

Functionality

Data monitoring software should meet the data requirements of its user. Proper software should evaluate the data sets and points expected by the organization and report them consistently. The software should also alert data analysts whenever data generated and collected deviates from the standard. Additionally, its data output should be easily understandable to afford administrators timely decision-making after analysis. Software that does not meet this standard scrutinizes data poorly, thus generating inaccurate information. That results in costly unintended results since the organization acts on misleading information. Moreover, the software should be easily updateable to guarantee continued functionality. Lastly, the software-generated results should justify the resources expended to keep it running.

 Real-Time Tracking

Outdated information can be highly problematic to any organization. It can cripple operations or be the difference between success and failure. Thus, data monitoring software must provide real-time feedback. Real-time information instantly shows whether progress is being made, thus supporting reform efforts. Moreover, real-time information highlights problems before they become disruptive, allowing organizations to make timely changes.

Compatibility

Data is usually gathered from wide-ranging sources and using various methods and tools. Good data monitoring software should be compatible with multiple data collection tools. Such a quality provides a one-stop shop where all data can be merged and analyzed. Furthermore, it eases a data analyst's workload by facilitating a seamless transition between platforms.

Intelligence

Problem-solving is a highly sought skill by employers. However, that quality is also expected of automated systems. The data monitoring software should be adept at troubleshooting whenever it encounters issues. That means computing possible outcomes and charting the best course of action to meet data quality targets. The software should also flag suspicious activity to responsible persons to ensure organizational objectives are not jeopardized or derailed.

A good data monitoring software also evaluates the suitability of targets set by an organization. Some executives may be overly ambitious in the data quality metrics they expect, while others may be underachieving. Such evaluation keeps an organization accountable to its objectives and promotes productivity. Lastly, the software should offer constructive criticism to facilitate appropriate data utilization.

For more information, contact a local company, like FirstEigen.