5 Best Practices of Effective Data Quality Management
For businesses, maintaining the quality of the ever-growing volumes of data is easier said than done. Data quality management is not just about removing incorrect data or repeat entries but adhering to quality standards all through the streamlining and integration of processes.
There are many strategies to ensure data quality and a focused plan for data governance and data management will have far-reaching benefits. It is always recommended to prevent data errors and anomalies rather than dealing with data failures. Prevention is indeed better than cure as far as data quality management is concerned.
The business units that are the main consumers of the data should effectively define quality to ensure priority for the business-critical data. A dedicated team of data leaders from within the organization who control data integrity in the system can go a long way in enhancing the quality of data. They can be held accountable for the accuracy of data, which will ensure good quality data generation at the source.
Setting up a board with representations from all business functions will streamline the process of ensuring data quality across various departments in the organization. The board should have periodic meetings to define new data quality targets and analyze the status of data quality within various business units.
Data is the biggest asset of any organization. So, make sure to carry out quality audits to ensure that the data, entering the systems is of acceptable quality. Every time the data is retrieved or modified, it may lose its accuracy and bad data elements can negatively affect subsequent data stores, thereby impacting business. Setting up an intelligent virtual firewall can block the bad data at the point of origin and prevent it from entering the system. The firewall on detecting corrupt data can be rectified before letting it pass into the enterprise’s environment.
Best Practices for Data Quality Implementation
Data quality management is a cyclic process that involves multiple steps and foolproof data management practices. Make sure to set up a system to implement data quality at all stages of its processing.
1) Data Quality Assessment:
Random and regular quality audits and detailed inspection of the company’s data will go a long way in identifying data quality issues within its environment if any. Poor quality data can slow down the business processes and hinder the attainment of your goals. Data quality assessment is a sensible investment for organizations to ensure data quality improvements and measure the outcomes. A formal report can be drawn out with a listing of the findings to be circulated among stakeholders and the top management levels to create better awareness and compliance of data quality improvement actions.
2) Data Quality Measurement:
Data quality measurements will help organizations to measure the quality of data. Setting up acceptable standards for these metrics is the basis of implementing improvement processes. Factors like completeness, consistency, and timeliness can be employed to ensure the desired levels of quality. These metrics can also be used while formulating data validity rules. These data quality scores can be stored and periodically assessed to measure the improvement.
3) Incorporating Data Quality into Business processes:
Data quality systems should be incorporated into the business processes of organizations. A thorough understanding of data flow within each application will give a clear overview of the possibilities in data inspection and control routines. These requirements can thus be included in the system’s functional requirements.
4) Data Quality Improvement in operational systems:
To ensure the stipulated levels of data quality, there should be a contractual agreement between the data providers and consumers within which data is shared. Data metrics like performance SLA s can be incorporated into these contracts easily. Setting up a data standard will ensure a smooth flow of information and an active data center management that would ensure that data remains beneficial to both sides.
5) Timely Remedial actions:
Manual or automated data quality inspections can be carried out to ascertain the working levels and take remedial actions if required. There are data defect tracking software systems, which will ensure quick remedial actions if the data quality levels drop beyond the standards.
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