Data Quality Management
What is Data Quality Management?
Data quality management includes all actions and procedures intended to guarantee and maintain the quality of data within an organization. Data quality management includes, among other things, measuring the condition of data based on factors such as accuracy, completeness, consistency, reliability and timeliness. Measuring data quality levels can help organizations identify data errors that need to be resolved and assess whether the data in their IT systems is fit for purpose.
The importance of Data Quality Management
Bad data can have significant economic consequences for businesses. Poor quality data is often seen as the source of operational problems, inaccurate analyzes and poorly designed business strategies.
When data is of high quality, it can be easily processed and analyzed, providing insight that helps the organization make better decisions.
How to start your initiative?
Data quality is an all-time exercise. So why not start in a small area where solving this data quality issue will lead to rapid delivery of value to the organization?
Four steps to start improving the quality of your data:
- Evaluate your data current state
- Define rules (process modification, cleaning, deduplication, standardization, etc.)
- Apply the new rules to address data quality across the organization
- Monitor and improve. Data quality is not a one-off exercise and must be monitored, adapted over time.
For more than twenty years, Synchrotech has been working with its clients in the context of projects which combine, among other things, data integration, Business Intelligence, Business Analytics, Data Management, Data Governance, etc. What common points do all these types of projects share? The data and above all, its quality!