Data quality is a measure of the condition of data based on data quality factors such as accuracy, completeness, consistency, reliability. 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 to serve its intended purpose. Product variety and complexity in finance sector has caused the emergence of many distinct operational systems that creates and enlarges seperate data silos each day. This situation increases the data quality requirements in the financial sector.
Our approach & Solution
We do not only approach data quality issues from a technology perspective. To build and efficient data quality solution, we focus on 3 main areas:
- Data quality organization, roles and responsibilities
- Procedures and guidelines to follow
- Technologies to detect and report data quality issues
Once the roles, responsibilities, procedures and guidelines are clear, it is time to invest in technology and start detecting data quality issues and sharing them with the stakeholders like data owners and data stewards.
Technological part of the solution consists of 3 components:
- A data quality data model that stores data quality rules and quality measurement results
- A data quality engine designed using ETL tools and database procedures
- A data quality dashboard