Data is deemed of high quality if it correctly represents the real world to which it refers and meets business-driven metrics. Data quality is essential, but the level of quality and the related investment depends on an understanding of business and investment priorities.

High-quality data must be complete, timely, accurate, consistent, relevant and reliable. Initiatives that only address portions of the data quality strategy are ineffective and costly in the long term and tend not to be aligned with overall business priorities. What is required is an ongoing program of improvements in all aspect of the enterprise ranging from data entry standards and measures to technical data validation routines to how business organization and structure changes are implemented.

The systematic analysis of data, or data profiling, gathers actionable and measurable information about its quality. Information gathered from data profiling activities is used to assess the overall health of the data and determine the direction of data quality initiatives.

Data cleansing is a continuous process that requires corrective actions throughout the data lifecycle. Data cleansing activities must have adequate and dedicated resources from both the business and technical support organizations. Business resources are critical to provide context and insight into potential data anomalies.

Automated and/or manual processes are used to continuously evaluate the condition of an enterprise’s data. Information obtained from these data monitoring activities would serve to guide the plan and focus for data improvement initiatives.

Data compliance consists of the ongoing processes to ensure adherence of data to both enterprise business rules, and to legal and regulatory requirements. Data compliance includes four areas, controls, audit, regulatory compliance and legal compliance.

Data traceability follows the lifecycle of data to track all access and changes to the data. It helps an enterprise demonstrate transparency, compliance and adherence to regulations. Data traceability, along with data compliance, can be considered part of a data audit process.

Although data consistency seems to be a part of data quality concepts, it is slightly  different concepts. We bring solution to check data consistency between different data sources as well and this solution can be used in datawatehousing, data migration projects and master data verification needs.