Nowadays, many organizations are focussing on data quality is even better than it was before. The reason is they know that good data quality is always good for business.
Data Quality is the ability of a given data set to serve an intended purpose which must be consistent and unambiguous. In order to assure the data fit for consumption and meet the needs of data consumers, you might have to plan, implement, and control on quality management techniques.
When improving data quality, the aim will be to measure and improve a range of data quality dimensions. The basic dimensions of data quality are as below:
- Uniqueness / Deduplication
Data Quality Management
To prevent future data quality issues and fulfil the data quality Key Performance Indicators (KPIs), the aim for data quality management is to employ a balanced set of remedies for achieve the business objectives.
The data quality KPIs must relate to the KPIs used to measure the business performance in general.The categories of themfor core business data assets are as follows:
The remedies used to prevent data quality issues and eventual data cleansing includes these disciplines:
- Data Governance
- Data Profiling
- Data Matching
- Data Quality Reporting
- Master Data Management (MDM)
- Customer Data Integration (CDI)
- Product Information Management (PIM)
- Digital Asset Management (DAM)