What is Data Quality?
Data quality refers to the overall utility of a dataset(s) as a function of its ability to be easily processed and analyzed for other uses, usually by a database, data warehouse, or data analytics system.
What do I need to do know about data quality?
Quality data is useful data. To be of high quality, data must be consistent and unambiguous. Data quality issues are often the result of database merges or systems/cloud integration processes in which data fields that should be compatible are not due to schema or format inconsistencies. Data that is not high quality can undergo data cleansing to raise its quality.
What activities are involved in data quality?
Data quality activities involve data rationalization and validation.
Data quality efforts are often needed while integrating disparate applications that occur during merger and acquisition activities, but also when siloed data systems within a single organization are brought together for the first time in a data warehouse or big data lake. Data quality is also critical to the efficiency of horizontal business applications such as enterprise resource planning (ERP) or customer relationship management (CRM).
What are the benefits of data quality?
When data is of excellent quality, it can be easily processed and analyzed, leading to insights that help the organization make better decisions. High-quality data is essential to business intelligence efforts and other types of data analytics, as well as better operational efficiency.