Data quality management is crucial for both unstructured and structured data, though some of the steps taken may look different depending on the type of data you’re working with.

Talend Data Quality offers data standardization based on predefined rules, while SAS Data Management uses a combination of parsing rules, standardization rules, phonetic matching and token-based ...

6. Inadequate Data Quality Monitoring The most important thing to remember about data quality is that it is not a one-time effort; it requires continuous monitoring and improvement.

Data quality reporting: communicate around the state of data quality to consumers Data quality issue management: remediate root causes of data issues Data quality improvement: have process and ...

Better demand forecasts. Inventory management. Big data can become even more powerful when combined with other new technologies, such as artificial intelligence (AI) and the internet of things (IoT).

Building Data Muscle for AI Innovation As AI becomes ubiquitous, the models powering it require more high-quality data with increasing complexity. The amount of global data being produced is ...

Risk-based quality management, or RBQM, is the process by which pharmaceutical organizations monitor and manage quality throughout clinical trials.

Thus, the new Holy Grail for state and local data managers is data integrity, an essential goal in evolving an information system to support decision-making and the timely delivery of services to ...