Data Cleansing: From Merging Data, Standardizing Data, Deduplicating & Verifying Data to Exporting & Implementing Real World Data
Time: 3:00 pm
day: Pre-Conference Day
Details:
By identifying and rectifying errors, inconsistencies, and missing values, data cleansing enhances the reliability and validity of analyses and conclusions drawn from RWD. Standardizing data elements and formats enables meaningful comparisons within and across datasets, supporting data integration and linkage efforts. Quality-assured RWD enhances the credibility of study findings, fostering confidence in evidence-based decision-making in healthcare and other domains. How to achieve maximum data cleansing across numerous datasets and implement them in real-world data?
- The value of ‘cleaning’ data before using it as evidence
- Points to consider before cleaning data to ensure high data quality
- Effective and efficient ways to clean data
