What is Code Standardization

Code standardization is essential in data management to ensure uniformity across datasets. Different sources may use different codes or labels for categories like status, priority, severity, and gender. Since the data lakehouse combines data from multiple sources into a single dataset for analysis, it needs to standardize these category values so they are uniformly understood. This process is called Code standardization. Digital.ai Analytics has standardized codes for these category values from different sources to ensure consistency.

For example, different departments might use various codes for the same status, such as "Active," "A," or "1." Without standardization, conforming and analyzing this data can cause inaccuracies. Standardizing these source codes or labels ensures smooth integration and comparison across sources.

The benefits include cleaner, more reliable data, improved analytics across sources, and efficient data integration. Standardized data ensures consistent reporting which is crucial for making informed, analytics-driven decisions.

Some Digital.ai sources allow the addition of custom categories or new source codes or labels to existing categories. When these are added, especially when new source codes are introduced to existing out-of-the-box categories or the meaning of custom source codes is modified, customer admins need to map the new or modified source codes/labels to the appropriate standardized codes for analytics.

This screen allows customer admins to perform this activity directly in the Digital.ai Platform UI.