3.1.3. Importing Data

💡 First Principle: Efficiently and accurately transferring data from external sources into ServiceNow ensures that the platform remains a comprehensive single system of record, without compromising data integrity or introducing duplicates.

Scenario: Your organization has acquired a new company, and you need to import thousands of new user records from their HR system into ServiceNow, ensuring that existing users are updated and new ones are created without duplicates.

ServiceNow instances rarely operate in isolation. Organizations often need to import large volumes of data from external systems (e.g., user lists from HR systems, asset data from discovery tools, legacy incident records). The fundamental 'why' of the data import process is to populate ServiceNow tables with accurate and structured information, enabling it to become a comprehensive Single System of Record (as discussed in 1.2.1) and facilitating the automation of workflows based on this data. A flawed import process can lead to data duplication, corruption, and an unreliable platform.

Key concepts and processes for Importing Data:

• Data Sources: External files (e.g., CSV, XML, Excel) or direct database connections (via JDBC or web services).
• Import Sets: A temporary staging table (sys_import_set) within ServiceNow where imported data is initially loaded.
  • Why use it? It acts as a buffer, allowing you to review and transform raw incoming data before it's moved into the permanent production tables. This prevents bad data from directly polluting your instance.
• Transform Maps: Crucial for the import process. A Transform Map (sys_transform_map) defines the relationships between the fields in an Import Set (staging) table and the fields in a target (production) table.
  • Field Maps: Define how each source field from the import set maps to a target field in the destination table.
  • Coalesce Fields: One or more fields in the Transform Map can be designated as "coalesce" fields. If a record in the target table already exists with the same value(s) in the coalesce field(s), the existing record is updated. If no matching record is found, a new record is inserted. This prevents duplicates and supports updates.
  • Transform Scripts: Server-side scripts within a Transform Map that allow for more complex data manipulation or validation during the transformation process (e.g., converting dates, concatenating strings, looking up reference values).
  • Why use it? To ensure data is correctly mapped, transformed, and handled (insert/update) during the transfer from staging to production tables.
• Data Import Process Steps:
  1. Load Data: Import the data file into an Import Set table.
  2. Create Transform Map: Define the field mappings and coalesce criteria.
  3. Transform Data: Run the Transform Map to move data from the Import Set table to the target table.
• Scheduled Imports: Imports can be scheduled to run automatically, ensuring data synchronization with external systems.

As a CSA, you'll often be responsible for setting up and troubleshooting data imports. A thorough understanding of Import Sets, Transform Maps, and coalesce functionality is vital for maintaining data quality and efficiency when integrating information from various sources into your ServiceNow instance.

💡 Tip: Always use coalesce fields when importing data that might already exist in your target table (e.g., updating user records, not creating duplicates). Test your Transform Maps thoroughly in a sub-production environment with a small dataset before running large imports in production.

⚠️ Common Pitfall: Not using a coalesce field when importing data that might already exist, leading to duplicate records. Or, using an unreliable coalesce field that doesn't uniquely identify records.

Key Trade-Offs:

• Automation (Scheduled Imports) vs. Manual Control (One-time Imports): Automated imports ensure continuous data synchronization but require careful setup and monitoring. Manual imports offer more control for one-off tasks.

Reflection Question: How do Import Sets and Transform Maps, particularly the concept of coalesce fields, ensure data integrity and prevent duplication when importing data into ServiceNow?