Taming the Data Beast: Your Guide to ServiceNow Import Sets

Alright, let’s talk data. In the world of IT, data is everywhere, and it rarely sits neatly in one place. We’ve got user lists from HR, configuration items from discovery tools, software licenses from procurement, incidents logged in external systems… the list goes on. The challenge isn’t just having data; it’s getting that data into your core systems, like ServiceNow, in a clean, consistent, and structured way.

If you’ve ever faced the task of populating a ServiceNow table with information that lives outside the platform, you’ve probably heard of, or will soon encounter, Import Sets. Think of Import Sets as your indispensable toolkit for data wrangling in ServiceNow. It’s not just for initial migrations; it’s for ongoing synchronization, bulk updates, and keeping your ServiceNow instance reflecting the true state of your IT environment.

Whether you’re new to ServiceNow, an IT support engineer looking to understand data flows, a seasoned admin tackling complex integrations, or even someone coming from a BMC Remedy background, mastering Import Sets is a fundamental skill. It simplifies what could otherwise be a messy, manual, and error-prone process. So, grab a coffee, and let’s break down how to truly leverage this powerful feature.

So, What Exactly is a ServiceNow Import Set?

At its heart, an Import Set in ServiceNow is essentially a staging table for external data. Imagine you’re running a busy warehouse. Before you can put new inventory items onto the shelves (your target ServiceNow tables), they first arrive at a loading dock (your Import Set table). At this loading dock, you inspect them, perhaps re-label them, and prepare them for their final destination.

That’s precisely what an Import Set does. It’s a temporary holding area where data from an external source (like a CSV file, an Excel spreadsheet, a database, or even an API response) first lands. This data isn’t immediately committed to your “real” ServiceNow tables, like sys_user or cmdb_ci. Instead, it sits in an Import Set table, ready for you to process and transform it before it moves to its final home.

Every time you load data into an Import Set, ServiceNow creates a new Import Set table instance with a unique name, usually prefixed with u_, followed by a generated identifier. This temporary table then holds all the rows and columns from your source data, exactly as they appeared externally.

Why Are Import Sets So Important in ServiceNow?

You might be thinking, “Why bother with a staging area? Can’t I just push data directly?” Well, you could, but it would be like throwing unlabeled boxes straight onto your warehouse shelves – chaos. Import Sets provide several critical advantages:

Data Quality Control: This is perhaps the biggest one. External data is rarely perfect. It might have different column names, inconsistent formats, missing values, or even duplicate entries. The Import Set acts as a buffer, allowing you to clean, validate, and standardize data before it pollutes your production tables.
Flexibility and Transformation: You often need to map fields from your source data to different field names in ServiceNow, or even transform data values (e.g., “Active” in a CSV might need to become “true” in a boolean field). Import Sets, combined with Transform Maps, give you this power.
Preventing Duplicates: A crucial aspect of maintaining data integrity. Import Sets, using a concept called “coalescing,” help identify if an incoming record should update an existing record or create a new one.
Auditability and Troubleshooting: If something goes wrong during an import, you have a clear record of the original data in the Import Set table and logs detailing what happened during the transformation process. This makes troubleshooting much easier.
Recurring Imports and Integrations: Once you set up an Import Set and its associated processes, you can schedule it to run automatically, making it ideal for regular data synchronization with external systems like HR databases, CMDB discovery tools, or monitoring platforms.
Mass Updates and Migrations: Need to update thousands of user records or migrate historical data from an old system? Import Sets are the tool for the job, handling large volumes of data efficiently.

Without Import Sets, managing external data in ServiceNow would be a nightmare of manual entry, custom scripting, and endless data cleanup. They are truly foundational for any serious data management strategy on the platform.

Core Concepts: The Building Blocks of an Import Set Operation

To truly master Import Sets, you need to understand the key components that work together.

Data Sources

This is where your external data originates. A Data Source defines how ServiceNow will get the data. Common types include:

File: The most common and often easiest to start with. This includes CSV, Excel (XLS/XLSX), XML, and even JSON files. You upload these directly to ServiceNow or specify an attachment.
JDBC (Java Database Connectivity): Connects directly to an external database (e.g., SQL Server, Oracle, MySQL) to pull data using SQL queries. Great for larger, recurring imports from structured databases.
LDAP: Used primarily for importing user and group data from directory services like Active Directory.
SOAP/REST: For pulling data from web services or APIs. More advanced, but crucial for complex integrations.

For most initial learning and common use cases, you’ll be dealing with File type data sources. When setting up a file-based data source, you’ll specify the file format (CSV, Excel), the header row number, and potentially character encoding.

Import Sets (The Staging Area)

As discussed, this is the temporary table. When you “Load All Records” from a Data Source, the data gets dumped into an Import Set table. You’ll see records here exactly as they were in your source file, unformatted and untransformed. Each row in your source file becomes a record in the Import Set table, and each column becomes a field.

You can inspect the Import Set table (e.g., u_my_excel_import_12345) to verify that the raw data loaded correctly before proceeding with the transformation. This is a crucial sanity check.

Transform Maps

This is the brain of the operation. A Transform Map defines the relationships between the fields in your Import Set table (the source) and the fields in your target ServiceNow table (the destination).

Think of it as a detailed instruction manual: “The ‘Employee ID’ column in my CSV should map to the ‘User ID’ field in the sys_user table.”

When you create a Transform Map, you choose:

Source Table: Your Import Set table.
Target Table: The actual ServiceNow table where the data will ultimately reside (e.g., sys_user, cmdb_ci, incident).

Within the Transform Map, you create Field Maps. These maps specify how each source field corresponds to a target field. You can have:

Automatic Mapping: ServiceNow tries to match fields with identical names.
Manual Mapping: You drag and drop or manually select source and target fields.
Scripted Field Maps: For complex transformations where a simple one-to-one mapping isn’t enough. For example, concatenating first and last names into a “Full Name” field, or looking up a reference value.

Transform Scripts

Sometimes, simple field mapping isn’t enough. You might need to perform more complex logic during the transformation process. This is where Transform Scripts come in. These are server-side JavaScript scripts that run at specific points during the import.

Common Transform Script types include:

onStart: Runs once at the very beginning of the transformation, before any data processing starts. Useful for initializing variables, logging, or setting global conditions.
onBefore: Runs before an individual source row is transformed and inserted/updated into the target table. This is where you do most of your data manipulation, validation, or conditional logic for each record. For example, skipping a row if a certain field is empty or modifying a value.
onAfter: Runs after an individual source row has been transformed and inserted/updated into the target table. Useful for post-processing, like updating related records or sending notifications.
onComplete: Runs once at the very end of the entire transformation process. Good for cleanup, summary reporting, or triggering subsequent actions.

You have access to source (the current record in the Import Set table) and target (the current record in the target table being processed) objects within these scripts, allowing you to read and modify data.

Coalesce

This is a critical concept for preventing duplicate records and ensuring updates happen correctly. When you’re importing data, ServiceNow needs to know: “Is this incoming record something new, or is it an update to an existing record?”

Coalescing is the process of comparing a field (or multiple fields) in your source data to a field (or fields) in your target table. If a match is found, ServiceNow updates the existing record. If no match is found, it creates a new record.

You can coalesce on:

Single Field: Common for unique identifiers like user_name, email, serial_number. If source.u_email matches an existing target.email, update. Otherwise, create.
Multiple Fields: Used when a combination of fields uniquely identifies a record. For example, asset_tag and model_id might together uniquely identify a Configuration Item (CI).
Script: For complex coalescing logic that can’t be handled by simple field matching.

Without coalescing, every incoming record would create a new record in your target table, leading to massive duplication. Always think about your coalesce strategy when setting up a transform map!

Real-World Examples: Where Import Sets Shine

Let’s look at some practical scenarios where Import Sets are your best friend:

Onboarding New Employees: Your HR system exports a CSV file every Monday with new hires. You need to create user accounts in ServiceNow, populate their basic details (name, email, department, manager), and assign them to a default group. An Import Set with a scheduled data source and transform map ensures these users are ready in ServiceNow before they even walk through the door.
CMDB Population and Updates: You’ve run a network discovery tool, and it’s spit out a list of servers, databases, and network devices with their attributes (IP address, OS, CPU, memory). You can import this data into your cmdb_ci_server or cmdb_ci_network_device tables. Regular imports (e.g., weekly) can then update existing CIs with fresh data, using fields like serial_number or name for coalescing.
Software License Management: Your software asset management tool generates a report of all installed software, versions, and license keys. You can import this into a custom table in ServiceNow to track license compliance.
Bulk Incident/Request Updates: Imagine a critical outage affecting a specific service. You have 500 open incidents related to it, and you need to update their state, work_notes, and potentially reassign them to a different group once the service is restored. Instead of manually editing each one, you export the incident numbers, add the new data to a spreadsheet, and use an Import Set to perform a bulk update.
Department Reorganizations: The company is restructuring, and many users are moving departments. You get an updated list from HR. An Import Set, coalescing on user_name or email, can efficiently update the department field for hundreds or thousands of users in one go.

Practical Scenarios: A Walkthrough Approach

Let’s sketch out a couple of practical workflows without getting bogged down in every UI click, focusing on the logical steps.

Scenario 1: Importing Brand New Users from an HR CSV

Goal: Create new user records in sys_user from a CSV, mapping basic fields.

Prepare the Data Source:
- HR provides new_hires.csv with columns like FirstName, LastName, Email, EmployeeID, Department.
- In ServiceNow: Navigate to System Import Sets > Data Sources.
- Create a new Data Source record.
- Set “Type” to File.
- Choose CSV for “Format.”
- Attach your new_hires.csv file.
- Save the Data Source.
Load the Data into an Import Set:
- From your saved Data Source, click “Load All Records.”
- ServiceNow creates a new Import Set table (e.g., u_hr_new_hires_12345).
- Click “Create Transform Map” from the Import Set record.
Create the Transform Map:
- Name: Give it a descriptive name like “HR New Hires Import.”
- Source Table: This will automatically be your Import Set table (e.g., u_hr_new_hires_12345).
- Target Table: Select User [sys_user].
- Field Maps:
  - Click “Auto Map Matching Fields” – this will try to match u_email to email, u_firstname to first_name, etc.
  - Manually add mappings for any fields that didn’t auto-map (e.g., u_employeeid to employee_id).
  - Coalesce Strategy: Crucial here. Since these are new users, we don’t want to update existing ones. We can coalesce on a truly unique identifier if the HR system guarantees it, like u_employeeid against employee_id in sys_user. If u_email is guaranteed unique, use that. If you only want to create new records and not worry about updates, you might even choose not to coalesce, but that risks duplicates if the file is ever run again with the same data. For new users, a unique identifier as coalesce is generally a safe bet; if a user already exists (maybe they were added manually), it won’t create a duplicate.
Run the Transform:
- From the Transform Map record, click “Transform.”
- Monitor the Import Set Run History and check for errors or skipped rows.
- Verify the new users in the sys_user table.

Scenario 2: Updating Existing CI Details from a Discovery Tool Export

Goal: Update the OS and RAM for existing servers in cmdb_ci_server from an Excel file, creating new CIs if they don’t exist.

Prepare the Data Source:
- Discovery tool exports servers_update.xlsx with columns like HostName, SerialNumber, OperatingSystem, MemoryGB.
- In ServiceNow: Create a Data Source (Type: File, Format: Excel). Attach the servers_update.xlsx.
Load Data and Create Transform Map (similar to above).
- Load Records.
- Create Transform Map, pointing to Configuration Item [cmdb_ci_server] as the target.
Crucial: Coalesce for Updates!
- In the Transform Map, identify a unique identifier for your servers. This is often serial_number or a combination of name and manufacturer.
- Set the “Coalesce” checkbox to true for the field map that corresponds to your unique identifier (e.g., map u_serialnumber to serial_number and check “Coalesce”).
- If a record in the Import Set table has a serial_number that matches an existing serial_number in cmdb_ci_server, that existing record will be updated.
- If no match is found, a new cmdb_ci_server record will be created.
Field Mapping:
- Map u_operatingsystem to os.
- Map u_memorygb to ram.
- Consider a Transform Script (onBefore) if MemoryGB needs to be converted (e.g., if it’s in MB in the source and GB in ServiceNow, you’d divide by 1024).
Run and Verify:
- Run the Transform.
- Check the Import Set Run History. Look for “Inserted” records (new CIs) and “Updated” records (existing CIs).
- Verify the updates and new CIs in cmdb_ci_server.

Common Mistakes to Avoid

Even experienced pros can trip up here. Watch out for these:

Forgetting Coalesce (or coalescing on the wrong field): This is probably the number one mistake. Forgetting it means you’ll create duplicate records. Coalescing on a non-unique field means you might update the wrong records. Always double-check your coalesce strategy!
Not Testing in Dev/Test: Never run a new or significantly modified Import Set directly in production. Always test with a subset of data in a non-production instance first.
Poor Source Data Quality: Import Sets can clean data, but they aren’t magic. Garbage in, garbage out still applies. Work with the data source owner to get the cleanest data possible. Missing mandatory fields, incorrect data types, or inconsistent formatting in your source will cause errors.
Incorrect Data Type Mapping: Trying to map a string from your source to a reference field in ServiceNow without a proper lookup will fail. Same for booleans (true/false, 1/0, yes/no). Use transform scripts or proper reference field mapping.
Not Handling Reference Fields Correctly: If you’re importing a user and want to set their manager field (which is a reference to another user record), simply providing the manager’s name won’t work. You need to map to a unique identifier for the manager (like user_name or email) and tell the field map it’s a “Reference field” and specify which field in the referenced table (sys_user) to match on.
Over-reliance on Transform Scripts: While powerful, complex transform scripts can be harder to debug and maintain. Use them when necessary, but always consider if a simpler field map with a choice action or a coalesce strategy can achieve the same result.
Not Reviewing Import Set Logs: After an import, always check the “Import Set Run” and “Import Set Rows” related lists for errors, ignored rows, or warnings. They tell you exactly what happened.

Interview Questions Relevance

Import Sets are a common topic in ServiceNow interviews, especially for administrator and developer roles. Be prepared to discuss:

“Explain the end-to-end process of importing data into ServiceNow using Import Sets.”
“What is a Transform Map, and what role does it play?”
“What is coalescing, and why is it important? Can you give an example?”
“When would you use a Transform Script versus a simple field map?”
“What are the different types of Transform Scripts (onStart, onBefore, etc.), and when would you use each?”
“How do you handle data validation or transformation during an import?”
“What are some common challenges you might face when importing data, and how would you troubleshoot them?”
“How would you ensure data integrity and prevent duplicates when importing recurring data?”

Having a solid, practical understanding and being able to articulate real-world scenarios will make a great impression.

Career Opportunities

Mastering Import Sets directly impacts several career paths within the ServiceNow ecosystem:

ServiceNow Administrator: This is a core skill for daily operations. Admins frequently import users, manage CMDB updates, and handle bulk data changes. Your efficiency with Import Sets directly translates to how smoothly the platform runs.
ServiceNow Developer/Integrations Specialist: Developers will often build custom integrations that use Import Sets in the backend, especially for more complex scenarios involving scripting, external APIs, and scheduled data feeds.
ITSM/ITOM Consultant: When advising clients on ServiceNow implementations, consultants regularly deal with data migration from legacy systems. A deep understanding of Import Sets is vital for planning and executing successful data migrations.
Business Analyst: While not directly executing imports, BAs need to understand the capabilities and limitations of data import processes to accurately gather requirements and define data mapping rules with stakeholders.

It’s a foundational skill that opens doors to more advanced data management and integration tasks.

Best Practices for Seamless Imports

To ensure your Import Set operations are robust, reliable, and error-free, follow these best practices:

Always Prototype and Test: Develop and thoroughly test your data sources and transform maps in a non-production environment (development or test instance) with representative data. This cannot be stressed enough.
Clean Your Source Data: Wherever possible, work with the data source owners to clean, validate, and standardize data before it even reaches ServiceNow. The cleaner the source, the smoother the import.
Choose Your Coalesce Strategy Wisely: Spend time identifying the most reliable unique identifier(s) for your target table. If no single field is truly unique, use multiple fields for coalescing, or a script. This is key to preventing duplicates and ensuring correct updates.
Document Your Transform Maps: Add clear descriptions to your transform maps, explaining their purpose, the source of the data, and any complex logic or scripts involved. Future you (or your colleagues) will thank you.
Leverage Reference Field Mapping: When mapping to a reference field, don’t just map a display name. Map a unique identifier (like User ID for a User reference) and configure the reference field mapping to match on that identifier.
Error Handling in Transform Scripts: If you’re using transform scripts, include gs.log() statements for debugging and consider error handling logic (e.g., ignore = true; to skip problematic rows).
Monitor Import Set Runs: Regularly check the Import Set Run History and the “Import Set Rows” related list for skipped records or errors. Don’t just assume it worked perfectly.
Use Scheduled Imports for Recurring Tasks: Once a data source and transform map are stable, configure them for scheduled execution for routine data synchronization (e.g., daily user updates from HR).
Review Target Table Data Beforehand: Especially for initial large imports, perform a data audit on your target table to understand the current state and identify potential conflicts or existing duplicates before your import adds to the problem.
Keep Transform Maps Simple: Avoid overly complex transform scripts if simpler field maps or lookups can achieve the same result. Complexity breeds maintenance headaches.

Wrapping Up

ServiceNow Import Sets are a workhorse feature, vital for managing the flow of data into and within your instance. They provide the necessary structure and control to ensure your ServiceNow data remains clean, accurate, and up-to-date, reflecting the dynamic nature of your IT environment.

By understanding the core concepts – Data Sources, Import Sets, Transform Maps, Transform Scripts, and especially Coalesce – you’re equipped to tackle a vast array of data management challenges. It’s a skill that will not only make your life easier as a ServiceNow professional but also significantly enhance the reliability and value of the platform for your organization. So, go forth and tame those data beasts!