FieldKo is built on the Salesforce platform, which means all FieldKo data is stored in Salesforce objects (standard or custom). As a Salesforce Admin, you can use the Salesforce Data Loader (a free Salesforce tool for bulk data operations) to insert new FieldKo records, update existing ones, or export FieldKo data in bulk.
Data Loader treats FieldKo objects just like any other Salesforce object, so you can leverage standard Salesforce functionality to manage FieldKo’s custom objects (such as Visits, Tasks, Surveys, etc.). This article will guide you through:
Preparing your data and identifying FieldKo object/field API names
Inserting new FieldKo records using Data Loader
Updating existing FieldKo records using Data Loader
Exporting data from FieldKo objects using Data Loader
Handling errors and troubleshooting common issues
You can download the Salesforce Data Loader here: https://developer.salesforce.com/tools/data-loader
Before You Begin: FieldKo Object API Names and CSV Preparation
Identify API Names: FieldKo “Visits”, “Tasks”, “Surveys” and other custom entities are represented as Salesforce objects. To use Data Loader, you’ll need the API names of these objects and their fields. In Salesforce Setup, go to Object Manager, find the FieldKo object (e.g. Visit), and click it. In the object detail or Fields & Relationships section, you can find the object’s API Name (called Developer Name for custom objects) and a list of fields with their API names. For example, a custom object labeled "Visit" has an API name such as Visit__c, and a field labeled "Visit Date" might have API name Visit_Date__c. (All custom object and field API names end in __c.)
Prepare a CSV File: Use a spreadsheet editor to create a CSV file with your FieldKo data. Each column should correspond to a Salesforce field, and each row to a record. Make sure to include all required fields for the object (for example, many objects require a Name or Owner field). We recommend naming your CSV column headers exactly as the field API names. This way, Data Loader can auto-match them during import for easier mapping.
Ensure your data values are in the correct format for each field (e.g. dates in a supported format, picklist values match Salesforce options, and for lookup fields, use the related record’s Salesforce ID or an external ID). Save the CSV file in a location you can browse to from Data Loader.
Tip: If you're not sure which fields are required or what the API names are, check the object’s detail page and the Fields & Relationships list in Object Manager. The Field Name column shows the API name next to each Field Label. Also, be aware of any FieldKo-specific business rules (like validation rules) that might reject records unless certain criteria are met.
Now that your data is ready, let's walk through using Data Loader for various tasks.
Inserting New FieldKo Records with Data Loader
Use Data Loader’s Insert function to add new records (e.g. new Visits, Tasks, or Surveys) in bulk. Here are the steps:
Launch Data Loader and log in: Open the Salesforce Data Loader application on your computer. Click Insert (this starts the insert wizard). When prompted, enter your Salesforce username and password, then click Log in. (If your org requires a security token for API logins, append your security token to your password when logging in.) After successful login, click Next.
Select the FieldKo object: In the next screen, choose the Salesforce object you want to insert records into. For example, select Visit if you are importing new Visit records. If you don’t see your object in the list by default, tick the “Show all Salesforce objects” checkbox to display all custom objects. Objects are listed by their label, with the API name in parentheses – e.g. you might see “Visit (Visit__c)”. Highlight the correct object and click Next.
Choose your CSV file: Click Browse... and locate the CSV file you prepared with the new records. Select the file and click Next. Data Loader will detect the columns from your CSV.
Map CSV columns to Salesforce fields: In the mapping step, you need to align your CSV columns with Salesforce fields. If you used API names as headers, simply click Auto-Match Fields to Columns, and Data Loader will automatically match columns to fields based on name similarity. Check the results and ensure all needed fields are matched. If any columns are not matched, or if you used different header names, you can manually drag the field from the list of Salesforce fields at the top down to the corresponding CSV column name at the bottom. For example, drag Visit Date from the Salesforce field list to the "Visit Date" CSV column. When all columns are mapped correctly, click Next to continue.
Begin the insert and confirm: Choose a directory on your computer to save the output files (Data Loader will ask where to store the results files, such as on your Desktop). Then click Finish, and confirm Yes when asked to proceed with the insert operation. The Data Loader will start importing the records. A progress window will show the status of the upload, and once completed, you’ll get a confirmation message.
Review the results: After the insert completes, Data Loader will generate two log files: a success file and an error file. The success file (CSV) contains all records that were inserted successfully, including their new Salesforce Record IDs. The error file (CSV) contains any records that failed to insert, with a message explaining why each one failed. Click View Successes to see inserted records or View Errors to see any failures (you can also open the CSV files manually).
Handle any errors: If some records were not inserted, review the error messages in the error file to determine the cause. Common issues include missing required fields or invalid field values (for example, a date formatted incorrectly, or a picklist value that doesn’t exist in Salesforce). Fix the data in your CSV for those error rows, save a new CSV with just the failed records (or you can use the error file itself, which contains the failed rows), and run the Insert again on those records. Data Loader will add them if the issues are resolved. Always verify that all intended records are now created in Salesforce.
Updating Existing FieldKo Records with Data Loader
To bulk-update FieldKo records (modify data in existing records), use the Data Loader Update operation. Unlike inserts, updates require knowing which specific records to change – typically by using each record’s Salesforce ID. Follow these steps to safely update FieldKo object records:
Export the records to update (with IDs): Before making updates, it’s wise to retrieve the current data and IDs of the records you plan to change. You can do this by running a Data Loader export (explained in the next section) or a Salesforce report. Ensure the export includes the Record ID field for each record. For example, if you need to update 100 Visit records, export those 100 records with their Ids and current relevant field values. This CSV will serve as your starting point for updates and also as a backup of the original data.
Make a backup copy: Save a copy of the exported CSV file as a backup (preserve it in case you need to revert changes). Do not skip this step – having a backup ensures you can restore the original values if something goes wrong.
Modify the data in the CSV: Open the working copy of your CSV (not the backup copy) in Excel or a text editor. Update the field values that need to be changed. For instance, you might change the status of some Tasks, or correct some Survey responses. Do not delete or alter the "Id" column – each row’s Id is the unique identifier that Salesforce uses to find the record to update. You can remove rows for any records you don’t want to update, but make sure every row you keep has a valid Id. Once you’ve made your changes, save the CSV file.
Launch Data Loader and select Update: Open Data Loader and click Update this time (rather than Insert). Log in with your Salesforce credentials if not already logged in. Choose the FieldKo object that you are updating (e.g. Visit or Task). Again, use "Show all Salesforce objects" if you don’t see the custom object in the list. Click Next.
Select the updated CSV file: Click Browse... and locate the CSV file you just edited with the new values. After selecting the file, click Next. If prompted, confirm the object and file selection.
Map fields, including IDs: As with inserts, map your CSV columns to the Salesforce fields. If your CSV has the column header "Id", Data Loader should auto-match it to the Salesforce record ID field. Verify that Id is mapped to Id (the record ID) in the field mapping screen. Map all other fields you changed to their corresponding FieldKo fields. You can use Auto-Match to speed this up, and manually drag any unmapped fields as needed. Once all columns (including Id and the fields to update) are mapped, click Next.
Run the update and review results: Choose a directory for the success and error logs, then click Finish and confirm Yes to begin the update. Data Loader will process each row, matching by the Id value to update the existing record. When finished, check the success and error files. The success file will list records that updated successfully, and the error file will list any records that failed with error messages. Review these results carefully:
If all records were updated successfully (no error rows), you’re done! Spot-check a few records in Salesforce to ensure the changes appear as expected.
If some updates failed, open the error CSV to see why. You might encounter errors like “REQUIRED_FIELD_MISSING” (if a required field was blank in your data) or “INVALID_VALUE” (if a value didn’t meet validation rules or picklist values). Fix the data for those records in the CSV. You can use the error file itself (it contains the failed rows and their Ids) to correct the data.
After fixing, you can re-run the Data Loader update for just those failed rows. Alternatively, since you have the backup of original data, you could restore the original values for failed records if needed by doing another update using the backup file (to undo changes). This is rarely needed but good to know.
Verify and clean up: Once all intended records are updated, verify in the FieldKo app (or Salesforce UI) that the changes are reflected. If you used a backup file and everything looks good, keep the backup for a while (in case further issues are found) or securely archive it. You may delete any temporary CSV files (like partial error CSVs) once you’re confident the updates are correct.
Note: If you have an external ID on a FieldKo object (a field marked as External ID), you could use the Upsert operation instead of Update. Upsert allows you to match existing records by an external ID field or create new records if no match is found. This is advanced usage; for straightforward updates, using the record Id with the Update operation as above is the simplest approach.
Exporting FieldKo Data with Data Loader
Data Loader isn’t just for importing data – you can also use it to export data from Salesforce objects, including FieldKo objects. Exporting is useful for data backups, reporting in Excel, or preparing data for updates. Follow these steps to export FieldKo data:
Launch Data Loader and choose Export: Open the Data Loader application and click Export. (If you want to include records that have been deleted and are in the Recycle Bin, choose Export All instead. Export All includes “soft-deleted” records.) Click Next and log in with your Salesforce credentials if prompted.
Select the object to export: Select the FieldKo object you want to extract data from, such as Survey, Visit, or Task. If it’s not showing in the default list, mark Show all Salesforce objects to see the full list of objects and select it from there. Click Next.
Specify the target file for export: Click Browse... to choose a location and filename for the export CSV file. For example, you might name it Visits_Export.csv. If you pick an existing file, be aware that Data Loader will overwrite its contents with the new export data. Once you’ve set the file path, click Next.
Build a SOQL query or select fields: The Data Loader will then prompt you to define a query to retrieve the data. You have two options here:
Use the query builder: You’ll see a list of available fields for the object. Tick the checkboxes for each field you want to include in the export (e.g., Id, Name, Date__c, Status__c, etc.). You can also set conditions (filters) if you only want specific records (for example, “Status = Completed”). If you leave the criteria blank, all records you have access to will be exported.
Write a SOQL query (optional): If you are comfortable with Salesforce Object Query Language, you can manually write a query in the text box. For example:
SELECT Id, Name, Status__c FROM Visit__c WHERE Status__c = 'Open'. The query builder will actually construct a SOQL query for you based on the fields and filters you select, so writing SOQL by hand isn’t necessary for most admins.
After selecting the fields (and any filters), click Finish to run the export.
Save and review the exported data: Data Loader will save the results to the CSV file you specified. You’ll get a confirmation of how many records were exported. Open the CSV file with Excel or another editor to review the data. You should see the column headers for each field you selected and a row for each exported record. (Unlike imports, a pure export operation doesn’t produce separate success/error files – it simply outputs the data. If the export fails for some reason, you would receive an error message in Data Loader, but under normal circumstances you will just get the CSV of results.)
Use cases for export: Exporting FieldKo object data can help with reporting or backups. For instance, you might export all Survey results to analyze them in Excel, or export Visit records before making mass changes (so you have a point-in-time snapshot or to use that CSV for the update process as described earlier). Remember that Data Loader exports only data you have permission to see (it respects Salesforce security), so ensure your user has Read access on the object and fields you need.
Handling Errors and Troubleshooting Tips
Working with Data Loader is powerful, but you may encounter errors or issues. Here’s how to handle common scenarios:
Understanding Data Loader error files: As mentioned, whenever you insert or update data, Data Loader generates an error file listing any failed records and the reason for failure. Always read the error messages – they are usually very descriptive. For example, an error might say “REQUIRED_FIELD_MISSING: Required fields are missing: [Name]”, indicating your CSV didn’t provide a value for a field called Name that is required. Another common error is “INVALID_FIELD_FOR_INSERT_UPDATE” which can mean a field in your CSV doesn’t exist or isn’t writeable (check that you used the correct API name and that the field is editable). By understanding these messages, you can quickly adjust your data and re-run the operation for the failed rows.
Missing required fields: Every Salesforce object (including FieldKo’s) may have certain fields that must be filled in on insert/update. If these aren’t in your CSV or are blank, the operation will fail for those records. Ensure you include values for all required fields. If you’re not sure which are required, check the object in Object Manager (required fields often have a red asterisk in the UI and are marked as Required in field settings).
Picklist or data format issues: If a picklist value in your CSV isn’t one of the allowed values in Salesforce, you’ll get an error. Use consistent values or add the value to the picklist in Salesforce beforehand. Similarly, date/time fields must be in an acceptable format (e.g.
YYYY-MM-DDor your locale format) or you could see errors. Salesforce Data Loader supports multiple date formats – for example,2025-04-22or22/04/2025are typically accepted as dates. If you see a format error, adjust the format in the CSV.Lookup relationship errors: FieldKo objects might have lookup fields (relationships) to other objects (e.g., a Visit might link to an Account or a User). When importing, those lookup fields need to reference an existing record’s ID (or an external ID). A common error is “INVALID_CROSS_REFERENCE_KEY: invalid id”, which means the ID you provided for a lookup field isn’t valid or doesn’t exist in that target object. To avoid this, double-check that you have the correct 15- or 18-character Salesforce ID for any lookup fields. If you only know a related record’s name, consider doing an export of that object to get the IDs, or use VLOOKUP in Excel to match names to IDs from a prior export.
Validation rules and triggers: Sometimes Salesforce has business logic (validation rules, Apex triggers, etc.) that can block a record from being saved if certain criteria aren’t met. If a FieldKo object has such rules, Data Loader will report the error message that the rule or trigger produces. For example, a validation rule might say an error like "Survey must have at least one question". If you hit a validation rule, you have two choices: update your data to satisfy the rule’s conditions, or temporarily deactivate that rule (not usually recommended in production without careful consideration) and re-run the load. Typically, it's best to adjust the data to meet the requirements.
Permissions issues: If Data Loader’s error file shows errors about permissions (e.g., "INSUFFICIENT_ACCESS: not able to access object"), it means the user account you used to log in doesn’t have the needed Salesforce permissions on that object or field. Ensure your profile or permission sets allow you to read and modify the FieldKo object and fields in question. As a Salesforce Admin, you likely have the access, but if running under an integration user account, permissions might need to be adjusted.
Data Loader login and connection issues: If you have trouble logging in to Data Loader (for example, login fails due to IP restrictions), you might need to add your IP to trusted IP ranges or use a security token as mentioned earlier. Also, ensure you’re using the correct API version of Data Loader for your Salesforce instance (generally the latest version is fine). If Data Loader seems to hang or crash on a large file, consider splitting the CSV into smaller batches. Salesforce also has Bulk API settings in Data Loader (in Settings you can enable Bulk API) which are better for very large data loads, but for most admin tasks the default settings work well.
Review logs and success files: For every operation, don’t forget to review the success file as well – it can be useful. For inserts, the success file will give you the new record IDs, which you might need if you plan to reference those records elsewhere. For updates, the success file simply confirms which records were updated (it usually repeats the Id and maybe some fields). You can archive these logs for audit purposes if needed.
In summary, Salesforce Data Loader is a powerful ally for managing FieldKo data in bulk. By understanding FieldKo’s object and field API names and preparing your CSV data carefully, you can insert new records, update existing ones, and export data with confidence. And if errors occur, the tool provides clear feedback so you can fix the issues.
FieldKo being native to Salesforce means you don’t need any special data tools beyond what you already use as a Salesforce Admin – Data Loader treats FieldKo objects just like any standard or custom object in Salesforce.