JSON Export: Unlock Deeper Insights in Your Job Data

1. What is JSON Export?

JSON Export allows IronSight Admins to export a complete, structured dataset of jobs directly from the Jobs list.

Unlike CSV exports, JSON export preserves nested and relational data, allowing you to analyze information such as:

• Form submissions and responses
• Custom fields and metadata
• Line items
• Approval workflow timestamps
• Field workers and resources
• Stops and inventory movements

This format enables deeper analysis using tools such as:

• Business intelligence tools
• Large Language Models (LLMs) like ChatGPT Claude or Microsoft Copilot
• Data warehouses
• Internal analytics scripts

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2. How to use JSON Export

Step 1 — Select export format

Click Export and choose:

• JSON (recommended for advance analytics and automation)
• CSV (for operational reporting)

Step 1 — Configure your job filters

Configure the Jobs list filters the same way you would while searching for jobs. Your export will show the same jobs, including:

• Job group
  
  
  
  
• Date range
  
  
  
  
• Job filters
  
  
• Sort order
  
  

Step 3 — Generate the export

Click on the "Export Jobs Button"

After clicking Export:

• IronSight generates a file containing jobs that match your current filters (up to 20,000 jobs per export for both JSON and CSV).
  
  
• Export processing usually takes ~10 seconds, but in rare cases, large datasets may take up to 1 minute. While the export is generating, keep this tab open to ensure the download completes.
• The file will automatically download to your computer when ready.

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3. Understanding the JSON data structure

Each exported job includes multiple fields describing the job, scheduling, operational data, stops, forms and custom fields.

Job Information

 

Scheduling & Execution

Field	Description
scheduled_start_time	Planned job start time
scheduled_end_time	Planned job end time
activated_time	When the job started
completed_time	When the job was completed
duration: active_hrs, paused_hrs	Time actively spent on the job
paused_duration_hrs	Time the job was paused
distance	Travel distance recorded for the job

Operational Context

Field	Description
resource_type	Type of assigned resource
activity	Job activity type
division	Division responsible for the job
hub	Hub or operational location
assigned_resource	Assigned vehicle or resource
field_workers	Workers assigned to the job

Stops & Field Operations

Field	Description
stops: type, location, requested_inventory, actual:date, inventory_receipts	Actual stops completed during the job. Includes type & location of a job, requested inventory, date and inventory receipts

Forms & Custom Data

Field	Description
forms type:job_form type:adhoc	Forms submitted during the job Adhoc forms that only Service Providers have access to
custom_data	Custom fields or additional job metadata

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4. Prompts you can use to analyze JSON Export data

You can upload your JSON export to an LLM tool (such as ChatGPT, Claude, or Microsoft Copilot), write a simple prompt, and instantly generate insights or key KPIs from your job data.

Below are example prompts to help you get started.

Operational performance analysis

Analyze this IronSight jobs dataset and calculate:

1. Average job duration

2. Average delay between scheduled_start_time and activated_time

3. Top 5 resources completing the most jobs

4. Jobs that exceeded 4 hours of active_duration_hrs

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Approval workflow analysis

Using the fields submitted_for_approval_time and approval_time:

1. Calculate average approval time

2. Identify the longest approval delays

3. Show which divisions have the slowest approval cycles

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Resource productivity

From this dataset:

1. Calculate jobs completed per resource

2. Identify resources with the highest average completion time

3. Rank resources by total completed volume

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SLA compliance analysis

Analyze completion time and response time:

1. Calculate the time between created_time and activated_time

2. Identify jobs that exceeded a 2 hours response time

3. Identify trends by activity or division

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Form and compliance analysis

Analyze form submission data:

1. List all forms submitted for completed jobs

2. Identify jobs missing required forms

3. Summarize key form responses

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Advanced analytics prompt

Act as a data analyst. Using this IronSight job dataset:

- identify operational bottlenecks

- calculate performance metrics

- recommend improvements to scheduling, approvals, or resource allocation

Tips

For best results, ask the LLM to:

• Treat each JSON object as a job record
• Use timestamps to calculate durations
• Aggregate results by resource, division, or activity

⚠️ Important

Insights generated by AI tools may not always be accurate. We recommend reviewing the results and confirming them with the underlying data before making operational or business decisions.