Glossary
Operational data
Quick answer
Records of how a business runs day to day, such as work orders, tickets, approvals and process logs.
Operational data shows sequences of actions and decisions, making it useful for training AI agents.
Why Operational data matters when licensing company data to AI
Operational data records how a business runs day to day, such as work orders, tickets, approvals and process logs. When it is licensed, the records are exported from the systems where they live, and the order of actions and decisions is kept intact. The prepared extract therefore shows how work moved from one stage to the next.
- 01Your work records
- 02Personal details removed
- 03You approve the release
- 04Licensed to AI developers
Related terms
Operational data vs transactional data vs analytical data: what is the difference?#
Transactional data is the narrowest of the three, and analytical data is a copy reshaped for reporting. Microsoft describes transactional data as records of business interactions, such as payments, orders, inventory movements and services delivered, that usually carry a time, some amounts and references to other records.
Analytical data lives in warehouses and OLAP systems built for reading rather than writing. Oracle notes that warehouse data should not change once loaded, because its purpose is to analyze what has occurred, and that it is refreshed on a schedule by bulk loads.
| Aspect | Transactional data | Operational data | Analytical data |
|---|---|---|---|
| What it records | Individual business transactions such as orders and payments | Transactions plus the surrounding work records | Cleaned, combined copies of operational records |
| Where it lives | OLTP databases behind business apps | Business applications and their databases | Data warehouses, marts and OLAP models |
| How it changes | Updated and appended constantly | Updated as work moves forward | Loaded in scheduled bulk batches, not edited in place |
| Typical workload | Heavy writes, moderate reads | Mixed, driven by daily work | Heavy reads, low writes |
| Structure | Highly normalized tables | Varies by system, often normalized | Modeled for analysis, often as star or snowflake schemas |
| History | Often only a recent window kept live | Depends on system settings and retention | Often kept for trend analysis |
What are common operational data sources?#
Operational data sources are the systems where staff do the work and the records are first created. Most mid-sized companies have several of the system types below; the examples are illustrative, not a complete list.
| System type | Example products | Typical operational records |
|---|---|---|
| Help desk | Zendesk, Freshdesk | Tickets, replies, status changes, resolutions |
| CRM | Salesforce, HubSpot | Accounts, opportunities, activities, stage changes |
| ERP and finance | NetSuite, SAP | Purchase orders, invoices, approvals, payments |
| Project and issue tracking | Jira, Asana | Issues, assignments, comments, transitions |
| Field service | ServiceTitan | Jobs, dispatches, visit notes, closeouts |
| Team communication | Slack, Microsoft Teams | Channel threads, requests, handoffs |
| Document and e-signature | SharePoint, DocuSign | Contracts, forms, signature and approval trails |
How do you check that an operational data source is complete?#
Check where older history went and what was changed on the way, because the live system and the warehouse copy rarely hold the same records. Microsoft notes a common practice of keeping only a recent window, such as the current fiscal year, in the transactional system and moving older history to a data mart or warehouse.
The warehouse copy is not a full substitute either. ETL pipelines usually filter, aggregate, join, clean and deduplicate records before loading, which can drop step-by-step detail.
- Confirm how many years of records the live system still holds and where older years were moved.
- Ask whether status changes and edits are kept as history or only the latest value.
- Check whether the warehouse copy was filtered, aggregated or deduplicated, and which fields it dropped.
- List attachments, comments and free text that live outside the main tables.
- Note retention or deletion settings that removed records on a schedule.
- When records are licensed, the company approves exactly what is released, once per deal, before anything is shared.
Sources for this section
- Microsoft defines transactional data as records of business interactions handled by OLTP systems, and notes the practice of keeping a recent window live and offloading history to a warehouse. Source
- OLAP databases are optimized for heavy reads and low writes, modeled and cleansed for analysis, often preserve history, and typically use star or snowflake schemas instead of traditional normalization. Source
- ETL transformations commonly filter, sort, aggregate, join, clean, deduplicate and validate data before loading. Source
- Oracle describes warehouse data as nonvolatile, meaning it should not change once entered, and refreshed by scheduled bulk ETL. Source
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