IFS Cloud and Tableau integration

What moves between them

Data flows from IFS Cloud into Tableau datasources on a scheduled basis. ml-connector periodically polls IFS Cloud for new or updated purchase orders, supplier invoices, GL accounts, vouchers, and sales orders using OData filters on modified timestamps, then pushes the extracted records into Tableau datasources as CSV or API mutations. The schedule is configurable (daily, weekly, or tied to a business calendar such as month-end close). Reference data such as suppliers and customers are synced first so dimension tables in Tableau are current. GL postings flow continuously so financial dashboards reflect the latest actuals.

How ml-connector handles it

ml-connector stores IFS Cloud OAuth credentials and Tableau PAT encrypted, refreshes the IFS bearer token before expiry, and uses a company-code-scoped filter on every IFS OData query to isolate the correct financial entity. ETag tracking prevents re-posting identical GL records to Tableau. IFS OData page size is kept under 5000 elements to avoid server errors. On the Tableau side, ml-connector obtains a session token from the PAT, then either appends new records to an existing datasource via API or creates a new CSV extract, depending on datasource type and refresh requirements. IFS Event Actions (server-side triggers) can notify ml-connector of high-priority financial events, but registration is manual per customer; the integration does not depend on them. Retries on HTTP 429 (IFS rate limit) use exponential backoff with jitter. All records carry audit timestamps and can be replayed if a Tableau refresh fails.

A real-world example

A mid-sized discrete manufacturer uses IFS Cloud ERP for procurement, supply chain, and finance across multiple plants. Finance and supply chain teams rely on Tableau dashboards for accruals forecasting, PO aging, invoice status, and labor cost allocation, but Tableau extract refreshes are manual and often lag by hours or days. With IFS Cloud and Tableau connected, critical operational data (new POs, matched invoices, GL vouchers, supplier master) refreshes automatically on a daily schedule. Finance gains current accruals visibility, supply chain sees real-time PO status, and the monthly close cycle starts with current data already in place instead of waiting for manual extracts.

What you can do

• •Extract purchase orders, supplier invoices, GL accounts, and vouchers from IFS Cloud and load them into Tableau datasources on a defined schedule.
• •Automatically refresh Tableau datasources when IFS financial or operational data changes, keeping dashboards current without manual intervention.
• •Map IFS company codes and accounting dimensions to Tableau project and datasource structure, isolating data correctly for multi-entity scenarios.
• •Authenticate to IFS Cloud with OAuth 2.0 client credentials and tenant-specific URLs, and to Tableau with Personal Access Token session exchange.
• •Poll IFS Cloud on schedule with ETag concurrency control and OData pagination, respecting rate limits with exponential backoff and a full audit trail.

Questions

Which direction does data move between IFS Cloud and Tableau?

Data flows from IFS Cloud into Tableau datasources. ml-connector extracts purchase orders, invoices, GL postings, and reference data from IFS and loads them into Tableau datasources via API or CSV refresh. Tableau is read-mostly, so no data is written back to IFS from Tableau.

How does the integration handle IFS Cloud's pull-only model and lack of standard webhooks?

ml-connector polls IFS Cloud on a schedule defined by the business calendar (daily, weekly, or triggered by month-end events) rather than relying on a push. IFS Event Actions can optionally notify ml-connector of high-priority financial events if manually configured by the customer, but the core integration does not depend on them. OData filters on modified timestamps minimize data transferred per poll.

How are company codes and dimensions managed in this integration?

IFS Cloud requires a company code on most financial entities. ml-connector accepts company-code configuration from the customer at setup time and applies a company-code filter to every OData query, ensuring only the correct legal entity's data is extracted. Accounting dimensions (such as cost centers) are synced to Tableau first, so GL vouchers land on valid dimension combinations when loaded.