EPM platforms are only as good as the data they consume. Every budget, forecast and variance report depends on actuals flowing from ERPs, headcount from HRIS, pipeline from CRM and operational data from dozens of other sources. How that data gets into the planning environment — and whether it arrives clean, timely and correctly mapped — determines whether the system delivers value or frustration.
This guide covers common data sources, integration patterns, ETL fundamentals, refresh cadences, staging layers and best practices for building reliable data pipelines into EPM.
Common Data Sources
ERP (actuals)
General ledger balances, trial balance data, journal entries. The most critical integration — actuals are the foundation of variance analysis and forecast accuracy measurement.
HRIS (headcount)
Employee records, compensation, department mapping, start/end dates. Feeds workforce planning and is the largest cost driver for most organizations.
CRM (pipeline)
Opportunity data, stage progression, close dates, deal values. Feeds revenue forecasting and sales capacity planning.
Billing and revenue systems
Invoice data, subscription metrics, ARR/MRR, churn. Feeds detailed revenue models and SaaS metrics.
Spreadsheets and offline data
Assumptions, targets, one-time adjustments and data that does not live in any system. Every EPM implementation has some manual data entry.
Integration Patterns
Direct API
EPM platform connects directly to source system APIs. Simplest for single-source environments. Limited transformation capability.
Pre-built connectors
Vendor-provided integrations for common systems (NetSuite, SAP, Salesforce). Faster to deploy but may not cover all data requirements.
ETL / iPaaS middleware
Tools like Fivetran, Workato or custom ETL scripts extract, transform and load data. Most flexible but adds another system to manage.
Data warehouse
Source data lands in a warehouse (Snowflake, BigQuery) first. EPM reads from the warehouse. Best for complex, multi-source environments.
Best Practices
•Map your chart of accounts before building integrations
•Validate data at the source — do not clean it in the EPM tool
•Build reconciliation checks between source and destination
•Document transformation logic so it is not locked in one person's head
•Plan for exceptions — failed loads, changed schemas, new accounts
•Separate actuals from plan data in your integration architecture