What is data integration?
Most finance teams do not have a data problem. They have a data fragmentation problem. The information they need exists, but it lives in too many places to be useful in real time. That is the problem data integration solves.
Summary
- Data integration connects disparate systems into a unified data flow, giving finance teams a single, reliable source of truth rather than a patchwork of disconnected exports.
- The scale of the problem is sizeable. The Stripe CFO Insights Report (1,700+ finance leaders) found that 63% use 10 or more systems just to get a unified view of their company’s financials.
- Finance teams waste countless hours every month manually reconciling data and fixing errors across systems.
- The underlying mechanism for most financial data integration is: extract, transform, load, a three-stage pipeline that pulls, standardises, and delivers data automatically.
- Common finance use cases include reconciliation automation, regulatory reporting, and real-time cash visibility across banking and ERP platforms.
- Data automation and integration work together: integration connects the systems; automation determines what happens with the data once it flows through.
What is data integration in finance?
Data integration is the process of combining data from multiple source systems into a unified, consistent dataset for reporting, analysis, and automated processing. In finance, those sources typically include ERP platforms, bank feeds, payment processors, sub-ledgers, and third-party providers. The Stripe CFO Insights Report found that 45% of finance teams spend more than 10 hours every month manually reconciling data across these disconnected systems. Data integration eliminates that overhead by automating the connections.
A practical use case: a business with SAP as its ERP, three banking partners, and a payment processor needs to reconcile its cash position daily. Without integration, this requires manual exports, format normalisation, and manual matching. With data integration automated, data flows without manual intervention into a central pipeline, is standardised on ingestion, and matched continuously.
How does data integration work?
The core process is ETL: extract, transform, load. For a detailed breakdown, see our guide: What is data integration in ETL? In brief: extraction pulls data from source systems via API or file transfer; transformation cleans and standardises it so records from different systems can be compared; loading delivers processed data to a data warehouse, reconciliation engine, or ERP. Modern platforms like Aurum also support ELT, where raw data is loaded into a cloud warehouse first and transformed there, which suits organisations that need flexibility for ad-hoc analysis.
The benefits of using data integration in your automation stack
- Single source of truth. Every team works from the same validated dataset, eliminating the version discrepancies that arise when data is consolidated manually.
- Automated reconciliation. Integration is the prerequisite for reconciliation automation. Without connected data flows, matching logic has nothing reliable to work with.
- Real-time visibility. Integrated systems deliver a live view of cash position, outstanding payables, and receivables without waiting for a period-end report.
- Scalability. Adding a new bank or payment processor becomes a configuration task rather than a manual process redesign, handling growing volumes without proportional increases in team effort.
Examples of data integration software
- Enterprise platforms: MuleSoft, Dell Boomi, and Microsoft Azure Data Factory provide broad connectivity across complex stacks but require significant technical resource.
- Cloud-native ETL tools: AWS Glue, Informatica, Fivetran, and Airbyte are built for modern cloud architectures with managed connectors to common SaaS and data warehouse platforms.
- Finance-specific integration: Aurum is designed for the data structures, reconciliation requirements, and compliance needs of finance teams. It connects to bank feeds, ERP platforms, and payment processors out of the box. Data integration with ETL sits at the core of Aurum’s approach.
Finance teams that need rapid time-to-value are typically better served by a finance-specific platform than an enterprise tool that requires months of configuration before producing results.
Data integration use cases
Multi-bank cash reconciliation
A business with five banking relationships across three currencies consolidates all feeds through a single integration pipeline, normalises currencies and references, and matches transactions against the ledger automatically, replacing five separate manual bank exports each morning.
Regulatory reporting
Regulated firms submit data to the FCA, PRA, and HMRC in prescribed formats. Data integration pulls required fields from multiple source systems, applies submission formatting rules, and generates reporting output automatically with a queryable audit trail.
ERP and payment processor reconciliation
Payment processors settle in batches that rarely map directly to individual ERP orders. Integration maps settlement files to internal records, handles timing differences, and flags exceptions, without the manual matching that extends the close cycle.
Data integration is the infrastructure layer that makes everything else possible. Reconciliation automation, real-time reporting, AI-powered anomaly detection: none of it works reliably if the data coming in is fragmented or inconsistent. Getting the integration right is not a technical detail, it is a strategic decision.
Joao Saraiva
Lead Consultant / Delivery Manager at Aurum Solutions
Aurum supporting accounting teams with data integration
Aurum connects to ERP systems, bank feeds, payment processors, and sub-ledgers via API, applying consistent transformation and matching logic across every source. Book a demo to see how data integration can reduce manual workload and strengthen your financial operations.
Data integration FAQs
How is AI used in data integration?
AI improves data integration by handling records that do not meet exact-match criteria, assessing confidence across multiple fields simultaneously to identify likely matches. It also flags anomalies and inconsistencies in incoming data before they reach downstream reports, and learns from human corrections over time to improve match rates without manual rule updates.
What is data orchestration?
Data orchestration is the coordination layer that sequences integration workflows: determining when each pipeline runs, in what order, and what happens when a step fails. It ensures bank feeds are ingested before reconciliation runs, currency conversion is applied before consolidation, and exceptions are routed correctly when automated processing cannot resolve them.
What is ETL and how does it work?
ETL stands for extract, transform, load: the three-stage process by which data is pulled from source systems, standardised, and loaded into a destination for use. For a full guide, see What is data integration in ETL?.
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