Large organizations often have data spread across multiple systems and sites, including operational details and customer information. Managing distributed, siloed data can allow errors to creep in from duplications to inconsistencies. Reliable analytics and decisions require accurate, unified data. To enable vital functions like sales, manufacturing, and customer support, organizations need robust data reconciliation to deliver trusted, integrated information across the business.

What is Data Reconciliation and How Does It Work?

Data reconciliation is the process of validating data accuracy by comparing information from multiple sources. It plays a critical role across industries, from finance and accounting to manufacturing, energy systems, and IT.

At its core, data reconciliations look for discrepancies across data sets via a data management service. It then investigates the inconsistencies drawing on supplementary knowledge to provide context. The goal is to resolve differences to ensure synchronized, complete, and trustworthy data. This creates a unified view of information across the organization, enabling confident decision-making and planning.

When Do Companies Need Data Reconciliations?

Regardless of how data is structured across systems, divisions, or migrations – inaccuracies sneak in. Reconciliation identifies missing, duplicated, incorrect, or formatting errors in 3 pivotal cases:

Ensuring Accuracy When Transferring Data

Data reconciliations enable organizations to identify and remedy omissions or errors that occur during data migrations between systems. This is achieved by validating that the data contained in the legacy source system matches the data in the new target system, except for deviations accounted for by predefined transformation rules set up by data governance vs data management processes.

Various technologies and approaches verify consistency post-migration to the new platform. These often validate that aggregate metrics like total columns, rows, and annual sales volumes are consistent between the source and the target. Additionally, checksums and other comparison methods inspect small batch data transfers to expose discrepancies between the systems.

Regular Data Quality and Consistency Checks

A secondary pivotal application of reconciliation constitutes continuous enterprise-wide data integrity audits done through data analytics and integration.

Checks run periodically to flag inconsistencies across systems and business units needing cleaning and validation. This ensures synchronization of customer details between CRM and billing systems, matching product pricing in catalogs and e-commerce, etc. 

Reconciliation often entails comparisons against a verified “gold standard” reference data set.

Importantly, these business-as-usual checks must run continuously since data changes often, allowing errors to creep in. Reconciliation also helps enormously with mergers, acquisitions, and complex legacy system integrations using techniques like gross error detection to detect systemic data issues.

Advanced Financial Services Needs

Reconciling financial data needs custom features to handle changing economic factors like inflation, currency value differences compared to the original transaction amount and acceptable margins for rounding errors based on a well-defined data quality testing strategy.

So extra functionality beyond just matching data across systems is required. Additional business rules and math formulas need to be added to account for all the external variables. This way the data is formatted cleanly for monthly, quarterly, and yearly financial reporting. Doing this normalization enables easy reporting and analytics.

Common Challenges in Data Reconciliation

Many organizations create custom data-matching tools using complicated math formulas in SQL or similar programming languages. However, building these in-house is costly and time-intensive. It also means business leaders need technical help to understand data differences or quality problems.

Instead, companies can buy ready-made reconciliation software. However, these often demand additional coding work, along with extensive computing power and memory to function right. This makes some options unrealistic for certain needs and budgets. Using these off-the-shelf tools can lead to four main issues:

  • No single source of truth – With data copies spread across systems, businesses can’t tell which version is current and correct.
  • Expensive and complex to build – In-house tools typically require big investments and strong technical skills.
  • Limited insights – Custom solutions are hard for non-technical users to access and understand.
  • Disconnected data – When systems and data are siloed, inaccuracies and data quality inconsistencies creep in.

Best Practices of Data Reconciliation to Overcome the Challenges

Here are some of the common challenges in data reconciliations and ways to overcome them:

Data in Different Formats

It can be difficult to reconcile data from multiple sources that use different data formats, structures, naming conventions, etc. Using data integration tools that can map different schemas to a common data model helps overcome this.

Very Large Data Volumes

Reconciling billions of records between systems manually is infeasible. Leveraging big data pipelines and frameworks like Apache Spark helps process large data volumes efficiently for comparison.

Disparate Systems

When data resides on different systems like ERP, CRMs, and payment gateways, the integration complexity grows. Using middleware allows these disparate systems to speak to one another for reliable data sharing.

Weak Change Data Capture

If changes happening across systems are not reliably captured, identifying data differences becomes error-prone. Maintaining robust CDC processes via timestamping, deltas, audit logs, etc improves reconciliation.

Legacy Systems

Outdated systems often lack APIs/connectivity necessitating manual data extractions. This slows down the process. Implementing lightweight connectors or APIs exposes the data for seamless migration to target warehouses.

Limited Transparency

Blackbox ETL pipelines mask data issues. Incorporating checks at each transformation stage to capture anomalies allows early corrections preventing large downstream impacts.

Investing in scalable integration frameworks minimizes these data reconciliations struggles by enabling transparent data lineage across systems and pipelines. Ongoing monitoring also ensures quality by design.

Why Choose Intone?

Fully accurate data reconciliations can be challenging due to complex data, quality issues, or technique limitations. However, organizations aim to maximize precision by employing rigorous methods and validation. Exploring partnerships with expert data reconciliation services brings major benefits. These include resource and cost-savings and access to experienced, trained specialists for your exact needs.

To uncover the hidden potential in your data faster, Intone offers customized data advisory solutions. IntoneSwfit is a tool that can assist you in achieving all of your necessities. It offers:

  • Knowledge graph for all data integrations done
  • 600+ Data, and Application and device connectors
  • A graphical no-code low-code platform.
  • Distributed In-memory operations that give 10X speed in data operations.
  • Attribute level lineage capturing at every data integration map
  • Data encryption at every stage
  • Centralized password and connection management
  • Real-time, streaming & batch processing of data
  • Supports unlimited heterogeneous data source combinations
  • Eye-catching monitoring module that gives real-time updates

Contact us to learn more about how we can help you!