When you hear the term “reconciliation”, you probably envision a process that matches data or “balances the books”. While this is true, reconciliation is much more complex. Reconciliation involves integrating data from different source systems into a central data warehouse, continuously counting and comparing it to contrasting records. Finding and implementing the best reconciliation tool is vital for accurate and timely reporting, minimizing risk exposure, and increasing business opportunities.
With today’s volumes of low-quality data being ingested and reconciled, firms can quickly become overwhelmed with manual processes, leading to additional risks, costs and resources. It may seem obvious that firms must abandon their traditional methods of reconciliation; however, many assume that implementing new technologies is costly and laborious. Therefore, these organizations continue using outdated methods or simply work to improve their current reconciliation models. As a result, they still struggle with low-quality data and experience significant operational challenges that affect their risk, operations, and overall bottom line.
Current reconciliation tools have a long list of limitations and challenges that prevent firms from unlocking business possibilities. Some of these include:
They require manual preparation and normalisation of data.
Onboarding of new data sets can be time-consuming, leading to long lead times (2-3 months) when onboarding new reconciliations. Today, it is imperative that data can be identified, prepared, and normalized on-demand with an integrated API-driven ETL layer.
They only perform bi-lateral matching.
Current reconciliation tools essentially only match pairs of records or two data sets to one another, which is both time-consuming and resource intensive for complex workflows. Data must be loaded multiple times, and results must be manually stitched together to create a complete list of exceptions for the entire workflow.
They only support batch processing.
Right now, firms must load their data multiple times, which is manual and costly. Batch processing can be an overnight process, preventing timely reconciliations. Additionally, there is little to no support for streaming data sources, resulting in delayed, and slow rules execution. Enriching datasets by merging multiple source datasets, including reference datasets, requires additional IT effort and resources.
They provide limited metrics on high-priority data quality errors.
Reconciliation metrics, while useful, are limited when determining and prioritizing the total business value impact of data quality issues. Firms must adjust their efforts to solve the most valuable and costly errors first.
They require significant IT involvement.
Most reconciliation tools require significant IT involvement to be built out, update, and maintain rules or processes. This results in lags and delays, with business users having to wait to push through updates or changes.
They are not scalable.
The majority of reconciliation solutions struggle with handling large volumes of data, especially when there are millions of records and rules to manage.
Having the right reconciliation tool will not only save you money, but it will also empower your firm, unlock business potential, and boost overall operational efficiency.
The PeerNova® Cuneiform® Reconciliation platform is a zero-code solution that enables business users to monitor data quality metrics, quickly address high-impact exceptions, and build lineage across internal and external systems. As an effective reconciliation tool, financial firms can streamline their root-cause analysis and exception management processes.
The solution offers seven reconciliation tool must-haves:
Multilateral (N-way) matching.
Cuneiform supports efficient multi-lateral matching that handles large and complex workflows without requiring multiple “passes”. Data is loaded just once, and the results are automatically collated to generate a consolidated list of exceptions for the entire workflow.
Stream processing, data preparation, and data enrichment.
The solution supports diverse batch and streaming data sources in real-time, with no preprocessing required. Users can upload and merge multiple datasets to create enriched derived datasets, as well as easily integrating reference data sources.
Business value impact metrics for exception prioritization.
Cuneiform Reconciliation automatically calculates a score for each data quality error, in addition to the total business impact of data quality issues across the entire workflow. These metrics can be customized based on any user-defined dimension, allowing firms to prioritize their exceptions.
Requires minimal IT effort.
Business users use Cuneiform’s self-serve, zero-code interface with drag-and-drop features to set up complex reconciliation and data quality rules. This allows for lowered cost and faster time-to-market for new controls.
Traceability and end-to-end visibility.
Through the platform’s Business Workflow Lineage™ feature, the solution accelerates root-cause analysis of data quality issues. Cuneiform provides users with traceability and a complete data quality view across systems, applications, and workflows.
Streamlined data normalization and standardization.
Cuneiform’s ETL layer enables data to be ingested in various formats (from batch as well as real-time streaming sources). Data is easily transformed before applying data quality rules.
Scalability.
The solution processes millions of checks with complex rules in runtimes of mere minutes at a cloud scale.
By leveraging the Cuneiform Platform, you can obtain and use more accurate, data-driven insights through effective data quality monitoring for faster and efficient reconciliation. If you’re ready to streamline and automate your reconciliation processes, request a demo of Cuneiform Reconciliation today!
