Reconciliation is one of the most important processes for any financial institution. Whether it applies to integrating data from different source systems to a central data warehouse, or “balancing the books” within a firm, the process can be extremely complex.
Traditionally, reconciliation involves using simple record counting and comparing it to contrasting records. However, this approach is extremely laborious and would only accurately capture the issue leading to missing records. With large volumes of low-quality data being ingested and reconciled, firms can quickly become overwhelmed, leading to additional costs and resources.
Even today, institutions still experience bottlenecks due to the volume and quality of both the migrated and housed data within their systems. Firms must prioritize their need for an effective reconciliation tool.
Current reconciliation tools have a wide variety of challenges and limitations, such as:
They perform bilateral (two-way) matching. Current tools essentially match pairs of records. This is time-consuming and laborious especially for complex workflows which involve a large number of datasets and complex rules, requiring multiple “passes”. Data has to be loaded multiple times, and results manually stitched together to get a consolidated list of exceptions for the entire workflow.
They only support batch processing and require a pre-processing layer. Firms have to load their data multiple times, which is time-consuming and laborious (especially with hundreds of data sources and rules). 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 IT effort.
They do not support root-cause analysis of data quality issues. Firms must manually query individual data sources, manually loading and linking to various sources.
They do not include business impact metrics or data quality scorecards. While reconciliation metrics can answer a wide variety of questions, they are limited when determining the total business impact of data quality issues. Firms become unable to easily prioritize what exceptions are most valuable and should be solved first.
They require significant IT involvement. Reconciliation tools require significant IT effort to build out and maintain rules and processes, including querying, scripting, programming, etc.
They are unable to handle large volumes of data. These solutions are unable to handle the increasing volumes of data, when there are millions of records and complex rules.
PeerNova’s 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. Financial firms can streamline their reconciliation processes, saving them time and money.
Specifically, Cuneiform Reconciliation:
Supports multi-lateral (N-way) matching. Cuneiform supports multi-lateral matching that is fast and efficient to handle workflows which involve a large number of datasets and complex rules, including fan-ins and fan-outs (for compressions and allocations), all without requiring multiple “passes”. Data is loaded just once, and results are automatically collated to generate a consolidated list of exceptions for the entire workflow.
Supports stream processing as well as data preparation and enrichment. The solution supports diverse batch and streaming data sources, with no pre-processing needed, regardless of the number of data sources or rules. Users can merge multiple datasets to create enriched derived datasets as well as easily integrate reference data sources with no IT effort or additional pre-processing software.
Supports easy root-cause analysis.With continuous data quality monitoring, firms can quickly identify, address, and resolve exceptions.
Provides business impact metrics and data quality scorecards 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, and allows 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.
Handles large volumes of data. The solution processes millions of checks with complex rules in runtimes of mere minutes at a cloud scale.
Implementing the right reconciliation tool is vital for a business’ success. However, there are many solutions available on the market today, all promising something similar. Which should you choose for your business? If you’re ready to experience what makes us different through the power of our platform, request a demo of Cuneiform Reconciliation today!
