Within the last decade, firms have been collecting data at an increasing rate. However, having a vast amount of data is meaningless if it is low quality. Data and business analysts rely on high-quality data to increase efficiency, reduce risk, and gain actionable insights for better decision making. Staying ahead of the competition and satisfying stakeholders requires firms to trust their data completely. If even a single datum is incorrect, every area of a business can be affected. Firms everywhere have shifted their efforts and resources to implementing an effective data quality monitoring tool. Even with a model in place, business and data analysts continue to face significant data quality challenges today.
Data Fragmentation and Visibility Issues
With an increase in data comes an increase in systems, applications, and workflows. Financial institutions have complex IT infrastructures with numerous databases and data sources (both internal and external). Data is acquired from and stored in disparate locations, making it difficult for both business and data analysts to locate or update it. With this fragmentation, data becomes scattered, outdated, and even duplicated, leading to availability, visibility, and accessibility issues. Firms become unable to gain an end-to-end view across all sources to gain transparency into their data.
Resource-Intensive, Manual Processes
When data is fragmented across the landscape, it becomes increasingly difficult and resource-intensive for IT and operations teams to put the pieces together. Additionally, fixing errors can take significant time for business and data analysts to resolve. Manually performing root-cause analysis and exception management becomes time-consuming and unnecessary in a firm’s fast-paced business environment.
Static, Reactive Data Quality Tools
Firms often address data quality issues through static, reactive tools. These tools reconcile the incorrect piece of data instead of fixing the process, rules, or systems that caused the error. Additionally, these tools provide data quality metrics for individual systems, instead of providing multiple systems as a whole. Firms fail to run data quality checks continuously, resulting in incomplete, incorrect, inconsistent, and untimely data.
No End-to-End Data Quality Insights
Data quality must be measured from end to end, across the data’s lifecycle. Most tools fail to do this, therefore only measuring data quality in pieces. As a result, the complete business impact is impossible to accurately calculate.
Long Data Quality Cycles Lead to Higher Risk
Another data quality challenge is that reconciliation tools cannot handle mass volumes of data. As the amount of data and the number of business rules grow, the execution time for data quality batches increases. If there are multiple batches, data quality runs take too long to complete, leaving insufficient time to identify and resolve data quality issues.
Difficult and Time-Consuming Root-Cause Analysis
Firms deploy reconciliation tools to address data quality issues. To perform root-cause analysis of all exceptions for a transaction that spans multiple data sources, many exception reports must be manually collated and analyzed. As the number of bilateral reconciliations increases, the number of these exception reports also increases. The entire process becomes overly complex, laborious, costly, and time consuming.
High Costs to Build and Maintain Data Quality Rules
The majority of reconciliation tools are bilateral in nature. As the number of data sources increase, new bilateral reconciliations must be set up and executed. For firms that have hundreds of data sources, this procedure quickly becomes difficult and expensive to build and maintain.
The PeerNova® Cuneiform® Platform solves all of these data quality challenges. It is a zero-code platform that provides data-quality monitoring and exception resolution across internal and external data sources.
The data quality monitoring solution instills confidence in data by measuring data quality metrics and allowing users to prioritize and resolve errors quickly. It provides unparalleled end-to-end data quality, visibility, and traceability for your transactional workflows to ensure data correctness, completeness, consistency and timeliness. As a result, firms increase operational efficiency, improve business performance, and address executive, stakeholder, and regulatory inquiries confidently and promptly.
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