Your Firm’s Success Depends On An Effective Data Quality Model
A guest blog from Chamber Member PeerNova
June 7, 2021
To learn more about this important topic, be sure to download our Data Quality White Paper.
The success of a business relies on its data quality. Data must be correct, consistent, complete, and timely for it to be useful and fit-for-purpose. However, achieving high-quality data has been a significant challenge within today’s financial firms. Low-quality data can quickly trickle down the pipeline, causing a variety of issues for firms of all sizes. For example, a single low quality datum can result in business leaders making uninformed decisions and submitting inaccurate regulatory reports. This can lead to missed opportunities, client dissatisfaction, and heavy regulatory fines. Firms must also use additional resources to fix any errors or resolve exceptions, increasing their overall operational costs. It becomes critical that businesses implement an effective data quality tool or data quality platform to meet their goals, and ultimately, succeed.
What is a Data Quality Tool?
A data quality tool identifies, parses, and corrects flawed data to support effective information governance and data management. It applies rules and automates manual processes to ensure that firms have high-quality data throughout their organization. High-quality data that is ingested or integrated into the firm’s systems still undergoes various transformations that could compromise its quality. Therefore, catching any inaccuracies is vital at all touchpoints of a business. Data quality tools typically address four core areas:
- Data cleansing
- Data integration
- Master data management
- Metadata management
Data quality tools create rules to correct data that streamline processes, reduce operational costs, and improve the accuracy of downstream analytics and insights. Modern solutions can now also provide data mapping, consolidation; ESL (extract, transform, load); and more. Additionally, reconciliation functions are also performed by most of today’s data quality tools.
What are the Challenges with Today’s Data Quality Platforms?
Ensuring end-to-end (E2E) data quality across the firm is laborious and costly, due to the inherent complexity of business processes and the heterogeneous nature of IT applications. Many data quality platforms on the market have significant limitations due to their static nature. These solutions often perform data quality checks as a last step in the pipeline, instead of perpetually checking the data throughout the data’s lifecycle. Additionally, most are batch-oriented and bilateral, which means that to perform data quality checks on entire business flows, multiple bilateral checks must occur. As a result, firms must increase their operational resources to hire teams to manually fix errors and unify siloed systems, applications, and workflows.
What Benefits Can an Effective Data Quality Tool Provide?
There are a variety of benefits that firms can experience by implementing an effective data quality tool.
Better Decision-Making and More Opportunities
Firms have continuous, high-quality data, resulting in more accurate analytics, clearer insights, and accurate data-driven decisions. Institutions can then monetize their data for direct top-line growth.
Reduced Risk and Regulatory Fines
Firms can be confident in the data they are submitting to regulators, reducing their regulatory risk and avoiding hefty fines.
Increased Operational Efficiency and Reduced Costs
Firms no longer have to manually comb through and verify large volumes of data manually. They can use fewer resources, and avoid false or duplicate investigations.
What is the Ideal Data Quality Platform?
The ideal data quality solution provides the following:
Continuous, E2E Data Quality
An effective solution provides continuous data quality by performing data quality checks and applying data quality rules across siloed systems, applications, and workflows. Additionally, the tool should automate the process of acquiring data, connecting datasets, running data quality checks, generating relevant reports, and fixing errors in real-time.
Faster Exception Management and Root-Cause Analysis
Using an effective data quality tool, firms can perform faster exception management and root-cause analysis to manage, prioritize, and resolve errors as they occur. This also reduces resolution times for data quality, SLAs, and timeliness metrics, ultimately leading to better operational efficiency and faster time to market.
With a zero-code platform, business users can easily create, reconfigure, and implement a variety of applications and workflows themselves through a self-serve graphical user interface (GUI).
It is vital that the tool achieves continuous data quality at scale, through stream-based execution and auto-scaling to handle any volume spikes. By implementing an effective data quality tool, firms increase their operational efficiency, reduce risk, and make better decisions. At its core, the PeerNova Cuneiform Platform is a zero-code platform that provides continuous data quality and simplifies exception resolution across internal and external data sources. For more information, please visit peernova.com or contact us.
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