Businesses of all sizes are placing data quality at the top of their priority list. Measuring data quality across the dimensions of correctness, completeness, consistency, and timeliness is vital to ensuring efficient and timely decision-making. However, meeting these metrics alone is not sufficient for identifying and resolving the data quality issues affecting mission-critical business workflows.
Firms must obtain metrics surrounding the business impact of their data quality issues to prioritize remediation efforts. Identifying these issues is important but does not paint a complete picture. Firms must know the business value implications of each data quality break to prioritize addressing them accordingly.
In other words, each data quality issue must be tied to a material business value for prioritization to avoid missed opportunities, hefty fines, and operational inefficiencies.
The Business Value Impact metric is a quantitative measure showing material impact to a business caused by data quality issues. The specific attribute used to calculate Business Value depends on the business workflow. Some examples are listed in the table below.
Workflows in today’s global enterprises are complex, involving tens or hundreds of activities, with data flowing through multiple data sources and stored in various formats. Data quality issues may arise at any step of the workflow. How should the operations and business analysts prioritize data quality issues? They should focus on identifying and resolving issues that have the maximum business impact. However, accurately measuring the business impact of data quality issues across the entire workflow using traditional reconciliation tools or bespoke homegrown solutions is exceptionally difficult (if not impossible).
Without insights on the business impact of data quality issues, teams cannot prioritize them effectively, resulting in sub-optimal resource allocation and higher operational risk.
The Cuneiform Platform is a zero-code solution that provides continuous, end-to-end data quality monitoring, business value impact measurements, and exception resolution across internal and external data sources.
With Cuneiform, firms can prioritize their data quality exceptions based on their business impact to resolve the most critical issues. The multidimensional nature of Cuneiform’s data quality metrics makes it easy for customers to view insights and report the material business impact of exceptions. This allows for value-driven prioritization of data quality efforts and fixes. Users can calculate Business Value Impact grouped by any attribute, e.g., product, customer/counter-party, SLA level, etc.
By discovering the impact of data quality issues across relevant business metrics in real-time, firms can prioritize fixes that may result in credit risk, regulatory fines, and more. As a result, firms can utilize high-quality data and real-time, actionable insights to make confident business decisions, increase operational efficiency, and reduce risk.
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