When it comes to releasing new products and services, time-to-market is an important KPI. To stay competitive and maximize revenue opportunities, new products and services are often unveiled at a rapid pace. Data quality issues can disrupt the customer onboarding processes onto these new offerings, resulting in revenue delays, SLA misses, and customer dissatisfaction. To avoid these problems, firms must incorporate zero-code data quality rules into their overall data management model.
Data quality rules are rules that typically measure data quality across four data quality dimensions (correctness, completeness, consistency, and timeliness). Data quality rules are applied to all types of data (transactional data, reference data, and master data) and cover all mission-critical workflows in an enterprise.
Currently, data can reside in various formats in disparate sources across the enterprise. Data is continuously created, enriched, transformed, and reported by multiple applications. Data sources, applications, and systems are governed by a firm’s data architecture.
Measuring data quality across silos requires standardizing the data, defining relationships between the data, and then applying data quality rules to them at every point in the data lifecycle. This multi-step process is extremely challenging and requires expensive investment in extracting, transforming, and loading (ETL) tools, point solutions (such as reconciliation tools), and data lineage tools. In addition to the cost of tools, they must be configured separately and integrated together, which requires system integration (SI) effort. This significantly increases operational costs as well as long build-out times for data quality rules. Maintenance of existing data quality rules also incurs similar costs and time overhead.
As firms focus on digital transformation and operational efficiency, many have realized that building applications manually is extremely time-consuming and resource-intensive. When business users need a specific application created or updated, they must wait for IT to take action.
To avoid this bottleneck, enterprises have begun to address these challenges through zero-code solutions, which focus on streamlining and decentralizing application development. Zero-code empowers business users to be the ones creating and updating applications for clients. Instead of wasting hours developing and tweaking products, IT teams can channel their energy towards further innovation.
Cuneiform is PeerNova’s zero-code platform that provides data quality monitoring and exception resolution across internal and external data sources. The solution instills confidence in data by measuring data quality metrics and allowing users to resolve data quality errors quickly. This enables financial firms to increase operational efficiency, improve business performance, and address executive, stakeholder, and regulatory inquiries confidently and promptly.
The platform offers a self-serve, zero-code data quality rules builder. Firms experience faster build-out times for new rules and for maintaining existing rules without IT effort. Users can easily create reusable business logic for data quality rules without writing a single line of code.
Through a drag-and-drop interface, this tool seamlessly applies rules and automates manual processes to ensure that firms have high-quality data throughout the organization. As a result, clients experience faster time-to-market, reduced IT costs, and streamlined processes. Having high-quality analytics and actionable insights results in improved decision-making.
Ready to see what the Cuneiform Platform can do for you? Request a demo today!
