Data quality is a critical issue within today’s enterprises, as their success relies on having reliable and trustworthy data. Data must be correct, consistent, complete, and timely for it to be useful and fit-for-purpose. Without an effective data quality tool, low-quality data can cause a variety of problems for enterprises of all sizes. Specifically, business leaders can make uninformed decisions and submit inaccurate regulatory reports based on faulty data, leading to missed opportunities, client dissatisfaction, and hefty regulatory fines. Additionally, firms must use substantial resources to fix any errors and discrepancies, increasing overall operational costs.
Given the rising amount of incoming data from various sources, there is a critical need for enterprises to use data quality tools within their data management strategy, in order to monitor and fix low-quality data across a variety of internal sources.
According to Gartner, data quality tools provide the processes and technologies for identifying, understanding, and correcting flaws in data that support effective information governance across operational business processes and decision-making (Source).
Data quality tools apply rules and automate manual processes to ensure that enterprises have high-quality data throughout their organization. Even if the data is of high quality upon initial integration or ingestion, it generally undergoes various transformations that could compromise its integrity. Therefore, it becomes vital that data quality tools create rules to correct “dirty” data to streamline processes, reduce operational costs, and improve the accuracy of downstream analytics and insights.
These tools typically address four basic areas, specifically:
However, data quality tools can now also accomplish data mapping, consolidation, ETL (Extract, Transform, Load), and much more. Additionally, reconciliation functions are also performed by most data quality tools.
Ensuring data quality and process correctness across the enterprise is extremely difficult and costly, due to the inherent complexity of business processes and the heterogeneous nature of IT applications. Enterprises are forced to either hire large teams to manually fix errors or implement an effective data quality tool to identify and address them.
Many data quality tools on the market have significant limitations due to their static nature. These tools perform data quality checks as a last step in the pipeline instead of perpetually checking the data throughout the data’s lifecycle. Additionally, most solutions are batch-oriented and bilateral, which means that to perform E2E data quality checks on entire business flows, multiple bilateral checks must occur.
Using most data quality tools, enterprises cannot monitor data quality throughout the data’s lifecycle. Many tools use a passive approach, in which firms manually fix errors or discrepancies within an enterprise’s siloed systems, applications, or workflows, instead of across the E2E landscape.
Firms that implement an effective data quality tool experience a variety of benefits.
Better Business Decisions and Opportunities
Enterprises have cleaner, safer, and higher-quality data, resulting in more accurate analytics, clearer insights, and data-driven decisions. These firms can then monetize their data for direct top-line growth.
Reduced Risk and Increased Regulatory Compliance
Firms can reduce their risk and avoid hefty fines by having the right data quality controls for mission-critical data used for regulatory reporting.
Increased Operational Efficiency and Reduced Cost
Enterprises no longer have to comb through and verify large volumes of data manually, which is resource-intensive, laborious, and can often lead to false or duplicate investigations.
Ensuring enterprise-wide data quality is a key goal of any data governance and data management model. Modern data governance solutions include both metadata management and data quality capabilities.
Metadata management tools capture data quality rules in one unified location. Their data catalogs describe properties of data sets and list the data quality rules they need to adhere to. Metadata tools are focused more on documentation. However, data quality tools actually implement the rules and apply them to the data.
With an effective data governance strategy (that includes both tools), firms can solve key problems in an automated and real-time fashion.
At its core, the PeerNova® Cuneiform® Platform is a zero-code E2E data quality automation platform.
The solution provides continuous data quality by performing data quality checks and applying data quality rules across workflows. It also offers E2E visibility, ensuring firms can quickly identify and resolve data quality issues in real time.
Additionally, the solution automates the process of acquiring data, connecting datasets, running data quality checks, generating relevant reports, and fixing errors in real-time (or scheduled increments).
As a zero-code platform, the Cuneiform Platform provides business users with a self-serve interface to dynamically create, configure, and execute applications and rules without coding knowledge. Business users can rapidly define and deploy data integrity and process correctness controls across a firm’s workflows, applications, or business processes.
Cuneiform breaks down silos and manages lifecycle events across workflows through cross-functional collaboration, the lineage of data and metadata, and by building a single source of truth. Firms can efficiently perform root-cause analysis to manage, prioritize, and resolve exceptions promptly.
Using this dynamic approach to data quality and data management, the platform creates E2E, integrated, and active lineages across disparate tools and systems, making the solution significantly more efficient than others. By ensuring continuous E2E data quality, enterprises will increase their operational efficiency, reduce risk, and have better decision-making abilities.
For more information, reach out to the sales team for a no-commitment demo. You can also watch the supplemental video featuring Gangesh Ganesan, PeerNova’s Founder and CEO, to discover why data quality tools are so critical today.
Sources:
Gartner Glossary. DQ Tools. Definition. Link.
