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5 Key Differences Between an Active Data Governance and a Passive Data Governance Model

15 Apr, 2020  |  By PeerNova   |  Published in Articles,

Active vs. Passive Data Governance

In today’s fast-evolving, tech-driven world, data is only as valuable as the methods used to manage, organize, and protect it. Enterprises are quickly realizing the importance of implementing a strong data management strategy, with a significant focus on data governance. For enterprises operating in highly regulated industries such as finance and healthcare, it is imperative that they build and implement an active data governance model to ensure ongoing data quality and regulatory compliance.

When we talk about data governance, not all models are created equal. Today, most enterprises implement a passive data governance approach and are therefore unable to actively enforce, track, or measure the effectiveness of their data and policies. This approach involves performing data quality checks as a last step in the value chain (ie., as part of an end-of-day process or right before critical reports are generated). It measures data quality at specific points for individual data sources—it does not provide data quality metrics across end-to-end systems, applications, and workflows. By failing to cohesively monitor quality throughout the data’s lifecycle, discrepancies and errors are easily missed. This can quickly manifest itself throughout the rest of the enterprise, affecting the ability to make critical business decisions, and comply with regulatory requirements.

The most effective approach is an active data governance model. By incorporating active data governance into daily data management, firms can set up rules and policies to proactively monitor data quality and foresee problems before they occur. Through automation and ongoing data quality checks, an active data governance model not only remedies existing data issues but it also continuously manages and monitors data throughout its entire lifecycle. By having correct, consistent, and complete data to rely on, enterprises have more accurate reports, clearer insights, and strategic advantages. As a result, enterprises reap the benefits of increased operational efficiency, improved data quality, better business decisions, and regulatory compliance.

Five Key Differences Between Active and Passive Data Governance

When it comes to active versus passive data governance models, there are five key differences.

Unified and Standardized Data with End-to-End Visibility

The enterprise landscape is often fragmented. It is difficult to gain insight into data when it is scattered across siloed systems, applications, and workflows. Passive data governance attempts to organize and manage data within these individual silos, leading to duplicate or incomplete pieces of data. Active data governance unifies and standardizes data across these disparate systems while providing end-to-end visibility and maintaining the local perspective. With this unified view, enterprises can cohesively streamline, optimize, and transform their data, processes, and operations.

Auto-generating Data Dictionaries, Glossaries, Catalogs, and Rule Repositories 

Data dictionaries, glossaries, catalogs, and rule repositories are the foundation of data governance. Passive data governance requires constant manual creation of these fundamental components. Active data governance builds, updates, monitors, and optimizes continuously using automated tools for metadata and rules management. This results in faster root-cause analysis and lower operational costs.

Ongoing Data Quality

Even with current data governance tools, enterprises continue to experience data quality challenges. Regulators use ‘Key Quality Indicators’ to measure the quality of data to understand its fit for purpose. In order to stay compliant, enterprises must comb through large volumes of data, which is both resource-intensive and laborious. This is especially true when data is stored in multiple systems. These manual efforts can lead to duplicate investigations and false positives, resulting in increased operational costs. Passive data governance attempts to address data quality issues by reconciling the actual data, instead of fixing the process, rules, or systems that originally caused the error. Additionally, a static data quality tool provides data quality metrics for individual systems. Comparatively, active data governance fixes the processes that caused the errors and applies rules, controls, and policies across the enterprise as a whole. These run data quality checks continuously, guaranteeing the completeness, correctness, and consistency throughout the data’s entire lifecycle.

Real-Time Exception Tracking

Exception tracking is extremely important to ensure that errors are caught before they interrupt an enterprise’s processes and workflows. With static data governance tools, it is difficult to identify or resolve exceptions due to the siloed views into the data. It is also difficult to gain insights into specific trends or recurring exceptions. Active data governance uses ongoing data quality checks to track, group, and prioritize exceptions across tools in real-time. Exceptions are flagged immediately and corrective actions quickly—and often automatically—begin. By monitoring exceptions in real-time, you can easily locate the root cause, identify future errors or trends, reduce operational costs, and prevent a ripple effect into the rest of your data.

Faster Root Cause Analysis

Due to the fragmentation in an enterprise, trying to identify the root cause of an exception may feel like finding a needle in a haystack. Due to a lack of end-to-end visibility and inefficient exception monitoring, passive data governance often results in duplicate and wasted efforts when investigating and resolving data issues. By providing end-to-end visibility, active data governance provides faster root cause analysis, leading to new digital transformation opportunities and reduced cost.

PeerNova’s Active Data Governance Tool: the Cuneiform Platform

PeerNova’s Cuneiform Platform is an active data governance and data quality tool that enables end-to-end (E2E) trust and transparency of data and business flows. The platform automatically builds, updates, monitors, and optimizes data dictionaries, glossaries, catalogs, and rule repositories. Using this dynamic approach to data quality and management, the platform creates E2E, integrated, and active lineages across disparate tools and systems, making it significantly more efficient than other data governance tools. By using the Cuneiform Platform, enterprises can quickly recognize and resolve exceptions, exceed client and stakeholder expectations, and identify the most promising digital transformation opportunities.

The value of data today is immeasurable. The volume and the speed at which data travels throughout an enterprise makes data governance invaluable. Even the smallest error or discrepancy in data quality could trickle down the pipeline and cause significant distress to an enterprise’s operational efficiency, regulatory compliance, and overall bottom line. Enterprises that implement active data governance have a significant advantage over those that take a backseat, passive approach to managing data.

If your institution is looking for an active data governance tool like PeerNova’s Cuneiform Platform, be sure to get in touch with us and request a demo today.

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