A data governance failure can be catastrophic but preventive toolkits exist: Here’s what to do

The scenario in India is not very different. As regulatory penalties soar into hundreds of crores, Indian organizations now face an unprecedented convergence of regulations; a single incident can trigger action under multiple regulatory frameworks within hours. The risks are not just regulatory. Flawed data governance practices not only disrupt business operations, but also result in inaccurate reporting, delayed decision-making and impaired customer trust.

Yet, most still operate with a ‘we’ll find it when we need it’ approach to data management, even though such an attitude can prove financially and operationally catastrophic.

Why do organizations struggle?: In our experience, the core issues that lead an organization’s data governance to fail, often lie in outdated, fragmented technology and decades of underinvestment in building a strong governance backbone.

Often, organizations that are undergoing massive restructuring exercises with multiple mergers or acquisitions are the ones that fall prey. On the other hand, organizations with legacy IT systems with an under-evolved risk environment remain vulnerable.

When organizations begin to reassess governance frameworks, uncover vulnerabilities and strengthen their technology infrastructure, they start recognizing drawbacks of the siloed approach they have always taken. They find it has prevented scale, which, coupled with manual processes and controls, results in weak cyber defences.

Moreover, data stored in different formats makes it difficult to establish a unified data governance framework. As a result, organizations often rely on technology to compensate for poor data governance.

Data governance failures represent a strategic and not just regulatory risk: Preventing such failures isn’t about deploying more technology, but about embedding integrated, business-aligned governance capabilities across the organization.

A fragmented, reactive approach no longer suffices in an environment where operational continuity, regulatory scrutiny and brand credibility are tightly intertwined.

Here’s a five-pillar framework designed to help enterprises build resilience and agility at the core of their data strategy:

First, break down data silos with unified architecture by centralizing logs and enabling forensic-grade search without needing to move data. Organizations with evolved data governance practices locate critical communications in minutes, not months, saving time, reducing risk and improving their decision-making.

Second, automate compliance to meet regulatory deadlines by integrating pre-configured templates for compliance with rules laid down by the country’s apex cyber-security body CERT-In, India’s digital personal data protection law and the market regulator Securities and Exchange Board of India.

These templates should be auto-populated with data from live systems. Establish escalation workflows that can be activated within regulatory service-level agreements, enabling timely and accurate reporting under pressure. This would reduce the risk of penalties and reputational damage.

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A clear, leadership-approved charter is essential to define the primary objectives of data governance and guide its implementation across the organization. Additionally, the company must align its data governance strategy across all group entities. It may even need to temporarily slow down the pace of report generation to reduce chaos and allow time for disparate sources of data to be unified effectively.

To sum it up, mastering data governance is no longer optional, it’s a critical competitive advantage today. Organizations that get it right don’t just avoid costly penalties, but accelerate decision-making, generate trust among stakeholders and create resilience that sets them apart in a hyper-regulated market.

The authors are, respectively, India and emerging markets leader, and partner, forensics and integrity services, at EY.