Taming Modern Data Challenges: The Importance of Information Governance

In our last post, we discussed the explosion of generative AI content, including examples of AI-generated content, when that content could be relevant in discovery, and challenges, considerations and best practices associated with the discovery of AI-generated content.

Throughout the series, we’ve illustrated that organizations face an unprecedented challenge: the sheer volume, variety, and velocity of data. From collaboration tools and mobile devices to cloud-based enterprise systems and generative AI outputs, the sources of electronically stored information (ESI) continue to expand at a pace that makes it difficult, if not impossible, to manage discovery without a disciplined approach to effective management of that ESI.

These challenges have elevated the role of information governance (IG) from a compliance afterthought to a strategic imperative – as well as a vital first step in any defensible eDiscovery process. In this post, we will discuss how the importance of information governance has evolved in eDiscovery, why IG is an important component in taming modern data, the role that technology plays in effective information governance, and best practices for building an IG program that supports your eDiscovery initiatives.

The Evolution of Information Governance in Discovery

Information governance is not new. At its core, IG is the framework of policies, processes, technologies, and accountability structures that dictate how organizations create, manage, protect, and dispose of information. But the importance of IG has grown exponentially as discovery obligations intersect with complex data environments.

This evolution is perhaps best represented in the Information Governance Reference Model (IGRM), a framework originally developed by the EDRM community. The IGRM emphasizes collaboration across stakeholders, which includes legal, IT, records, security, privacy, risk, and business units. This ensures that information is not managed in isolation, but as an enterprise-wide responsibility.

What’s particularly notable is how the IGRM has become deeply integrated into the EDRM itself. Once treated as a precursor to discovery, IG is now recognized as inseparable from the entire lifecycle of ESI; in fact, the IGRM model is contained within the EDRM model itself. Effective discovery doesn’t start with identification, preservation or collection; it starts with governance. Without a strong IG foundation, organizations risk inefficiencies, increased costs, spoliation claims, and compliance failures.

Why IG Matters in the Era of Modern Data Sources

The scope of modern data sources highlights just how critical information governance has become. Discovery is no longer just about email servers and file shares. It now encompasses highly dynamic, distributed, and even ephemeral forms of data. Let’s tie IG to the modern data types we’ve discussed earlier in this series:

Mobile Devices

Smartphones and tablets are now indispensable tools for business communication, collaboration, and productivity. From text messages and encrypted messaging apps to multimedia files and GPS metadata, mobile devices hold an immense amount of discoverable data. Yet these devices also introduce challenges: custodial ownership in BYOD (bring your own device) policies, encryption and privacy controls, and constantly changing app ecosystems.

An effective IG program defines policies around mobile usage, separates business from personal data where possible, and integrates mobile device management (MDM) tools to ensure discoverability without over-collecting irrelevant personal content.

Enterprise Solutions and Collaboration Apps

Organizations increasingly run on SaaS-based platforms like Salesforce, ServiceNow, Workday, and other enterprise solutions. These solutions generate structured and semi-structured data that may be relevant to disputes or investigations. Traditional discovery approaches (for example, massive CSV exports) often strip critical metadata or relational context.

Additionally, the rise of collaboration tools such as Microsoft Teams and Slack has been transformative, but these platforms blur traditional notions of “documents” and “attachments.” Linked documents, threaded chats, emojis, reactions, and dynamic content present real challenges in preservation and review.

IG programs help address this by establishing connectors and APIs, defining retention schedules, archiving practices, and standardized metadata capture for enterprise solutions (including collaborative apps). Governance technology can map communication patterns, link messages to underlying content, and preserve contextual integrity, which helps ensure that discovery presents a coherent story rather than disjointed fragments.

Structured Data

As we discussed, structured data has long been part of discovery, but its importance is growing. For example, structured data sources can be important in antitrust, securities, and product liability cases, but discovery professionals often need special expertise to interpret structured outputs without losing meaning.

With IG, organizations can build defensible protocols for how structured data is retained, mapped, and sampled. Instead of ad hoc exports, governance aligns IT and legal teams to ensure structured data collections are repeatable, validated, and explainable to courts.

Generative AI-Created Content

Generative AI tools like ChatGPT, Microsoft Copilot and others create drafts, code, chat transcripts, summaries, and analyses that may contain sensitive or privileged material. Complicating matters further, AI usage is often decentralized: employees experiment with personal accounts or third-party AI tools, creating shadow IT risks.

Governance frameworks are essential here. Policies must dictate approved AI tools, define how AI outputs are stored and tagged, and track metadata (such as prompts and training data sources) to ensure accountability. In discovery, IG enables organizations to identify, collect, and review AI-generated content just as they would other records, while also addressing questions of authenticity, reliability, and privilege.

Technology’s Role in Modern Information Governance

Effective IG isn’t just about policies; it requires technology that bridges gaps between business users, IT infrastructure, and legal obligations. Governance technologies today offer capabilities such as:

• Data mapping and classification: continuously identifying what data exists, where it resides, and who owns it.
• Retention and disposition automation: applying rules to delete data defensibly or archive it for legal or regulatory needs.
• Legal hold integration: ensuring relevant data is preserved once litigation or investigations are anticipated.
• Cross-platform connectors: linking governance to cloud, collaboration, and enterprise systems in real time.
• Audit trails and reporting: providing transparency and defensibility in how data was handled.

When paired with the IGRM framework, these technologies give organizations not just visibility, but also active control over their information ecosystems.

Seven Best Practices for Building an IG Program That Supports Discovery

To prepare for modern discovery challenges, organizations should take a strategic approach to IG implementation:

• Align stakeholders early: Governance is not just IT’s job or legal’s job. Involve records managers, compliance teams, security, and business units from the outset.
• Adopt the IGRM as a blueprint: Use the model to guide roles, responsibilities, and communication channels across the enterprise.
• Prioritize high-risk data sources: Consider focusing governance first on mobile, collaboration, and AI content, where risks of spoliation or oversight are greatest.
• Establish clear retention and disposition policies: Keep what you need, defensibly delete what you don’t. Over-retention creates cost and risk.
• Leverage automation wherever possible: Manual processes cannot keep pace with data growth. Automate classification, preservation, and policy enforcement.
• Integrate with discovery workflows: Ensure governance systems can directly support collection, review, and production without requiring ad hoc workarounds.
• Continuously monitor and adapt: Regulations, platforms, and data creation patterns evolve. Governance is not a one-time project but an ongoing program.

Conclusion

Discovery of modern data today requires a strong IG program. Without robust IG, discovery often becomes chaotic, expensive, and legally vulnerable. Conversely, organizations that invest in governance through frameworks, technology, and best practices position themselves to confidently discover data from modern data sources like mobile devices, enterprise platforms, collaboration apps, structured databases, and generative AI. A strong IG program isn’t just a safeguard; it’s a competitive advantage. Those who embrace it will be prepared not just for the discovery challenges of today, but for the ever-evolving landscape of tomorrow.

In our final post in the series, we will discuss the importance of the legal data intelligence initiative and how it can help legal teams streamline their modern data workflows in discovery!

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