From creation to disposition: Managing data through its lifecycle

Traditionally, information management professionals focused heavily on disposition. But a necessary shift in focus is occurring that emphasizes the beginning of the lifecycle instead. This shift (and subsequent broader view) is vital because data is the raw material that becomes information only when given value and context, two steps that are key when creating and acquiring data. This information may ultimately become official records requiring retention due to legal, regulatory, or business obligations.

While reminiscent of the familiar records lifecycle, the data lifecycle introduces new complexities. Data is often reused and transformed, and it requires integrity for longer periods, especially for analytics and AI. This complexity is amplified by reuse, making a clear understanding of the data’s context and journey essential from the very start.

Let’s talk third-party risk and Gen AI

Understanding the data supply chain—the end-to-end flow of data both inside and outside your organization—is a key challenge when we talk about the data lifecycle. It’s crucial to know your data’s origin, original use case, and fitness for its current purpose. This becomes particularly complex when data is acquired from or shared with third parties. Organizations are increasingly accountable for the data they purchase and are expected to perform due diligence. This is a critical intersection of two very important functions: data lifecycle management and third-party management. You may not currently be aligned with those in your organization who manage one or the other, so sharpening contractual clauses and conducting thorough vendor risk assessments are critical first steps in aligning these functions.

Generative artificial intelligence (AI) adds another layer of complexity to the data supply chain, both for in-house and third-party applications. The success of AI hinges on data integrity, and we know that the output is only as good as the input data. As models generate and reuse their own data, this becomes a more complex cycle. Understanding connections and ensuring data integrity requires a strong architecture from the outset.

Related: Retention, privacy, and security: Keys to AI success

The role of data governance and global regulation changes

A dedicated data governance team and framework are essential for establishing control and consistency, helping define policies, ensuring data alignment, and managing risk. Navigating the regulatory landscape remains challenging and requires a team committed to top-notch vigilance and flexibility. The environment is often described as “very fluid,” with varying approaches globally (e.g., comprehensive EU regulations vs. a more fragmented US picture with emerging state laws).

Global companies are literally piecing together regulatory requirements and must navigate carefully to satisfy these changing demands. Regardless of specific AI laws, existing privacy and cybersecurity regulations still demand careful data handling.

Ultimately, organizations face the push-pull of innovation versus established frameworks. The goal is to unleash technological capabilities safely to build trustworthiness. But how? Get your Legal, Compliance, IT, Information Governance, Privacy, and business units on board quickly and early. This is the best way to create clear policies and manage risk effectively.

Related: Stay ahead of data regulations in 2025

What’s next for data and information management?

Given these complexities, what practical steps can information management and governance professionals take? Several key areas demand focus.

Data mapping and documentation: Understanding the data journey

As AI models become more prevalent, the need for data explainability, or understanding how a decision was made, increases. Data maps document your data’s lineage and detail how it’s been created or acquired, used, stored, changed, and handled. While potentially daunting, mapping is crucial for compliance (especially with privacy regulations like GDPR), cybersecurity incident response, and business insights.

Tackling everything at once is unrealistic. Instead, prioritize based on risk. While personal information remains at the top of the risk list, other high-priority areas include external-facing systems and critical financial data. Addressing structured data might be easier initially, but don’t neglect the significant risks often hidden in unstructured repositories.

Building trusted sources and collaboration

Beyond mapping, building internal trusted data sources is important. This involves knowing and profiling data, understanding its business context, and managing its metadata effectively. Documenting this information helps ensure data is understood and used appropriately throughout its lifecycle. Success requires breaking down silos. The focus on data necessitates interconnection and collaboration between teams that may have historically worked separately.

Above all, professionals should recognize their value in this process. The goal is to drive business value and build trust, not just tick regulatory boxes. Make the message understandable and straightforward: We must ensure data integrity in order to innovate and ultimately drive organizational success in this data-centric world.

Watch the full webinar

Interested in learning more about this topic and hearing the live Q&A with our panelists? Visit Iron Mountain’s 2025 Education Series to watch the on-demand recording of From creation to disposition: Managing data through its lifecycle.