This is the fourth article in our series on data management and its connection to responsible AI. AI systems depend on large amounts of data, but without proper oversight, this data can quickly become a source of errors, privacy concerns, and ethical risks.

In this article, we focus on Data Lifecycle Management (DLM)—a structured way to manage data from the moment it is created until it is no longer needed. Drawing from the DAMA-DMBOK Framework and the 2024 AI Index Report, we explore how strong DLM practices help ensure that data is handled carefully, securely, and with clear purpose at every stage, supporting the development of responsible and trustworthy AI systems.

Stay tuned for more insights from our team on how thoughtful data practices can build a safer, fairer future for AI.

Primer

Data is at the heart of AI, decision-making, and every digital service we use today. But simply having data is not enough (what matters just as much is how we manage it). That is where Data Lifecycle Management (DLM) comes in. DLM refers to the way organizations handle data throughout its “lifetime”—from when it is first collected, to how it’s stored, used, protected, and eventually deleted.

In both the AI industry and broader data governance conversations, managing the lifecycle of data is becoming more important than ever. This article explores what DLM is, why it matters, and how it shows up in recent reports like the 2024 AI Index and foundational documents like the DAMA-DMBOK (Data Management Body of Knowledge).

Understanding Data lifecycle management

The DAMA-DMBOK defines DLM as the process of controlling data from its creation to its retirement. The lifecycle typically includes the following stages:

1. Data Creation and Capture: Data enters the system through input forms, sensors, or user activity.
2. Data Storage: Data is stored securely in databases or warehouses.
3. Data Usage: It’s used for analysis, AI training, reporting, or real-time decision-making.
4. Data Sharing: It might be shared with other systems, partners, or users.
5. Data Archiving: Older or less-used data is stored long-term but not deleted.

1. Data Destruction: Data is permanently deleted when it’s no longer needed.

Managing these stages well helps ensure data is accurate, safe, and useful without becoming a burden or risk.

Data Lifecycle and AI

The 2024 AI Index Report highlights just how critical good data management is for modern AI. Many of the biggest developments in AI, such as GPT-4 or AlphaMissense, rely on enormous datasets​​. If these datasets are not well-managed, the models trained on them can be biased, insecure, or low-quality.

One major trend is the rise of open foundation models. In 2023, over 65% of new foundation models were open-source​. This openness helps with transparency but also raises questions: Where did the data come from? Was it ethically sourced? Has sensitive data been protected?

This is where DLM links closely with responsible AI. According to the AI Index, privacy, fairness, and transparency are all tied to how data is managed from start to finish​.

Challenges in Practice

Even though most companies understand the importance of data management, implementing it correctly is hard. According to the DAMA framework, poor DLM often leads to:

• Duplicate data that wastes storage and creates confusion
• Outdated information that leads to wrong decisions
• Security risks, like data breaches

• Regulatory problems, especially with GDPR or HIPAA

The AI Index also reports that incidents involving AI and data misuse continue to rise year after year, with over 120 reported in 2023 alone​. Some of these cases involve leaked data, unclear data use policies, or failures in model training processes.

Where We are Headed

What is encouraging is that both government and private sectors are starting to take DLM seriously. In the 2024 AI Index, U.S. agencies like the Department of Energy and Department of Transportation began issuing AI-related regulations for the first time​. These rules often touch on how data is collected, stored, and used; key elements of DLM.

Similarly, new tools and systems are being developed to better manage data. For example, companies are investing more in data catalogues, metadata management tools, and automated data governance platforms.

And, as AI continues to evolve, we are likely to see a stronger focus on the “data supply chain”—tracking not just where data is, but where it came from, who used it, and for what purpose.

Thoughts from the Author

I have been thinking a lot about how easy it is to forget that behind every fancy AI model, there is a mountain of data that someone had to manage. When DLM is done right, you don not notice it. Things just work. But when it is done poorly, the whole system suffers; people lose trust, outcomes become less fair, and mistakes get amplified.

What struck me most from the AI Index is how many real-world problems (like bias or privacy leaks) stem from something as simple as not knowing where data came from or who handled it. In a way, DLM is like plumbing. It is not exciting, but it’s essential. And if it breaks, the damage is everywhere.

As we keep moving forward with more powerful AI, I think DLM will shift from being a “background” job to a strategic one. It’s not just about storing data anymore—it is about protecting people, building trust, and making AI something we can all rely on.

References

1. DAMA International. The DAMA Guide to the Data Management Body of Knowledge (DAMA-DMBOK). 2nd ed. Technics Publications, 2017.
2. Artificial Intelligence Index Steering Committee. AI Index Report 2024, Chapter 1: Research and Development. Stanford University, 2024​.
3. Artificial Intelligence Index Steering Committee. AI Index Report 2024, Chapter 3: Responsible AI. Stanford University, 2024​.
4. Artificial Intelligence Index Steering Committee. AI Index Report 2024, Chapter 7: Policy and Governance. Stanford University, 2024​.