A guide to data lifecycle management in ML

When it comes to machine learning (ML), data isn’t just fuel for the engine, it is the engine.

High-quality data leads to high-quality products. That’s true whether you’re talking about safer medical predictions, more relevant clinical insights or more accurate decision-making across industries. Conversely, poor-quality data leads to poor-quality models, no matter how advanced your algorithms might be.

Unlike traditional software, where a fixed algorithm transforms inputs into predictable outputs, machine learning models learn from data. The patterns they discover, and the conclusions they reach, are entirely shaped by the information you feed them.

That’s why data lifecycle management in ML is so critical. From the moment you plan your dataset to the day you securely delete it, every decision you make affects your model’s accuracy, fairness and safety. Neglect one stage, and you risk introducing bias, missing critical cases or even failing regulatory checks.

What is data lifecycle management?

Data lifecycle management is a policy-driven process or approach for managing data from its creation or acquisition all the way to its eventual archival or destruction. It’s a comprehensive process that involves organizing, storing, processing, analyzing, retaining and securely disposing of data in a systematic and efficient way.

Benefits of data lifecycle management include:

• Improved data quality and reliability for decision-making
• Increased business agility and better resource utilization
• Cost reduction through optimized storage and timely deletion of obsolete data
• Strengthened data governance and accountability
• Efficient compliance with industry and legal standards

What are the three main goals of data lifecycle management?

1. Confidentiality: This principle is about safeguarding sensitive information from unauthorized access, disclosure or misuse. It requires security practices like encryption, access controls and authentication so that only approved users can view or modify the data
2. Integrity: Data integrity means preserving the accuracy, completeness and consistency of information throughout its lifecycle. It involves protecting against unauthorized changes, maintaining data quality and using mechanisms to detect and correct errors
3. Availability: Availability ensures that authorized users can reliably access data whenever it is needed. This depends on resilient storage systems, effective backup and recovery processes, and disaster recovery plans

The data lifecycle in ML

Here we’ll dive into the data lifecycle for machine learning, outlining what’s needed from you in every step.

1. Data requirements definition

Before you gather a single record, you need a clear, documented understanding of what data your model requires. Skipping this stage often leads teams to collect whatever is available rather than what’s actually useful. The result? Models that underperform in the real world.

When defining requirements, ask yourself:

• What population segments should be included? If your medical model should work equally well for children and older adults, you need representative data for both groups — not just one
• What rare or edge cases should the model handle? If diagnosing rare diseases, these cases can be few but essential
• How much data is required to reflect real-world variation? A diverse dataset captures subtle differences in how conditions present
• What formats and structures will work with your ML approach? Text, images, sensor readings — each comes with its own processing needs

Equally important: make sure you confirm legal and ethical rights to use the data. That includes patient or user consent forms and clear documentation of why the data was collected in the first place. Time spent here prevents costly problems later. In particular when it comes to fairness, compliance and model generalizability.

2. Data collection and acquisition

Once you know what you need, the next challenge is acquiring the right data that actually meets those needs. Treat your data providers like you would any other supplier. That means applying rigorous quality checks, keeping detailed provenance records and understanding the context of the data’s collection.

Key questions to ask:

• Is the data from one site or multiple sites?
• Are collection methods consistent across locations?
• Does the dataset reflect the diversity of the target population?
• Are there gaps caused by geography, workflow or patient group differences?

For example, if all your training data comes from a single hospital, your model may unintentionally learn biases based on local procedures or equipment. Collecting from multiple sites helps reduce these risks but you still need to check for skews and imbalances in the combined dataset.

3. Version control

Data isn’t static. Over time, new samples are added, errors are corrected and structures evolve. Without proper version control, it becomes impossible to know exactly what the model was trained on — and that’s a huge problem for reproducibility and debugging. Your version control process should:

• Record the source of each dataset
• Track all changes (including cleaning, restructuring, or reformatting)
• Store metadata such as collection date, location, or equipment used

This is more than an organizational nicety, it’s essential for heavily regulated industries (like healthcare and banking), where being able to reproduce and explain a model’s behavior is a legal requirement.

For example, if a clinical AI system suddenly starts misclassifying cases, version control lets you pinpoint whether the problem came from a model change, a new data source or a preprocessing update.

4. Data preprocessing

Preprocessing is where raw data is transformed into a form your model can use. But it’s also one of the easiest places to introduce data bias if you’re not careful. Common data preprocessing steps include filtering, imputation (filling in missing values), scaling and normalization, duplicate elimination and formatting conversions.

Every step of data preprocessing needs to be intentional and documented. Key points include:

• If you filter out “outliers,” define your criteria clearly. Rare cases might be clinically important so removing them could cripple your model’s ability to handle them in the real world
• If you impute missing values, choose context-appropriate methods. In healthcare, “missing” itself can be a meaningful signal
• Never let technical teams preprocess data in isolation. Involve domain experts so that important nuances are preserved.

Think of data preprocessing as the bridge between raw reality and model-ready information, built with equal parts technical precision and subject-matter insight.

5. Labelling and annotation

In supervised learning, labels are the ground truth your model learns from. If those labels are wrong or inconsistent, your model will inherit those flaws — and amplify them. Best practices for labelling include:

• Use domain experts whenever possible. A radiology AI trained on labels from non-radiologists is a recipe for unreliable predictions
• Create detailed annotation guidelines so that multiple annotators apply the same logic
• Track annotator details. Things like who labelled what, under what conditions, and with what incentives
• Run consistency checks across annotators to detect disagreements

Every label is a decision about how the model will interpret reality. If you get this stage of the data lifecycle wrong, there’s no amount of algorithmic sophistication that can fix it later.

6. Data splitting

The way you split your dataset can make or break your model’s evaluation. A poor split can inflate performance metrics in development, only for the model to fail in production. Typically, ML datasets are divided into:

1. Training set to fit the model
2. Validation set to tune hyperparameters
3. Test set to evaluate final performance

In healthcare, regulators like the FDA often use the terms training, tuning and test sets. Whichever terminology you use, be consistent and clear. It’s advisable to avoid random splits when your data has temporal or institutional structure. Random splitting can cause data leakage, which is where similar or related samples appear in both training and test sets, leading to unrealistically high accuracy. Instead:

• Use time-based splits for timestamped data
• Use site-based splits for multi-institutional data
• Apply sampling strategies to balance class representation

The goal is to get evaluation metrics that reflect real-world performance and not just lab conditions.

7. Data retention and disposal

Once your model is deployed, your responsibility for data doesn’t end. You need clear policies for how long the data is kept, who can access it and how it’s eventually disposed of.

Retention requirements vary based on factors like whether your product qualifies as a medical device or which privacy laws apply (e.g., GDPR, HIPAA). Your retention and disposal plan should cover:

• Retention period for each dataset type
• Access control measures and audit logs
• Secure deletion methods when data is no longer needed
• Consistent encryption and security for both live and archived data

Handling this stage well reduces regulatory risk, prevents unauthorized access and maintains trust with users and partners.

Why data lifecycle management matters

A machine learning model is only as good as the data it’s trained on. And that data’s quality depends on how it’s managed across its entire lifecycle.

From defining clear requirements to securely disposing of data years after deployment, every stage carries its own risks and opportunities. The best-performing, most trustworthy models come from teams who approach data management as a discipline, not an afterthought.

Done right, this process improves accuracy and strengthens fairness, compliance and long-term product safety. In the world of machine learning, algorithms get the spotlight but data is the real star. Manage it well and your models will thank you.