Preparing your data for machine learning: Top 6 best practices

If you’re working with machine learning, one thing becomes clear fast: Your model is only as good as the data you feed it.

While algorithms often steal the spotlight, it’s the behind-the-scenes work, like getting your data clean, consistent, and ready, that truly drives success. Most data scientists will tell you that data preparation takes up the majority of their time on any machine learning project.

In this article, we’ll walk through the best practices for data preparation for machine learning - Why it matters, how to do it well, and how a solution like DataGalaxy can make your workflow much simpler and smarter.

What is data preparation?

Let’s start with the basics. Data preparation is the process of turning raw, messy data into something your machine learning models can actually use.

This usually means:

• Collecting the right data
• Cleaning it up
• Filling in missing information
• Structuring it properly
• And making sure everything’s labeled and consistent

It may not sound glamorous, but this stage is absolutely critical. If your data is off, your model results will be too.

Why data preparation is so important

Think of data preparation as laying the foundation for a building. If the base isn’t solid, it doesn’t matter how fancy your design is. In the end, everything will collapse.

Here’s why solid data prep matters:

6 best practices for data preparation in machine learning

Let’s break down the key steps to getting your data ML-ready and how to do them right.

1. Gather your data smartly

Start by identifying all the sources of data you’ll need. This might include internal systems (like CRMs or databases), third-party sources, or real-time feeds.

Tips:

• Make sure your sources are reliable and up-to-date
• Use automated tools or APIs to streamline the process
• Collect metadata along with your data—it’ll help later

2. Understand what you’ve got

Once you’ve collected your data, take time to explore it. This step, known as data profiling, helps you understand its structure, values, and any red flags.

Tips:

• Look for missing values, outliers, or inconsistencies
• Visualize distributions to spot weird patterns
• Document any quality issues early on

3. Clean it up

Now it’s time to fix problems. This step might involve removing duplicates, correcting errors, or filling in blanks.

Tips:

• Use logical or statistical methods to handle missing data
• Normalize extreme values that might skew results
• Stick to business rules or known standards for consistency

4. Transform the data

Machine learning models like numbers, not messy text. This is where you convert and reshape your data into a format that the model can work with.

Tips:

• Use encoding techniques for categorical variables
• Normalize or scale numerical values
• Create new features (AKA feature engineering) to help the model learn better

5. Label your data (if needed)

For supervised learning models, labeling your data is a must. Ensure your labels are accurate, consistent, and easy to read.

Tips:

• Use clear criteria for labeling
• Get human reviewers involved when necessary
• Keep track of who labeled what, and how

6. Split it up

Don’t forget to split your dataset into training, validation, and test sets. This ensures your model is trained and appropriately evaluated.

Tips:

• Use random or stratified sampling to ensure balance
• For time series data, keep things in chronological order
• Make sure there’s no overlap between datasets to avoid bias

Mistakes to avoid

Even experienced teams run into common data prep mistakes. Here are a few to watch out for:

Why data governance is a must

Machine learning projects aren’t just about data science—they also require data governance.

That means:

• Knowing where your data comes from (lineage)
• Making sure it’s consistent and high-quality
• Tracking who can access it and how it’s being used
• Complying with regulations like GDPR or HIPAA

Without governance, even the best-prepared data can cause problems, especially at scale.

How DataGalaxy helps you prepare data smarter

As a full-service data management and governance platform, DataGalaxy makes it easy to prepare your data for machine learning without the mess or manual effort.

Discover & understand your data

With a central data catalog, you can easily find and explore all your datasets in one place. No more digging through spreadsheets or asking around.

Trace data lineage automatically

See exactly how data flows from source to model. This helps you trust your inputs, track changes, and debug issues faster.

Share a common language

DataGalaxy’s business glossary ensures everyone across your organization uses the same terms. No more confusion over what a “customer” really means.

Work better together

Built-in workflows, comments, and task tracking make collaboration seamless across data engineers, stewards, and scientists.

Stay in control

With role-based permissions and compliance tools, you can enforce policies and track every data change for audits.

Final thoughts

Getting your data ready for machine learning isn’t always easy, but it’s one of the most important things you can do for your AI projects.

By following best practices and investing in good governance, you give your models the best chance to succeed. And with a platform like DataGalaxy, you can make the process easier, faster, and more reliable from end to end.

Want to spend less time wrangling data and more time building great models? Let DataGalaxy be your partner in innovative, scalable, and secure data preparation for machine learning.