Do You Really Need More Data for Machine Learning?

In Synaptiq’s recent webinar, Making AI Work When You Don't Have Enough Data, Dr. Tim Oates, Co-founder and Chief Data Scientist, tackled one of AI’s most persistent myths: that large datasets are always needed for an AI or machine learning initiative. While data is the fuel of machine learning, a full tank isn’t always necessary. The “right” amount of data depends on the task, the quality of information you start with, and the expert guiding the project.

Supervised Learning in Plain Terms

At the heart of this discussion is supervised learning—the most widely used approach in machine learning. It’s built on labeling data. For example:

• Emails tagged as important or not important

• Bank transactions labeled fraudulent or not fraudulent

By studying these labels, the model learns to recognize patterns and apply them to new, unseen data.

Why Data Requirements Aren’t One-Size-Fits-All

The number of examples you need to train a model depends on several factors:

• Domain knowledge: The more you already know about the problem, the less raw data you’ll need.

• Problem difficulty: Straightforward tasks demand less data, while complex ones require more.

• Team expertise: Skilled data scientists can squeeze far more out of small datasets.

Common Data Challenges

When Data Is Scarce

A lack of data doesn’t have to stall progress. Teams can get creative with:

• Transfer learning: Building on the work of pre-trained models.

• Open-source datasets: Borrowing from high-quality, publicly available sources.

• Data augmentation: Generating new examples by rephrasing, flipping, or tweaking existing ones.

• Web scraping: Collecting supplemental examples from online sources.

When Labels Are Scarce

Many organizations have plenty of raw data but not enough labels. To bridge that gap:

• Self-training: Let the model confidently label easy examples.

• Transfer learning: Reuse already-trained models for new tasks.

• Self-supervised learning: Learn from unlabeled data first, then fine-tune with a small set of labels.

• Active learning: Have humans label only the most challenging cases.

When No Data Exists

Even without any data, solutions exist: 

• Zero-shot image classification: Teaching models to match images with descriptions using encoded text.

• Zero-shot document classification: Using large language models to organize documents into categories when given a description of said document.

What Businesses Should Remember

• More isn’t always necessary—but it rarely hurts.

• Expertise matters most when data is limited.

• Few data points? Lean on pre-trained models, open-source sets, and augmentation.

• Few labels? Explore active learning, self-training, or self-supervised methods.

• No labels? Zero-shot techniques can still deliver meaningful results.

The bottom line

Success in AI isn’t just about how much data you have—it’s about how you use it. With the right methods and the right people, even small or imperfect datasets can unlock real business value.

This article only scratches the surface of Dr. Tim Oates’ insights on making AI work when data is limited. In the full webinar, he dives deeper into practical strategies, real-world examples, and the minute details of when “less” data can actually be “enough.”

Watch the recording here to gain a richer understanding of how to maximize the value of your data, no matter the size of your dataset.