In May 2021, Privacy International (PI) filed a legal complaint against Clearview AI. The complaint? That Clearview was scraping billions of facial photos from the web in an attempt to bolster their database for machine learning.
In their own marketing, Clearview describes a database of over three billion facial images. But their drive to grow this database further is testament to one big challenge for AI and machine learning: there’s never enough data.
All of which is to say: even the biggest businesses struggle to get data at the incredible scale they need. Machine learning with limited data is difficult. And every data scientist needs effective – and responsible – ways to find or generate training data.
Read on for five tips on dealing with a machine learning lack of data – and learn how to increase your data set without compromising on quality or accuracy.
Why do you need so many data points in machine learning?
For many years, machine learning was an evolving technology – a topic for exploration and improvement. In this context, minor inaccuracies were inconsequential. Today, machine learning is being used in critical settings and the stakes are higher than ever before.
In finance, for example, banks are already using machine learning for advanced fraud analytics. Here, a machine learning model’s inaccurate observations could come with severe, potentially irreversible consequences. Worse, these consequences become incredibly expensive as machine learning is used on a higher volume of accounts, transactions and business areas.
The scale and accuracy of your data set in machine learning is more important than ever before. So how do you create more data points quickly, accurately and to infinite scale?
#1 Find more real-world data
The first answer is perhaps the most obvious: get out there and capture more data. Of course, every data scientist or developer knows just how difficult this can be.
The reality is that every data point comes at a cost. Most data is captured manually at an expense that just doesn’t make sense when you’re dealing with data on a huge scale.
At the same time, much of the most valuable data you collect in-house is likely to be protected by privacy regulations like GDPR. Data scientists don’t just need to find more records. They need accurate data that they’re free to use as a data set to train machine learning models.
#2 Manually label more of your data
An alternative approach is to drive more value from the data you already hold. Most datasets for machine learning are in fact subsets of a larger original source – one that’s often full of errors, duplicates, or unlabeled records.
Manually labeling more data can increase your data points for machine learning. However, there must always be a balance between investment and value. Data preparation is resource-intensive. For most organizations, a small increase in available data just isn’t worth the time it takes to manually label it.
#3 Supplement your data with estimates
Most data sets include records with missing values. In the case of numerical values, you can estimate the missing ones in order to create usable records.
Many data scientists complete their data with mean or median values. However, this impacts the quality of your machine learning model considerably.
The accuracy of your data points for machine learning fundamentally influences the quality of your models and predictive capabilities. When you estimate values, you’re building in inaccuracy from the outset.
#4 Predict missing values automatically
Another approach to missing values is using AI to predict and complete them automatically. In this approach, your algorithm will:
- Analyze your complete data to identify trends in its values
- Create predictive values that follow the statistical properties of your wider data
Of course, this still depends on having enough data in the first place. You can’t auto-complete values to a high degree of accuracy if you don’t have enough data for your model to make fair predictions.
Predicting missing values is a first step into supplementing your data – but it misses a lot of the real value that newly generated data brings to the table.
#5 Create balanced data at any scale
Often, data scientists don’t struggle to find data – they struggle to find high quality, balanced data on the scale they need. With synthetic data, you can replicate the characteristics of an existing source dataset, generating more data from a minor class that’s well-balanced.
As a result, you can create new data of limitless scale, complexity, variety and balance. All while breaking free from privacy restrictions around how you can actually use that data.
As the need for unbiased, labeled data becomes more commonplace, data scientists are all looking for fast, agile ways to supplement the data they hold. But it’s not just about scale or creating new records.
Whatever the scale of your requirements, nothing is more important than building in accuracy from the outset. With synthetic data and a way to generate new records that are unbiased and accurate, you don’t get just scale – you get superior insights.