Fixing the Lack of Data: 3 Ways to Do It with Machine Learning

Machine Learning Needs a Vast Amount of Data

Machine learning needs a vast amount of data. So, the first question we ask clients is: do you have enough? You may answer ‘Yes,’ but you probably don’t have as much as you think. How can we be so sure? And how can you get more and achieve the best results? Find the answers you’re looking for in the following article.

Let’s Start with an Example

It’s always easier to grasp a concept through a real-life example, so let’s start there.

Imagine you’re organizing a party. It’s an important event, and you want to hire a photographer to capture it. You ask them to take ‘lots of photos’ because you don’t want to miss a moment: you tell them to ‘photograph it all.’

The photographer follows your instructions. They get paid — while you get a hard-drive-full of pictures.

The Problem

You lose hours trawling through the collection. You find less than a handful of photos you can develop. There will be no album. You’ve wasted thousands on unprofessional service, and what’s worse, you probably shouldn’t have ever received these photos in the first place.

What Caused the Problem?

Now, step back: What do you think caused this problem? And was there anything you could have done to avoid it?

The answer to the second question is, perhaps. As to the first, well: the photographer got a poorly-defined task as the outset. They were just told, ‘to take a lot of pictures’ — nobody said the pictures ‘must be of great quality.’

How Does This Relate to Machine Learning?

Well, building machine learning — or any software that relies on data — is not much different from the example above: how you define a task matters, particularly if you want the right quality results.

Useful Data is High-Quality Data

As was the case with your photographer, merely generating a lot of data rarely satisfies anyone’s requirements. In fact, focusing purely on quantity often means most of the data that results is useless. What’s important is the quality of the dataset, as it’s quality that determines the performance of AI software, which is the moment we understand. If your input is low-quality, your results will never meet expectations.

4 Steps to Get Good Quality Data for Your AI Software

First, let’s look at how you get the right quality data.

There are four steps, and if you follow each one in sequence, your machine learning software will give you the results you want.

Step 1: Specify Your Business Goal

This is the single most important aspect of every AI project. Think about what you want to achieve and why. Then explain it in clear, simple language to the team responsible for the build.

Step 2: Find Out What Data You Need

Next, be specific about what data you need to create a solution that matches your expectations.

Step 3: Clean Up Your Data

Now you know your goal, and you’ve identified the data you need, it’s time to eliminate all the ‘rubbish’ that could cloud your dataset.

Step 4: Work with Domain Experts

Data scientists can help you clean up your data. Other experts can help you get the rest right.

If You Don’t Have Enough Data, Here’s What to Do

When the four steps above don’t yield a big enough dataset, all is not lost. These next three steps can get you the volume your project needs.

Step 1: Consider If There’s a Hidden Dataset

If you don’t have enough data, you may have missed a hidden resource. Consult with a team of data scientists and ask them if there could be a relevant source of information that you haven’t yet thought of.

Step 2: Consider Simplifying Your Goal

When you first set out on your mission, you may have set the bar too high. Your goal may be overly ambitious, or overly complex, and so require ultra-detailed or accurate data, which you do not have.

Step 3: Consider Using Synthetic Data

There’s more than one way to collect data. An often-ignored route is to generate synthetic data.

Conclusion

You might think having access to a vast dataset is all you need to create an AI-based solution. Unfortunately, this is rarely the case. You need to analyze a dataset to understand the possibilities that lie within. And if you don’t have the right data, you need to follow one of the other three paths to get the high-quality results you want.

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