Data Quality: The Better the Data, the Better the Model

If your data’s not accurate, your model won’t run...properly, that is. While you may end up with a working model, it won’t function the way it was intended. The quality of your data is arguably the most important aspect of training a machine learning model. No matter how much data you supply the model with, it won’t make much difference to performance if it’s not usable. To put it simply, poor quality data is a waste of precious time and budget. It’s akin to the age old saying, practice makes perfect. In the world of data, high quality data is perfect, and non-high quality data is just practice. You wouldn’t fly in a plane if it wasn’t tested to meet all quality standards, so why not apply that same logic to the data you source for your AI projects?

As the leading provider of data for the AI lifecycle, we release a yearly report on the State of AI and Machine Learning. Our second key takeaway of this year's report is focused on data quality. In it we discuss our survey finding that more than half of respondents say data accuracy is critical to the success of AI, but only 6% reported achieving data accuracy higher than 90%.

The Importance of Data Quality

“Data accuracy is critical to the success of AI and ML models as qualitatively rich data yields better model outputs and consistent processing and decision-making. For good results, datasets must be accurate, comprehensive, and scalable.” ~CTO Wilson Pang

With technology constantly being updated with new features and innovation, the demand for more machine learning models has increased as well. These models need to be trained quickly and accurately, which means the data needs to be of the highest quality from the start. This is the data sourcing stage, or the first stage, of the AI lifecycle. If the data you source isn’t of high quality, then the model will be trained wrong or fail completely.

Some key factors to consider to ensure data is high quality

• The data is accurate and meets quality goals
• The data contains the relevant info needed for the machine learning model
• Data sets are complete and not missing values

The simplest way to ensure the above criteria is met, is to check the data as it’s being both sourced and trained. By putting in a systems of checks, one can make sure that the data adheres to certain labelling standards and contains all of the necessary info. Checks should be happening in all stages of the project, so if a new data source that can provide higher quality is needed, it can be sourced quickly.

Data Quality Challenges

Achieving high-quality datasets has the potential to be extremely challenging. 51% of our survey participants agree that data accuracy is critical to their AI use case and 46% agree it's important but can work around it.

Ensuring that data is of the highest quality doesn’t have to be difficult. Having a system of checks to make sure the data is correct for model training is critical to the success of your AI. For companies that don’t have that capability on site, a 3^rd party vendor with the capability to ensure data being sent to the ML model is exactly what’s needed. We have the ability to gather the quality data you need as well as annotate it on your behalf You’ll get the right data the first time and be able to stick the budget and project timeline you’ve set.

One promising shift indicated in our survey results is the average amount of time spent preparing and managing data is trending downwards from 53% in 2021 to 47.4% in 2022. This signals that many are placing strict measures at the kickoff of AI projects to ensure they have high quality from the start. Results also show the majority of people are leveraging 3rd party specialists for data sourcing and preparation as another step towards reducing the likelihood of having low quality data.

Learn More about Data Quality

Data quality is critical to AI model success and industry experts share their thoughts in our 8^th annual State of AI and Machine Learning Report that you can read today to better understand the current industry trends and challenges in relation to data quality, as well as read our other four key takeaways. For further information, watch our webinar where we go in-depth on all topics covered in our State of AI Report.