If the data is inaccurate, the model cannot function properly. While you might end up with a manageable model, it won't be as functional as it should be. It can be said that data quality is the top priority in machine learning model training. No matter how much data is provided to the model, if the data is not applicable, it will do nothing to improve the performance of the machine model. In short, using poor quality data is wasting valuable time and budget. . It's like that old adage, training makes perfect. In the world of data, high-quality data can create "perfection", while low-quality data will only be futile. If an aircraft cannot meet all the necessary quality testing standards, no one will dare to fly. Why don't we apply the same reasoning to the data acquisition of AI projects? As the world's leading AI lifecycle data provider, we released the annual "AI and Machine Learning Panorama Report" . The second key takeaway from this year's report is the focus on data quality. We talked about the findings in our report, and more than half of respondents said data accuracy was critical to the success of AI projects, but only 6% said data accuracy was above 90%.
The Importance of Data Quality
"Data accuracy is critical to the success of AI and ML models, as high-quality data leads to better model output and consistent processing and decision-making. To achieve good results, datasets must be accurate, comprehensive, and scalable." ——Wilson Pang, Chief Technology Officer As technology continues to be updated, new features and innovations emerge in an endless stream, and the demand for more machine learning models is also increasing. These models need to be trained quickly and accurately, therefore, they need to have high-quality data input from the beginning. This is the data acquisition phase, or phase one, of the AI life cycle. If the quality of data obtained is not high, model training will make mistakes, or even fail completely. To ensure high quality data, we need to consider some key conditions:
- Data is accurate and meets quality objectives
- The data contains relevant information needed by the machine learning model
- The dataset is complete and has no missing values
The easiest way to ensure that the above conditions are met is to inspect the data during data acquisition and training. By establishing a system of checks, you can ensure that data conforms to specific labeling standards and contains all necessary information. There should be checks at all stages of the project so that if a new data source with higher quality is required, it can be found quickly.
Data Quality Challenges
Obtaining high-quality datasets can be extremely challenging. Fifty-one percent of respondents believe that data accuracy is critical to their AI use cases, and 46% believe that while this is important, it can be worked around. Ensuring the data is of the highest quality is not difficult. Having checking systems in place to ensure the correctness of the data used to train models is critical to the success of AI projects. For businesses that do not have this resource in-house, a third-party vendor that can properly feed machine learning models with the right data is needed. We collect the high-quality data you need, annotate the data on your behalf, and get the data you need right the first time while meeting the project budget and schedule you set. Our findings show an encouraging change in the average time spent preparing and managing data, from 53% in 2021 to 47.4% in 2022. This shows that many companies are taking strict measures at the beginning of AI projects to ensure high quality from the beginning. The survey results also show that most enterprises are using third-party professional companies for data acquisition and preparation, which is another measure to avoid the risk of low-quality data.