We have previously explained the importance of data collection for any AI/ML system. Good data collection relies heavily on quality assurance. Considering the challenges attached to collecting data and the size of modern datasets, it is highly possible to overlook the quality aspect of collecting data for your AI/ML project. Whether you are working with AI data collection services or collecting the data by yourself, quality assurance is crucial.
This article discusses the quality assurance stage of a data collection process. We explain what quality assurance means for data collection, why it is important while collecting data, how it differs from data quality control, and what the characteristics to consider to ensure data quality.
What does quality assurance mean for data collection?
Quality assurance in data collection is a crucial process that primarily focuses on certifying the data being accumulated for a dataset is of superior quality before it is stored in a designated database. This procedure is essential because the quality of the data directly affects the accuracy and effectiveness of the resultant AI and ML models.
The process involves careful examination of the newly acquired data, scrutinizing it for potential defects, inconsistencies, inaccuracies, missing data, or any other issues that may compromise the integrity of the data.
Data quality assurance (DQA) vs. data quality control (DQC)
Data quality assurance (DQA) plays a vital role in making sure that the data collected or being collected for the purpose of training AI and ML models is of high quality. In other words, for quality assurance, you review data while collecting it.
On the other hand, data quality control focuses on identifying and rectifying any data errors, defects, and inconsistencies that may exist in a pre-existing dataset.
Figure 1. DQA vs. DQC
Why is quality assurance in data collection important?
Quality assurance is important in data collection procedures since it impacts the performance of the whole AI/ML model.
If the data collected is of high quality, the AI model will:
- Have a reduced level of bias
- Not be overfitting/underfitting
- Have a smooth training process
- Higher level of accuracy and performance
- Provide fewer false positives and erroneous results
Processing unstructured and raw data for quality can be challenging, and not all businesses have the budget or resources to purchase an expensive data collection instrument or hire a dedicated team.
What are the attributes of high-quality training data?
This section highlights some characteristics of quality training data and what impacts the quality of a dataset.
One way to ensure data quality is to keep it relevant and aligned with the scope of the model. All data that is irrelevant should be cleaned in the preparation stage. For instance, if a quality control computer vision system needs to analyze apples, it should be trained with all variations of apple pictures. Pictures of any other object would be irrelevant and might confuse the model.
The data should be complete and must cover the entire scope of the project. For instance, a product recommendation system must be trained on all types of relevant data, including purchase history, product image, sales data, customer lifestyle data, demographic data, etc. Any shortcomings will result in incomplete performance.
The data should also be up-to-date and reflect the present situations for it to be of high quality. For instance, in the healthcare sector, there are development and advancements every day; therefore, AI solutions, such as medical vision, needs to be updated with fresh data frequently. For instance, to ensure data quality, newer radiology imaging devices can provide sharper images of higher definition. Similarly, as new types of illnesses are discovered, newer image data of those illnesses can be used to improve and further train the medical vision model.
Uniformity and consistency are also important factors to ensure data quality. For instance, if a facial recognition system requires 50 face images in dark light with a specific resolution level, they should all have these attributes. Data annotation should also be done consistently in terms of the accuracy of the labels and tags. Since facial recognition systems require large datasets, consistency in their annotation is paramount in order to maintain the dataset quality level.
5. Validity or data integrity
Another important characteristic to look out for, to ensure data quality, is the validity of the data. The data being used should be reliable and authentic. For example, if a facial recognition system requires images of a man with dark skin color, then the dataset should consist of such images. The images should not be digitally modified or should not have been taken in inorganic circumstances in the first place. (see Figure 2).
Figure 2. Authentic vs. modified images
Considering the attributes mentioned earlier while screening external or internal datasets can help improve data quality for training and improving your AI/Ml models. This ultimately translates into high-performing AI/ML models with fewer errors. If you are outsourcing your data collection tasks, make sure to properly communicate your quality standards to the service provider to ensure that they are aligned with your expectations.
Also, if data collection is a regular part of your business operations and requires a dedicated team, ensure a culture of quality assurance is followed in the team/department.
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