AI is reliant upon data to acquire knowledge and drive decision-making processes. Therefore, the data quality utilized for training AI models is vital in influencing their accuracy and dependability. Data noise in machine learning refers to the occurrence of errors, outliers, or inconsistencies within the data, which can degrade the quality and reliability of AI models. When an algorithm interprets noise as a meaningful pattern, it may mistakenly draw generalized conclusions, giving rise to erroneous outcomes. Therefore, it is vital to identify and remove data noise from the dataset before initiating the training process for the AI model to ensure accurate and reliable results. Below is a set of guidelines to mitigate data noise and enhance the quality of training datasets utilized in AI models.
AI collects the relevant data from various sources. Data Quality checks and supporting rules will make sure all the details needed for organizing and formatting it are there so that AI algorithms can easily understand and learn from it. There are several widely used techniques for eliminating data noise. One such technique is outlier detection, which involves identifying and eliminating data points that deviate significantly from the rest of the data. Another technique is data smoothing, where moving averages or regressions are applied to reduce the impact of noisy data and make it more consistent. Additionally, data cleaning plays a crucial role in removing inconsistent or incorrect values from the dataset, ensuring its integrity and reliability. Data professionals can perform Data profiling to understand the data and then integrate the cleaning rules within data engineering pipelines.
Proper data validation is mandatory for even the most performant algorithms to predict accurate results. Once the data is collected and preprocessed, validation against reference values that have been tried and tested on various occasions would enhance confidence in data quality. This step involves checking the training data for accuracy, completeness, and relevance. Any missing, incorrect, or irrelevant data found is to be corrected or removed.
One such check is the field length check, which restricts the number of characters entered within a specific field. Phone numbers are an example where any number entered with more than ten digits needs correction before being used for prediction models. Another important check is the range check, where the entered number must fall within a specified range. Consider a scenario where you possess a dataset containing the blood glucose levels of individuals diagnosed with diabetes. As customary, blood glucose levels are quantified in milligrams per deciliter (mg/dL). To validate the data, it is imperative to ascertain that the entered blood glucose levels fall within a reasonable range, let’s say between 70 and 300 mg/dL.
A range check would establish a restriction, enabling only values within this range to enter the blood glucose level field. Any values that surpass this range would be promptly flagged and corrected before using in the training dataset. This meticulous validation process ensures the accuracy and reliability of the blood glucose data for further analysis and decision-making. Additionally, the present check must ensure data completeness, meaning a field cannot be left empty. For example, a Machine Learning algorithm to predict package delivery performance must implement a completeness check to verify that each package detail in training data records valid values of the customer’s name, shipment origin, and destination address.
Monitoring Data Quality Over Time
Regular monitoring is crucial for maintaining data quality in an AI system. As new data is collected and integrated into the system, it is essential to continuously assess the data’s accuracy, completeness, and consistency. The AI system can operate with precision and dependability by upholding high data quality standards. Various metrics for monitoring data quality, including accuracy, completeness, and consistency, highlight any change in data quality.
The accuracy metric evaluates the alignment between the data and reality, such as verifying the correctness and currency of customer addresses. The completeness metric measures the extent to which the required data is present in the dataset, such as ensuring that each order record contains all the necessary field values. The consistency metric examines the adherence of the data to established rules or standards, such as checking if dates follow a standard format. The AI system can maintain its accuracy and reliability over time by consistently monitoring these metrics and others.
Implementing these techniques allows AI systems to improve the quality of training datasets for AI models, resulting in more accurate and superior decision-making reliable outcomes.