For data to be fully utilized by AI and machine learning models, it needs to meet several requirements:
Quality: The data must be of high quality and free from errors, missing values, and inconsistencies. This will ensure that the models generate accurate results.
Quantity: The data needs to be sufficient in quantity, covering a diverse range of scenarios, in order to allow the models to generalize and make accurate predictions.
Relevance: The data must be relevant to the task the model is being used for. For example, in healthcare, the data must pertain to patient medical history, demographic information, and outcomes.
Format: The data must be in a format that can be easily processed by machine learning algorithms. This often involves cleaning, transforming, and normalizing the data to remove any biases or irrelevant information. This is why master data management is critical to leverage ML
Labeling: For supervised learning, the data must be labeled with clear and accurate ground truth information.