Finding the right quality and quantity of data is a crucial step in training an ML model. Aspects of the model like fidelity, tolerance, and reliability largely depend on this stage. Too little data can result in a high level of approximation. It will also make data selection difficult as the rejection of anomalies and normalization, if required, will become difficult. On the other hand, large volumes of data can help build an accurate and reliable model. However, managing volumes of data will need sophisticated big data and deep learning skills. Moreover, with unstructured data like image or unconstrained text, developing labels or processing information becomes complex, resulting in longer training cycles.
Finding a right balance between the quantity and quality of data can help achieve a healthy correlation with a shorter lead time.