PMI-CPMAI explains that modern AI projects often work with high-volume, high-variety data, including both structured (tables, logs, telemetry) and unstructured formats (text, documents, images). A core principle in the data preparation and pipeline design stages is that “variety must be explicitly addressed through normalization, harmonization, and feature extraction so that models receive coherent, compatible inputs.” If the project manager ignores the variety dimension—treating all data as if it were homogeneous—this typically leads to misaligned schemas, inconsistent encodings, missing modalities, and improperly handled unstructured content.
The guidance notes that such issues “manifest as degraded model performance, instability, and reduced generalizability, even when volume and velocity are adequately managed.” In a fleet management context, failing to harmonize telematics, maintenance records, driver logs, and external data (e.g., traffic or weather) means the model cannot fully capture relevant patterns, and some signals may be effectively unusable or misleading. Rather than improving accuracy or consistency, skipping this work undermines the quality of features, increases noise, and introduces hidden biases.
As a result, PMI-CPMAI indicates that not addressing data variety during preparation will most directly lead to reduced model performance, because the model is trained and evaluated on incomplete, inconsistent, or poorly integrated representations of the underlying operational reality.
