- Ensuring Data Quality: Data quality is critical for meaningful analysis. Consider factors such as data accuracy, completeness, and consistency during the instrumentation design process. Well-defined validation mechanisms should be implemented to ensure that the data being collected is reliable and usable for decision-making.
- Noise Reduction in Data Pipelines: Raw data often contains noise—extraneous information that may obscure meaningful patterns for a given use. Part of instrumentation design is considering what data are needed for what purposes and what questions will be answered using the raw data vs. what analytics will require filtering and processing in the data pipeline.