17  Challenges

Real machine data is often messy and requires substantial preprocessing before it can be used for analysis. Challenges from a data engineering perspective that often arise include:

• (Temporary) sensor failures
• Data gaps due to communication issues or buffer overflows
• Incomplete data
• Datatype inconsistencies
• Lack of documentation
• High data volume

Once data is collected and transformed, additional challenges may arise during analysis:

• Noisy or inconsistent measurements
• Outliers or anomalies in the data
• Variability in operating conditions
• There may be variables that are not captured in the data but still relevant for the process
• Complex relationships between variables
• Lack of labeled data for supervised learning
• Highly imbalanced datasets
• Data privacy concerns

Until now, we have primarily focused on static analysis of historical data.

However, many industrial applications require real-time data processing and analysis to enable timely decision-making and process optimization. Complexity increases significantly in real-time scenarios due to the need for low-latency processing, the handling of streaming data, and possibly the integration of data from multiple sources.