17 Challenges
This section is still under construction and will be completed in the near future. Please do not go beyond this point for now.
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.