In industry, data are abundant and collected from sensors, control and monitoring systems (DCS/SCADA), production histories, quality parameters, energy consumption, machine downtimes, and more. Yet, many organizations still struggle to turn this information into concrete decisions. Missing data, outliers, inconsistencies, and a lack of analytical methodology often limit the ability of teams to identify genuine opportunities for energy and operational performance improvements.
Specialized tools like the Explore software serve to address this challenge by enabling users to structure, clean, and analyze data to derive real value for organizations.
Data quality is the first step in any effective analysis process. Explore facilitates the rapid identification of missing data, inconsistent values, abnormal minimums and maximums, impossible negative values, and outliers.
Using descriptive statistics and anomaly detection tools, users clean and validate their data before moving on to analysis. This step is essential for obtaining reliable results and making informed decisions.
Explore helps streamline data cleaning by making it easy to identify and manage anomalies, outliers, and non-representative data points. In some cases, correcting issues directly in the source file, such as replacing invalid values, is the best approach. In others, Explore can quickly exclude or filter data that could distort the analysis. Whatever the method, documenting any changes helps ensure transparency and reproducibility.
Once the data have been stabilized, Explore enables you to quickly explore the dynamics between variables, such as distributions, statistics, correlation matrices that reveal the direction and strength of relationships between variables, and PCA, to visualize operating modes and identify clusters. This visual analysis helps confirm that the observed patterns are consistent with real-world operating conditions, providing greater confidence in the overall analytical approach.
Explore is also used to identify and handle outliers via indicators (e.g. SPE and T²) through a rigorous approach: Group, investigate, then decide whether to remove or retain outliers. A key principle of effective energy management is that whenever models are updated, the same data cleaning and processing rules should be reapplied. Ideally, these rules should be documented to ensure consistency, transparency, and reproducibility.
CIET’s Industrial Data Mining with the EXPLORE Software course is designed to teach this methodology. Participants develop a structured approach to preparing, analyzing, and interpreting industrial data to identify improvement opportunities, support operational decision-making, and enhance energy performance.
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