"5 common mistakes in industrial data analysis".

"5 common mistakes in industrial data analysis".

The following describes five common mistakes in this field:

1. ) Underestimation of data quality: Not paying enough attention to data quality can lead to erroneous analysis. Incomplete, duplicated, or incorrect data can lead to incorrect conclusions.

2. ) Lack of data normalization: In industrial environments, data can come from multiple sources with different formats. Not normalizing this data can create inconsistencies and difficulties in analysis.

3. ) Ignoring the operational context: Analyzing data without considering the operational context can result in biased interpretations. It is crucial to understand the conditions under which the data was collected for proper evaluation.

4. ) Excessive reliance on tools without human interpretation: Although analytical tools are powerful, relying solely on them without the intervention and interpretation of an expert can lead to the omission of critical details.

5. ) Do not consider the temporality of the data: Industrial data analysis must consider the temporal variations and trends over time.

   Ignoring these aspects can lead to conclusions that are not representative of the operational reality. Avoiding these mistakes is essential to develop accurate and useful analyses that can guide strategic and operational decisions in the industrial environment.