The 7 most common errors in industrial data (and how to avoid them)

Byjthibaut

In industry, industrial data has become a key lever for performance: predictive maintenance, process optimization, cost reduction…

But in practice, many engineers find themselves facing a simple problem:

👉 Data is difficult to work with.

Not because it’s complex, but because it contains errors.

In this article, we’ll look at the 7 most common errors in industrial data and, more importantly, how to avoid them.

❌ 1. Missing Data

This is one of the most common problems.

Faulty sensors, communication losses, recording errors…

Results:

  • Gaps in time series
  • Biased analyses

👉 Solution:

  • Automatically detect missing values
  • Use appropriate interpolation methods

❌ 2. Inconsistent units

Classic example:

  • Temperature in °C in one file
  • Temperate in °F in another

👉 Result: interpretation error

👉 Solution:

  • Standardize unit
  • Document the data

❌ 3. Misaligned timestamps

Very common in industry:

  • Data from multiple systems
  • Different frequencies

👉 Result:

  • Impossible to cross-reference data correctly

👉 Solution:

  • Re synchronize time series
  • Define a common frequency

❌ 4. Noisy data

erreurs dans les données industrielles

Sensors:

  • Vibrations
  • Perturbations
  • Instability

👉 Result:

  • False signals
  • Unreliable models

👉 Solution:

  • Filtering (moving average, etc…)
  • Anomaly analysis

❌ 5. Manual modifications (Excel)

Very common:

  • copy-paste
  • quick corrections
  • modified columns

👉 Result:

  • lost of traceability

👉 Solution:

  • automate processing
  • limit manual manipulations

❌ 6. Ambiguous variables names

Examples:

  • T1, Temp, Temperature, Temp_A

👉 Result:

  • confusion
  • analysis errors

👉 Solution:

  • clear nomenclature
  • naming standard

❌ 7. Lack of documentation

Often overlooked.

👉 Result:

  • Unable to understand the data
  • Dependence on certain persons

👉 Solution:

  • Systematically document
  • Create dataset sheets

🎯 Key takeaways

In most industrial projects:

👉 80% of the work = data preparation
👉 20% = actual analysis

Engineers who master data quality gain a significant advantage.

🚀 Going further

If you regularly work with technical data, start with something simple:

👉 Identify a repetitive task in your work and then look for ways to improve it.

I’ve created a 30-day challenge to help you do just that:

👉 Automate a repetitive task using your data

👉Click here to join: 30-days challenge