A two-sided Benefit Model

The business case for improving Data Quality is blessed by a two-sided benefit model: downside avoidance & upside realization.

Downside Avoidance

Plant data and information left unattended degrades over time in a way that is not perceptible to users.  This degradation can be caused by many factors including:

  • Meter Errors
  • Engineering Units Errors
  • Engineering Unit mismatch
  • Calculations Errors
  • I/O Timeout
  • Missing Data
  • Configuration Changes

The benefits due to downside avoidance can be very significant. Using Sigmafine will yield continuous benefits because:

  • Sigmafine automatically implements the repetitive data validation tasks required to handle the moment of data creation;
  • Sigmafine has a detection threshold for bad or poor data which results in earlier detection of biases as compared to user-based observation of data.

Upside Realization

Trustable Data is valuable. Upside value realization is based on the premise that data can be trusted, business processes become more reliable, and decisional acuity is increased because of the improved accuracy. Data Quality professionals have a rule which they call the “Rule of 10”: “It costs 10 times as much to complete a unit of work when the input data are defective as it does when they are perfect”.

Whether the factor is 10 or another number, the same logic applies to the Process industry. “Industry 4.0”  offers a great opportunity for smart plants with a low threshold for bad data/poor information to discover and learn about the importance of Data Quality.

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