Plausibility
The degree to which data values match knowledge of the real world.
- •Early detection of potential data errors or anomalies that contradict real-world knowledge.
- •Increased confidence in data that aligns with expected norms and common sense.
- •Reduced risk of making absurd decisions based on clearly illogical data values.
- •Improved data quality through validation against known constraints and business understanding.
- •Data containing values that are clearly impossible or highly improbable (e.g., negative age, future transaction dates for past events).
- •Erosion of trust in data if it frequently contains implausible entries.
- •Potential for serious errors if automated systems act on nonsensical data.
- •Wasted effort investigating or explaining data that defies basic logic.
Story
Logistics: Recorded vessel speeds are within the known operational range for that specific vessel type and current sea conditions.
Healthcare: A patient's recorded height and weight are within expected human physiological ranges.
Retail: The total order value is consistent with the sum of the prices of individual items and applied discounts.
Logistics: A container move is recorded in the system as taking only 0.1 seconds, which is physically impossible.
Healthcare: A patient's recorded age is 150 years, or their recorded body temperature is 5 degrees Celsius.
Retail: An order record shows a quantity of -10 for an item, or a product price of $0.00 for a high-value item.
Positive Numeric Values Only improves 38
Date Range Validation improves 38
Temperature Range Validation improves 38
Geofence Boundary Validation improves 38
Percentage Field Range Validation improves 38
Future Date Prohibition (Transaction Dates) improves 38
Weight/Volume Consistency Check improves 38