Weighted Average and the Importance of Precision of Data Transformation

This post intends to showcase the importance of ensuring precision while transforming the data in ELT pipelines using simple average and weighted average as examples.

 

Importance of Precision

Precision is important in any kind of data transformation in an ELT pipeline. This is because errors in the transformation could lead to problems in other models which rely on this transformed data. 

 

In the context of averages, we cannot indiscriminately use simple averages as this may not always be representative of the whole population. Thus, simple averages are kept for segment data while weighted averages are used in entire populations.

 

Simple Average

The simple average is determined by summing up all the values in a dataset and then dividing that sum by the number of values in the set. 

 

For instance, if we have values 10, 20, and 30, the simple average would be calculated as follows: (10 + 20 + 30) / 3 = 20. 

 

This method assumes that each value contributes equally to the calculation and is often used as a representative value for a dataset. For example, it can be used to find the average monthly revenue, where each month’s revenue is unique and contributes only one value.

 

Weighted Average

On the other hand, the weighted average takes into account the importance or weight of each value in the dataset. 

 

To find the weighted average, we multiply each value by its corresponding weight, sum up these products, and then divide the result by the sum of the weights. 

 

For example, if we have values 10, 20, and 30 with respective weights 1, 2, and 3, the weighted average would be calculated as follows: (10 * 1 + 20 * 2 + 30 * 3) / (1 + 2 + 3) = 110 / 6 ≈ 18.33. 

 

This method is employed when certain values occur more frequently than others, making it unfair to treat all values with equal weight. For instance, it is useful in calculating the average unit price, where different prices are used varying numbers of times.

 

Differences of Simple and Weighted Average

The key difference between the two types of averages lies in the inclusion of weight. In the simple average, all values are assumed to have the same weight (usually a weight of 1), whereas in the weighted average, values can have different weights, reflecting their varying degrees of importance.

 

Which to Use

When deciding which average to use, there is no one-size-fits-all formula; it requires critical thinking and consideration of the specific situation. 

 

As a general guideline, if all data points have equal importance, the simple average is appropriate. For example, raw order data, where each order appears only once, would be suitable for the simple average calculation. However, if some data points occur more frequently than others, a weighted average should be employed to reflect the varying degrees of importance. For instance, when comparing prices with different usage frequencies, using the weighted average ensures a fair representation of the overall data.

 

Beyond Averages

Precision is a crucial focus not only when dealing with averages and weighted averages but across various calculations. It is essential to recognize that different types of data may require distinct approaches to achieve accurate results, necessitating critical thinking and discretion in the calculation process.

 

For instance, consider finding the daily retail price. While non-retail orders may be included as valid orders, their prices could be considered outliers or abnormalities that need to be removed to ensure precise calculations. Removing such outliers helps maintain accuracy and reliability in the final result.

 

In any situation where uncertainty arises, it is always prudent to seek clarification and consult with the team. Collaboration and open communication can lead to better decision-making and help ensure that the chosen calculation method aligns with the project’s objectives.

 

In conclusion, precision should be at the forefront of all data calculations, extending beyond just averages and weighted averages. Each specific calculation may demand its unique treatment, and critical thinking is crucial in choosing the most appropriate approach for accurate outcomes.