sql-weighted-mean

SQL and BI: The Importance of Weighted Mean in Data Analysis

Data analysis is a critical component of any successful business, providing valuable insights that inform and guide key decisions. With the vast amounts of data being generated every day, having the right tools and techniques at hand to effectively analyze that data is more important than ever. This is where SQL, a powerful and flexible data management language, comes in.

In this blog, we’ll explore the art of data analysis with SQL and show how using weighted mean can bring new levels of insight and understanding to your data, highlighting trends and patterns that can inform and drive your business forward.

👇 Check out the practical work example at the end of the blog.

How does weighted mean enhance the understanding of data trends and insights?

Here you have 5 points about the role weighted mean plays in elevating the level of data analysis in businesses.

1. Contextual Relevance of weighted mean

Weighted mean takes into consideration the relative importance of different data points in calculating the average, making it more suitable for business reports where data with more weight or significance needs to be emphasized.

2. Accounting for Data Inequality

In business, data is not always distributed evenly, and weighted mean accounts for this by assigning more importance to data that is more relevant to the report.

3. Making Comparisons

Weighted mean can be used to compare data sets that have unequal units or values, making it a useful tool for business reports that need to make comparisons between different data sets.

4. Better Representation of Data Trends

By accounting for the importance of data points, weighted mean provides a more accurate representation of trends and patterns in the data, making it useful for business reports that need to draw insights from data.

5. Better Alignment with Business Objectives

Weighted average allows for customization of the average calculation to align with specific business objectives, making it a more suitable choice for business reports where a more tailored approach to data analysis is required.

How does SQL support weighted mean calculations in data analysis?

When you are dealing with big volume of data, tools as Excel or Google sheets are not the most suitable for the jobs.

With SQL you can interact directly with the database and start working with the data you need.

Certainty, there are more benefits of using SQL to calculate weighted mean for data analysis, but here are 3 point that I consider relevant:

1. Flexibility and Customizability

SQL offers the ability to easily modify the calculation of weighted mean to fit the specific needs and objectives of a business. This level of customization allows for more accurate and relevant data analysis in business reports.

2. Scalability

SQL is capable of handling large amounts of data, making it suitable for business reports that need to analyze and present data from multiple sources or departments.

3. Integration with Other Tools

SQL can be integrated with other tools and software commonly used in business analysis and reporting, such as data visualization tools, making it a valuable tool for streamlined and efficient data analysis in business reports.

What steps are involved in determining the weighted average through SQL?

The formula for weighted mean calculation is:

Weighted Mean = (Σ(Value_i * Weight_i)) / (Σ(Weight_i))

where “Value_i” represents the value of each data point and “Weight_i” represents the weight assigned to each data point. The weighted mean is calculated by summing up the product of each value and its corresponding weight and dividing the result by the sum of all weights.

An example of weighted average calculation in a practical work scenario.

As a business analyst, incorporating averages into reports and utilizing them in subsequent calculations is a common requirement. Take, for instance, an ecommerce scenario where determining the average number of units sold per order for each product is essential for informed decision-making.

1. Let’s take a look at our scenario: In this example, you can see that the quantity per single order is quite dispersed.

        select
	od.order_id,
	od.product_id,
	p.product_name,
	round(sum(od.quantity)) as order_quantity,
	sum(od.unit_price * od.quantity) as sales
	
        from order_details as od
	left join products as p
	on od.product_id = p.product_id
	
        group by 1,2,3
	order by 2

2. Simple mean and weighted mean calculation: Let’s take a closer glance to the Chai product. We sold in total 828 units in 38 orders. Considering this, the simple avg quantity per order is 22 units.

To calculate the weighted average, it’s very common to consider the amount of money a product contributed to the total sales and not only the unit quantity. When we take into account the money per order to calculate the weighted mean per order, this is what we get:

with base as(
	select
	od.order_id,
	od.product_id,
	p.product_name,
	round(sum(od.quantity)) as order_quantity,
	sum(od.unit_price * od.quantity) as sales
	
        from order_details as od
	left join products as p
	on od.product_id = p.product_id
	
        group by 1,2,3
	order by 2
	)

select
	b.product_id,
	b.product_name,
	count(distinct order_id) as total_orders,
	sum(order_quantity) as total_quantity,
        round(sum(sales)) as total_sales,
	round(avg(order_quantity)) as avg_quantity,
	round((sum(sales*order_quantity)/sum(sales))) as weighted_quantity
	
        from base as b
	where product_name = 'Chai'
	group by 1,2

Conclusion

In conclusion, the use of weighted mean in business decisions is crucial as it provides a more accurate and relevant representation of data by taking into consideration the relative importance of different data points.

This type of analysis allows for a better alignment of data analysis with business objectives and provides a more accurate representation of trends and patterns in the data.

The ability to customize the calculation of weighted mean in SQL makes it a valuable tool for business analysis and reporting, providing insights that can inform and guide critical business decisions.

If you are interested in SQL, then be sure to check out this post that delves into the topic of data cleaning using SQL: go to the post