Writing good SQL is the key to making insightful BI dashboards. The query structure should be organized according to granularity, such as grouping and aggregating indicators with time, region, and product categories to facilitate slicing and filtering; the index definition should be unified to avoid conflicts in different chart data, it is recommended to establish a unified model or view to encapsulate core indicators; flexibly process the time range, use parameterized conditions or relative time expressions to facilitate user-defined period comparison; data preprocessing should be completed in the SQL layer, such as classification renaming, null value processing, etc., to improve the efficiency of BI tools and ensure consistency.

When working on business intelligence (BI) dashboards, SQL is one of the most basic and critical tools. Whether you can extract valuable indicators from the data directly determines whether the dashboard is useful. Simply put: Only by writing SQL well can you create an insightful BI board .

The following parts are key points that are often encountered in actual work but are easily overlooked.

How to organize your query structure

BI dashboards usually require multiple dimensions and indicators to be presented in combination, and the structure of SQL is very important at this time. A common practice is to organize queries according to "grain size", such as first GROUP BY time, region, product category, and then aggregate to calculate sales, number of users, etc.

For example:

 SELECT 
    date,
    region,
    product_category,
    SUM(sales) AS total_sales,
    COUNT(DISTINCT user_id) AS unique_users
FROM orders
GROUP BY date, region, product_category

This structure facilitates you to freely slice in BI tools, and also facilitates the subsequent addition of filtering conditions or sorting logic.

suggestion:

• Use the time field as the default grouping item to facilitate trend analysis.
• Try to keep each query only one granularity level to avoid mixing aggregation at different levels.
• Use CTE or subqueries to split complex logic and improve readability.

The indicator definition should be unified, don't let the caliber fight

The problem with many BI boards is not that the charts are not good-looking, but that the same "sales" value is different in different charts. This is usually caused by inconsistent metric definitions in SQL.

For example, some places use SUM(order_amount) and some places use SUM(CASE WHEN status = 'paid' THEN order_amount ELSE 0 END) , and the result is that the data cannot match.

Solution:

• Establish a unified data model or view layer and encapsulate core indicators.
• Clearly define common terms within the team, such as "effective order" and "active user".
• If the caliber must be temporarily modified, it should be explained clearly in the comments to facilitate subsequent maintenance.

Time range processing should be flexible and accurate

BI Kanban often needs to compare today vs yesterday, this week vs last week, this month year-on-year, etc. If these time ranges are written in a rigid way, it will be very troublesome to adjust them later.

A common practice is to use parameterized time conditions in the WHERE clause, or to set dynamic variables to pass in in the BI tool.

For example:

 WHERE date BETWEEN '{{start_date}}' AND '{{end_date}}'

If you are using tools like Tableau, Power BI, or Metabase, they support custom SQL and bind variables, allowing users to choose their own time range without changing SQL every time.

Tips:

• Calculation of reserved "relative time", such as date >= CURRENT_DATE - INTERVAL '7 days' .
• For comparison periods, you can use LEFT JOIN yourself and stagger them by date for a period of time to compare.
• Pay attention to time zone issues, especially cross-regional data.

Data preprocessing can save a lot of trouble in BI tools

Many people like to clean and convert data in BI tools, but in fact, the more complex the logic, the more it should be done in the SQL layer. For example, classification renaming, empty value filling, status mapping, etc., process it in SQL in advance, and the chart configuration will be smoother.

for example:

 SELECT 
    CASE 
        WHEN product_id IN (101, 102, 103) THEN 'Electronics'
        WHEN product_id IN (201, 202) THEN 'Household Products'
        ELSE 'Other'
    END AS product_category

There are several benefits to doing this:

• There is no need to write a bunch of CASE expressions when configuring the chart.
• It can reduce the performance pressure of BI tools.
• It is easier to reuse, and multiple kanban boards can share a set of underlying SQL.

Writing SQL for BI boards does not pursue accuracy in rows and columns like writing reports, but requires flexibility and accuracy. With clear structure, unified caliber, controllable time and clean data, these four points are basically the same.

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