Hello Reader,

Here's a fact of analyst life: most of the people who use your analysis will never use SQL.

Instead, they'll use spreadsheets and reports.

Your director doesn't open pgAdmin. Your marketing team doesn't "connect to the database". They open the Excel file or Google Sheet you sent them. And if the data you exported requires 20 minutes of cleanup before it's usable (reformatting dates, splitting columns, fixing number formats) you've done extra work that didn't need to happen.

You're really just putting MORE work on their plate, making it way harder than it needs to be.

The good news: you can format your query output so it's already clean when it lands in a spreadsheet.

A few SQL tricks save significant cleanup time.

Raw SQL Output (Lazy) vs. What Executives Actually Want

Here's typical raw output from a revenue analysis:

Now have a look at this version:

Same data. But notice the difference in the improved version:

• commas
• dollar signs
• percentages
• capitalization

10 minutes of extra effort goes a LONG way!

BTW, if you are interested in using Excel more effectively in your business analytics work, check these out:

• ​8 Proven Websites to Sharpen Your Excel Skills​
• ​5 Steps to Master Excel-to-SQL Conversion (Plus Code to Get You Started)​
• ​The Step-by-Step Guide to Automating Call Center Reports Using Excel and Python (Code Included)​
• ​The Six Figure Data Analytics Tech Stack: The Tools You Need To Succeed And How To Learn Each One)​

How to Add Spreadsheet-Ready Formatting to your SQL Queries

Here's a good example of how this can be done quickly and easily using the Summit Adventures database (the fake adventure tourism company I created to help people learn business analytics):

-- Executive summary: spreadsheet-ready format
SELECT 
    INITCAP(e.expedition_type::text) AS "Category",
    COUNT(DISTINCT b.booking_id) AS "Total Bookings",
    '$' || TO_CHAR(SUM(p.amount), 'FM999,999') AS "Revenue",
    '$' || TO_CHAR(
        ROUND(AVG(p.amount)::numeric, 0), 'FM999,999'
    ) AS "Avg Value",
    ROUND(
        COUNT(DISTINCT b.booking_id)
            FILTER (WHERE b.status = 'cancelled')::numeric
        / COUNT(DISTINCT b.booking_id) * 100,
        0
    )::text || '%' AS "Cancel Rate"
FROM expeditions e
    INNER JOIN expedition_instances ei 
        ON e.expedition_id = ei.expedition_id
    INNER JOIN bookings b 
        ON ei.instance_id = b.instance_id
    LEFT JOIN payments p 
        ON b.booking_id = p.booking_id
        AND p.payment_status = 'completed'
GROUP BY e.expedition_type
ORDER BY SUM(p.amount) DESC NULLS LAST;

And the results:

4 SQL Functions that Make This Work

Here are the functions that make this work:

1. INITCAP: Capitalize First Letters

INITCAP('cultural photography') → 'Cultural Photography'

• Turns lowercase database values into proper display names.

2. TO_CHAR: Number Formatting

TO_CHAR(516346.23, 'FM999,999,999') → '516,346'

• FM removes padding. 999,999 adds comma separators.
• Use 999,999.00 if you want decimals.

3. Dollar Signs and Percent Signs

-- Dollar formatting

'$'|| TO_CHAR(amount, 'FM999,999') AS revenue
​
​-- Percentage formatting​
ROUND(rate *100, 0)::text ||'%'AS cancel_rate

• Concatenation with || adds the symbols. Simple and effective.

4. Column Aliases With Quotes

SELECT amount AS "Total Revenue" -- Preserves spaces and casing

SELECT amount AS total_revenue -- Lowercase, no spaces

• Double-quoted aliases keep proper column headers for your spreadsheet.

Three Export-Ready Query Templates

Template 1: Monthly Summary Report

Notice the 'Mon YYYY' format gives you "Jan 2025" instead of "2025-01". Much friendlier in a spreadsheet.

-- Monthly metrics for executive review
SELECT 
    TO_CHAR(b.booking_date, 'Mon YYYY') AS "Month",
    COUNT(DISTINCT b.booking_id) AS "Bookings",
    '$' || TO_CHAR(SUM(p.amount), 'FM999,999') AS "Revenue",
    '$' || TO_CHAR(
        ROUND(AVG(p.amount)::numeric, 0), 'FM999,999'
    ) AS "Avg Value"
FROM bookings b
    INNER JOIN payments p ON b.booking_id = p.booking_id
WHERE p.payment_status = 'completed'
    AND b.status IN ('completed', 'confirmed')
GROUP BY TO_CHAR(b.booking_date, 'Mon YYYY'), 
         TO_CHAR(b.booking_date, 'YYYY-MM')
ORDER BY TO_CHAR(b.booking_date, 'YYYY-MM')
LIMIT 10

Template 2: Customer List for Marketing

Dates formatted as "Mar 15, 2025" instead of "2025-03-15". COALESCE handles never-booked customers. Ready for a mail merge!

-- Clean customer export for email campaigns
SELECT 
    c.first_name || ' ' || c.last_name AS "Customer Name",
    c.email AS "Email",
    COALESCE(c.state, c.country, 'Unknown') AS "Location",
    INITCAP(c.experience_level::text) AS "Experience",
    COALESCE(
        TO_CHAR(MAX(b.booking_date), 'Mon DD, YYYY'), 
        'Never Booked'
    ) AS "Last Booking"
FROM customers c
    LEFT JOIN bookings b ON c.customer_id = b.customer_id
        AND b.status IN ('completed', 'confirmed')
GROUP BY c.customer_id, c.first_name, c.last_name, 
         c.email, c.state, c.country, c.experience_level
ORDER BY MAX(b.booking_date) DESC NULLS LAST;

Template 3: Variance Report

Status indicators make the spreadsheet scannable without conditional formatting.

SELECT 
    INITCAP(TRIM(e.expedition_type::TEXT)) AS "Category",
    '$' || TO_CHAR(SUM(p.amount), 'FM999,999') AS "Revenue",
    CASE 
        WHEN SUM(p.amount) >= 450000 THEN '● Above Target'
        WHEN SUM(p.amount) >= 350000 THEN '◐ On Track'
        ELSE '○ Below Target'
    END AS "Status"
FROM expeditions e
    INNER JOIN expedition_instances ei 
        ON e.expedition_id = ei.expedition_id
    INNER JOIN bookings b 
        ON ei.instance_id = b.instance_id
    INNER JOIN payments p 
        ON b.booking_id = p.booking_id
WHERE p.payment_status = 'completed'
    AND b.status IN ('completed', 'confirmed')
GROUP BY TRIM(e.expedition_type::TEXT)
ORDER BY SUM(p.amount) DESC;

Balancing Formatting in SQL vs. Spreadsheet

There are two rules to keep in mind when formatting in SQL. You have to be careful not to overdo it.

Format in SQL when:

• Results go directly into a presentation or report
• You're sharing a one-time export with non-technical colleagues
• The formatting is straightforward (dollars, percentages, dates)

Format in the spreadsheet when:

• The data will be used for further calculations (formatted strings can't be summed)
• You need conditional formatting, charts, or pivot tables
• Multiple people will manipulate the data

A good rule of thumb: if someone will use your output as-is, format it in SQL. If they'll do further analysis on it, export clean numbers and let them format.

Once you can write the query and format the export, the next step is automating that export. That's where tools like Python scripts become your multiplier, running the query, formatting the output, and emailing the spreadsheet automatically.

But that's a topic for another day.

Try This Today

Take your most common query and add three formatting improvements:

1. INITCAP on any display names
2. TO_CHAR with commas on numeric values
3. Double-quoted column aliases for proper headers

The first time your colleague opens the export and says "this is perfect, I don't need to change anything" -- that's the goal.

Until next time,

Brian

Brian Graves, creator of Analytics in Action Say 👋 on X/Twitter, LinkedIn, or book a call with me. You can always reply to these emails. I check them all.

​

P.S. Presenting data for business impact is a key theme throughout SQL for Business Impact. Whether it's formatting exports or framing insights for executives, the course teaches you the full workflow from query to decision. Check it out at sqlforbusinessimpact.com.

P.P.S. What's your least favorite part of the SQL-to-spreadsheet workflow? Hit reply and let me know! I might build a solution for it. I read every response.