Buyer’s guide Sourced and dated
Three free options keep the rows on your machine: xmetric in your browser tab on Mac, Windows or Linux with nothing to install, Tableau Desktop Free Edition installed on Windows or Mac for one person, and Power BI Desktop on Windows. Cloud AI tools like Julius are a different category: the file goes to their servers, whatever their policy on training.
Our pick
xmetric
Nothing to install and nothing uploaded, which matters on a locked-down laptop where you can't add software. It runs on Mac, Windows and Linux, and does the FIXED peer comparisons a pay review needs.
Pick something else if
You want a full installed app and work alone (Tableau Desktop Free Edition), your company runs on Windows and Microsoft 365 (Power BI Desktop), or the data isn't sensitive and you'd rather ask questions in a chat (Julius).
xmetric makes this page. Every vendor fact below links to the vendor’s own page and the date we checked it.
Here's a clinic group's payroll, opened in xmetric on this page. Women's median salary is $57,250 against $67,100 for men. Compare each person with peers in the same department and level, though, and the gap is gone. It's in who gets promoted.
Computed by the xmetric engine
The data
A 320-person regional clinic group: nurses, pharmacists, lab staff, admin, IT and facilities, across five job levels. No names, just what a pay review needs.
employee_id | department | level | gender | hire_year | base_salary |
|---|---|---|---|---|---|
| E0212 | Administration | L2 Specialist | M | 2015 | 55400 |
| E0039 | Nursing | L2 Specialist | F | 2018 | 60100 |
| E0228 | Administration | L3 Senior | M | 2012 | 68100 |
| E0018 | Nursing | L3 Senior | F | 2010 | 80200 |
Step 1
Across the whole company, women's median pay is well below men's. That's the number a board sees first.
Tableau
MEDIAN([base_salary])
// gender on ColumnsThe xmetric engine ran this formula and got the SQL’s Base salary column, row for row.
SQL
SELECT
gender,
COUNT(*) AS employees,
MEDIAN(base_salary) AS median_salary
FROM input
GROUP BY gender
ORDER BY genderRows
| Gender | Employees | Base salary |
|---|---|---|
| F | $212 | $57,250 |
| M | $108 | $67,100 |
Step 2
Compare each person with their own peer group and the gap disappears: both medians sit at 1.00. Same job, same pay.
Tableau
[base_salary] / { FIXED [department], [level] : MEDIAN([base_salary]) }
// then MEDIAN of it by genderSQL
WITH c AS (
SELECT
*,
base_salary
/ MEDIAN(base_salary) OVER (PARTITION BY department, level) AS compa_ratio
FROM input
)
SELECT gender, MEDIAN(compa_ratio) AS median_compa_ratio
FROM c
GROUP BY gender
ORDER BY genderRows
| Gender | Compa ratio |
|---|---|
| F | 1 |
| M | 1 |
Step 3
Here's the gap. Women hold three in four associate jobs and about a third of lead and manager jobs. The pay gap is a promotion gap.
Tableau
SUM(IF [gender] = "F" THEN 1 ELSE 0 END) / COUNT([employee_id])
// level on RowsThe xmetric engine ran this formula and got the SQL’s Share women column, row for row.
SQL
SELECT
level,
COUNT(*) AS employees,
AVG(CASE WHEN gender = 'F' THEN 1 ELSE 0 END) AS share_women
FROM input
GROUP BY level
ORDER BY levelRows
| Level | Employees | Share women |
|---|---|---|
| L1 Associate | 10200% | 74.5% |
| L2 Specialist | 10500% | 79% |
| L3 Senior | 6900% | 55.1% |
| L4 Lead | 2800% | 39.3% |
| L5 Manager | 1600% | 25% |
Step 4
The same FIXED peer median gives the review list. Move the threshold to see how fast the list grows.
| Employee ID | Department | Level | Gender | Base salary | Compa ratio |
|---|---|---|---|---|---|
| E0164 | Laboratory | L4 Lead | M | 77,400 | 83% |
| E0141 | Pharmacy | L1 Associate | F | 46,100 | 83.4% |
| E0147 | Pharmacy | L2 Specialist | F | 59,600 | 83.7% |
| E0243 | Administration | L1 Associate | F | 36,100 | 84.3% |
| E0211 | Administration | L1 Associate | M | 36,300 | 84.8% |
| E0226 | Administration | L3 Senior | M | 57,500 | 85.3% |
| E0135 | Pharmacy | L2 Specialist | F | 60,800 | 85.4% |
| E0251 | IT | L1 Associate | F | 47,400 | 85.6% |
| E0116 | Nursing | L2 Specialist | M | 52,500 | 85.9% |
| E0035 | Nursing | L3 Senior | F | 64,900 | 86.2% |
| E0041 | Nursing | L1 Associate | F | 41,000 | 86.3% |
| E0034 | Nursing | L2 Specialist | F | 52,900 | 86.6% |
| E0265 | IT | L2 Specialist | M | 59,600 | 86.9% |
| E0071 | Nursing | L2 Specialist | F | 53,100 | 86.9% |
| E0015 | Nursing | L1 Associate | F | 41,300 | 86.9% |
| E0260 | IT | L1 Associate | M | 48,200 | 87% |
| E0054 | Nursing | L1 Associate | F | 41,500 | 87.4% |
| E0205 | Administration | L2 Specialist | F | 46,900 | 87.5% |
| E0059 | Nursing | L3 Senior | M | 66,100 | 87.8% |
| E0122 | Pharmacy | L2 Specialist | M | 62,600 | 87.9% |
| E0201 | Administration | L3 Senior | M | 59,300 | 88% |
| E0094 | Nursing | L5 Manager | M | 112,200 | 88.1% |
| E0045 | Nursing | L1 Associate | F | 41,900 | 88.2% |
| E0099 | Nursing | L4 Lead | M | 89,800 | 88.3% |
| E0096 | Nursing | L3 Senior | F | 66,500 | 88.3% |
| E0230 | Administration | L1 Associate | M | 37,800 | 88.3% |
| E0098 | Nursing | L1 Associate | F | 42,000 | 88.4% |
| E0235 | Administration | L3 Senior | F | 59,600 | 88.4% |
| E0102 | Nursing | L2 Specialist | M | 54,100 | 88.5% |
| E0247 | Administration | L3 Senior | F | 59,700 | 88.6% |
| E0019 | Nursing | L5 Manager | F | 113,000 | 88.7% |
| E0195 | Administration | L1 Associate | F | 38,000 | 88.8% |
| E0267 | IT | L1 Associate | F | 49,200 | 88.8% |
| E0286 | Facilities | L1 Associate | M | 34,500 | 88.9% |
| E0270 | IT | L2 Specialist | F | 61,000 | 88.9% |
| E0005 | Nursing | L4 Lead | M | 90,500 | 89% |
| E0023 | Nursing | L2 Specialist | M | 54,500 | 89.2% |
| E0300 | Facilities | L1 Associate | F | 34,700 | 89.4% |
| E0184 | Laboratory | L2 Specialist | M | 50,600 | 89.6% |
| E0114 | Nursing | L3 Senior | M | 67,500 | 89.6% |
| E0244 | Administration | L2 Specialist | F | 48,100 | 89.7% |
| E0168 | Laboratory | L1 Associate | M | 39,400 | 90% |
| E0312 | Facilities | L2 Specialist | F | 44,900 | 90% |
Tableau
[base_salary] / { FIXED [department], [level] : MEDIAN([base_salary]) } < 0.9SQL
WITH c AS (
SELECT
*,
base_salary
/ MEDIAN(base_salary) OVER (PARTITION BY department, level) AS compa_ratio
FROM input
)
SELECT
employee_id,
department,
level,
gender,
base_salary,
compa_ratio
FROM c
WHERE compa_ratio < 0.9
ORDER BY compa_ratioRows
| Employee ID | Department | Level | Gender | Base salary | Compa ratio |
|---|---|---|---|---|---|
| E0164 | Laboratory | L4 Lead | M | 77,400 | 83% |
| E0141 | Pharmacy | L1 Associate | F | 46,100 | 83.4% |
| E0147 | Pharmacy | L2 Specialist | F | 59,600 | 83.7% |
| E0243 | Administration | L1 Associate | F | 36,100 | 84.3% |
| E0211 | Administration | L1 Associate | M | 36,300 | 84.8% |
| E0226 | Administration | L3 Senior | M | 57,500 | 85.3% |
| E0135 | Pharmacy | L2 Specialist | F | 60,800 | 85.4% |
| E0251 | IT | L1 Associate | F | 47,400 | 85.6% |
| E0116 | Nursing | L2 Specialist | M | 52,500 | 85.9% |
| E0035 | Nursing | L3 Senior | F | 64,900 | 86.2% |
| E0041 | Nursing | L1 Associate | F | 41,000 | 86.3% |
| E0034 | Nursing | L2 Specialist | F | 52,900 | 86.6% |
| E0265 | IT | L2 Specialist | M | 59,600 | 86.9% |
| E0071 | Nursing | L2 Specialist | F | 53,100 | 86.9% |
| E0015 | Nursing | L1 Associate | F | 41,300 | 86.9% |
| E0260 | IT | L1 Associate | M | 48,200 | 87% |
| E0054 | Nursing | L1 Associate | F | 41,500 | 87.4% |
| E0205 | Administration | L2 Specialist | F | 46,900 | 87.5% |
| E0059 | Nursing | L3 Senior | M | 66,100 | 87.8% |
| E0122 | Pharmacy | L2 Specialist | M | 62,600 | 87.9% |
| E0201 | Administration | L3 Senior | M | 59,300 | 88% |
| E0094 | Nursing | L5 Manager | M | 112,200 | 88.1% |
| E0045 | Nursing | L1 Associate | F | 41,900 | 88.2% |
| E0099 | Nursing | L4 Lead | M | 89,800 | 88.3% |
| E0096 | Nursing | L3 Senior | F | 66,500 | 88.3% |
| E0230 | Administration | L1 Associate | M | 37,800 | 88.3% |
| E0098 | Nursing | L1 Associate | F | 42,000 | 88.4% |
| E0235 | Administration | L3 Senior | F | 59,600 | 88.4% |
| E0102 | Nursing | L2 Specialist | M | 54,100 | 88.5% |
| E0247 | Administration | L3 Senior | F | 59,700 | 88.6% |
| E0019 | Nursing | L5 Manager | F | 113,000 | 88.7% |
| E0195 | Administration | L1 Associate | F | 38,000 | 88.8% |
| E0267 | IT | L1 Associate | F | 49,200 | 88.8% |
| E0286 | Facilities | L1 Associate | M | 34,500 | 88.9% |
| E0270 | IT | L2 Specialist | F | 61,000 | 88.9% |
| E0005 | Nursing | L4 Lead | M | 90,500 | 89% |
| E0023 | Nursing | L2 Specialist | M | 54,500 | 89.2% |
| E0300 | Facilities | L1 Associate | F | 34,700 | 89.4% |
| E0184 | Laboratory | L2 Specialist | M | 50,600 | 89.6% |
| E0114 | Nursing | L3 Senior | M | 67,500 | 89.6% |
| E0244 | Administration | L2 Specialist | F | 48,100 | 89.7% |
| E0168 | Laboratory | L1 Associate | M | 39,400 | 90% |
| E0312 | Facilities | L2 Specialist | F | 44,900 | 90% |
At the default threshold, 43 people are paid below that share of their peer median: that's the review list, and it was built without a single row leaving your browser.
Common mistake
Leave level out of the FIXED expression and every junior looks underpaid next to their own managers: 121 flags, 99 of them associates. With level in, it's 43 people who really sit low in their band.
Tableau
[base_salary] / { FIXED [department] : MEDIAN([base_salary]) }SQL
WITH c AS (
SELECT
*,
base_salary
/ MEDIAN(base_salary) OVER (PARTITION BY department) AS compa_ratio
FROM input
)
SELECT
COUNT(*) AS flagged,
COUNT(*) FILTER (WHERE level = 'L1 Associate') AS associates
FROM c
WHERE compa_ratio < 0.9Tableau
[base_salary] / { FIXED [department], [level] : MEDIAN([base_salary]) }SQL
WITH c AS (
SELECT
*,
base_salary
/ MEDIAN(base_salary) OVER (PARTITION BY department, level) AS compa_ratio
FROM input
)
SELECT
COUNT(*) AS flagged,
COUNT(*) FILTER (WHERE level = 'L1 Associate') AS associates
FROM c
WHERE compa_ratio < 0.9Drop your own payroll export (one row per employee, with department, level and salary) and the same steps run on it, in your browser. Nothing is uploaded.
Drop a CSV anywhere on this page
Up to 10,000 rows. The sample, payroll.csv, shows the shape.
If this is you
A locked-down laptop, a Mac, or no time to get software approved
xmetric
Opens in the browser; the file stays in the tab.
If this is you
You want an installed desktop app and work on your own
Tableau Desktop
Tableau Desktop Free Edition: full Desktop analytics for one person, stored locally; no publishing, online sharing or sharing packaged workbooks.
If this is you
Windows laptops and reports that end up in Microsoft 365
Power BI Desktop
Desktop is free and local; sharing means publishing to the Power BI service with Pro at $14.00 user/month, paid yearly.
If this is you
Data that isn't sensitive, and you'd rather ask in a chat
Julius
Files are analyzed in Julius's cloud workspace; plans list server RAM and file storage.
Open the browser's developer tools (F12, or Cmd+Option+I on a Mac), go to the Network tab, then drop the file and build the charts. No request carries your rows. Only saving a workbook online sends anything, and that's a separate, explicit step.
Not for most payroll, health or deal data. Not training on a file is a promise about what happens after the upload; the file still sits on someone else's server, which is what vendor reviews, data processing agreements and HIPAA or GDPR transfer rules are about.
If you haven't saved the workbook online, it's gone with the tab. Nothing was stored anywhere else.
An employee's salary divided by the median salary of their peers (here, the same department and level). 1.00 is the middle of the band; below 0.90 is a common flag for a pay review.