Power Query for Accountants: What It Is & How to Use It

Power Query for accountants: what it is and how to use it

By Vicky Sarin — CA (ICAI), INSEAD alumnus & founder, Eduyush · Authorised AICPA & CIMA channel partner · Last updated June 2026

If you rebuild the same Excel workbook by hand every month, Power Query is the single skill that gives you that time back — and it happens to be the most practical, ownable version of "AI-era" data skills for an accountant. It cleans, reshapes and combines data through steps you record once and refresh with a click, with no formulas and no code.

This guide explains what Power Query is, what it actually automates in a finance workflow, and how to use it in Excel from a standing start — then where it fits in the wider skill set that keeps a finance career durable.

What is Power Query in Excel?

Power Query is the "Get & Transform" engine inside Excel — it pulls data in from files, folders, databases or the web and lets you clean and reshape it before it ever lands on a sheet.

It has shipped inside Excel for Windows since Excel 2016 (and is available as an add-in for some earlier versions), so for most accountants it's already there under the Data tab — no separate purchase. Instead of writing nested formulas, you click through transformations, and Excel records each one as an editable step. Change the source data, hit Refresh, and every step replays in order.

Why Power Query matters for accountants

It removes the repetitive data clean-up that eats finance time — and it's the part of "automation literacy" you can actually own without becoming a programmer.

As AI absorbs more routine accounting work, the durable skill isn't defending manual clean-up — it's automating your own workflows. Power Query is the most direct way to do that: monthly reconciliations, consolidations and management packs that used to mean an afternoon of copy-paste become a refresh. It's exactly the kind of capability that keeps a finance professional valuable as the routine work shrinks — a point we make in full in will AI replace accountants?

What you can automate with Power Query

Most of the repetitive Excel chores in a finance close map to a one-time Power Query step.

None of these touch your source data — Power Query works on a copy and records the recipe, so the original file is untouched and the process is auditable.

How to use Power Query in Excel

Get your data, transform it in the editor, then load and refresh — five steps from raw export to a report that rebuilds itself.

1. Get the data. On the Data tab, choose Get Data and pick your source — a file, a whole folder, a database or the web.
2. Open the editor. Choose Transform Data (not "Load") to open the Power Query Editor instead of dropping raw data onto a sheet.
3. Shape it. Remove columns you don't need, filter rows, change data types, split or merge — each click becomes a step under Applied Steps.
4. Edit freely. Steps can be renamed, reordered or deleted, and nothing is destructive to the source, so you can experiment without fear.
5. Close & Load — then Refresh. Send the result to a sheet or the data model. Next period, replace the source file and click Refresh; every step replays automatically.

Power Query vs macros and VBA

For cleaning and reshaping data, Power Query is the better first tool — it's no-code, refreshable and easier to audit than a macro.

Macros and VBA still have their place for bespoke automation, custom dialogs or actions Power Query can't reach. But for the data-prep work that fills most finance workbooks, Power Query is faster to build, far easier for a colleague to pick up, and doesn't carry the security and maintenance baggage of VBA. If you only learn one automation tool as an accountant, learn this one. (The "M" formula language sits underneath for advanced users — useful later, but not needed to start.)

Where Power Query fits in your skill set

Power Query is one rung of data and analytics fluency — the capability that's becoming a baseline expectation in finance rather than a specialism. The natural next steps are deeper data handling and visualisation, then turning data into decisions.

Frequently asked questions

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