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If you run a sales team, an agency, or an e‑commerce brand, most of your decisions are hiding in spreadsheets. Orders, campaigns, lead lists, payouts, churn reports – they all land in Google Sheets or Excel. The challenge isn’t getting data in; it’s getting the right data out, on time, without someone spending Sunday night wrangling columns.
Extracting data well means you can answer sharp questions quickly: Which channels drive the highest LTV? Which accounts are about to churn? Which SKUs are killing margin? Instead of scrolling through thousands of rows, you slice, filter, and reshape data into targeted views: by segment, by rep, by country, by cohort.
This is exactly where an AI agent shines. Once you’ve defined the patterns – “pull last 30 days of paid traffic leads over $2k”, “split out B2B accounts into a new sheet” – you can delegate extraction to an AI computer agent. It logs into Google Sheets and Excel for you, applies filters, runs formulas, copies the right ranges, and updates your reporting workspace. Suddenly, the slowest part of your analytics loop disappears, and you make decisions at the speed of your data instead of the speed of manual clicking.
Every growing business eventually discovers the same painful truth: the bottleneck isn’t data collection, it’s data extraction. Your team drowns in Google Sheets and Excel files, exporting, filtering, and copying the same slices of information over and over.
In this guide, you’ll see practical ways to extract data in Excel and Google Sheets – from classic, manual techniques to no‑code automation and finally AI agents that handle the grunt work for you.
Excel:
Google Sheets:
Docs: https://support.google.com/docs/answer/3540681
Pros: Simple, visual, no formulas.
Cons: Repetitive; easy to make mistakes when criteria change often.
If you import CSV data where several fields are jammed into one column, split them first.
Excel:
Google Sheets:
Docs: https://support.google.com/docs/answer/6325535
Pros: Cleans raw imports so you can filter/join later.
Cons: One‑off; if the source file changes, you repeat the steps.
When your data is spread across multiple tables, lookup functions extract matching values.
Excel VLOOKUP example:
=VLOOKUP(E2, A2:C100, 3, FALSE).INDEX/MATCH pattern (more flexible): =INDEX(C2:C100, MATCH(E2, A2:A100, 0))
Google Sheets: Functions behave similarly: VLOOKUP, INDEX, MATCH.
Docs (Sheets VLOOKUP): https://support.google.com/docs/answer/3093318
Pros: Great for building slim, extracted reference tables.
Cons: Formulas can become brittle; complex workbooks are hard to maintain.
Excel 365+ and Google Sheets both have FILTER, which is almost purpose‑built for extraction.
Excel:
=FILTER(Master!A2:J100, Master!G2:G100="COLD") (wrap COLD in quotes in Excel).Google Sheets: =FILTER(Master!A2:J, Master!G2:G = "COLD")
Docs (Excel FILTER): https://support.microsoft.com/en-us/office/filter-function-f4f7cb66-82eb-4767-8f7c-4877ad80c759
Docs (Sheets FILTER): https://support.google.com/docs/answer/3540681#filter
Pros: Dynamic; updates automatically when the master table changes.
Cons: Criteria live in formulas, which non‑technical teammates may fear editing.
Manual steps are fine for ad‑hoc analysis. For recurring work – weekly sales reports, campaign pulls, cohort exports – you want automations that reset themselves.
Power Query (Get & Transform Data) is a point‑and‑click way to define repeatable extraction and cleaning steps.
Docs: https://support.microsoft.com/en-us/excel/power-query-3f7a0b4e-3f3a-4e6c-9c7f-34a3b9e7b0b9
Pros: Very powerful, no coding, ideal for repeated jobs.
Cons: Desktop‑centric, learning curve for non‑analysts.
QUERY lets you write SQL‑like statements over a range – an incredibly compact way to extract slices.
Example: pull all leads from the US with spend over 1000:
=QUERY(A1:F1000, "select * where C = 'US' and F > 1000", 1)
Steps:
QUERY formula pointing to the data range.Docs: https://support.google.com/docs/answer/3093343
Pros: Very compact, auto‑updating, powerful for analysts.
Cons: Requires SQL‑style thinking; errors can be cryptic.
You can also set up no‑code workflows that move and extract data into Excel or Google Sheets.
Common patterns:
Typical steps:
deal_amount > 10000).
Pros: Hands‑off once configured; connects many SaaS tools.
Cons: Limited to what connectors expose; complex logic can become a maze of steps.
When you’re running dozens of sheets and workbooks across desktop, browser, and cloud, even no‑code tools hit limits. This is where AI computer agents, like those powered by Simular Pro, become your virtual data ops team.
Imagine a Simular AI agent that:
How to implement at a high level:
Pros:
Cons:
A Simular agent can:
Because Simular’s execution is transparent and every action is inspectable, a non‑technical ops lead can open the agent’s run history and see exactly how data was filtered and extracted.
Pros:
Cons:
At scale, you can chain agents:
Because Simular focuses on production‑grade reliability (thousands to millions of steps) and offers webhook integration, these AI agents can sit inside your existing pipelines, quietly doing the grinding spreadsheet work your team hates.
The outcome: data extraction in Google Sheets and Excel stops being a fragile, manual chore and becomes a dependable, automated capability you can build the rest of your growth stack on.
