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Your marketing, sales, and finance data lives in too many places: ad platforms, CRMs, payment tools, export folders. Modern data aggregation tools solve this by handling three hard steps for you: collecting raw data from dozens of sources, cleaning and standardizing it, and presenting it in clear views non-technical teams can use. Platforms inspired by ETL and BI best practices bring no-code pipelines, real-time processing, and strong security so you avoid brittle spreadsheets and one-off scripts.This is exactly where pairing Google Sheets with an AI computer agent becomes powerful. Let the agent log into tools, pull exports, normalize columns, and feed Sheets on a schedule, while you focus on reading the numbers. Instead of babysitting CSV files, you design the questions you want answered and let the agent keep your dashboards fresh in the background.
### 1. Traditional, manual ways to aggregate data**Story setup:** Imagine you are a performance marketing lead. Every Monday, you open five tabs: Google Ads, Meta Ads, LinkedIn, Stripe, and your CRM. By noon you still do not have a clean view of last week’s ROAS. This is the classic manual aggregation trap.Below are concrete manual workflows in Google Sheets you might already be doing.**1) Export CSVs from every tool and merge in Sheets**- Step 1: From each platform (for example Google Ads, Meta Ads), export a CSV of last week’s performance.- Step 2: In Google Sheets, go to `File > Import > Upload` and import each CSV into its own sheet tab.- Step 3: Standardize column names manually (for example rename `Campaign`, `Ad set`, `Ad group` to a single `Campaign` column).- Step 4: Build a master sheet and use formulas like `={Sheet1!A:F;Sheet2!A:F;Sheet3!A:F}` to stack data.- Step 5: Use `SUMIFS` or `QUERY` to aggregate: - Example: `=SUMIFS(Master!H:H, Master!A:A, "Last week", Master!C:C, "Google Ads")`.- Docs: see Google’s guide on importing data and supported file types: https://support.google.com/docs/answer/6055139**Pros:** Full control; works in any tool stack. **Cons:** Time consuming, error prone, impossible to scale.**2) Copy-paste tables from dashboards into Sheets**- Step 1: Filter the dashboard in each tool to the date range you want.- Step 2: Select the table, copy, and paste it into a fresh sheet tab.- Step 3: Repeat for every source.- Step 4: Use `VLOOKUP` or `INDEX/MATCH` to join additional attributes (for example join CRM pipeline stage onto ad spend by campaign).- Docs: joining data with lookup functions in Sheets: https://support.google.com/docs/answer/3093318**Pros:** Quick for ad hoc questions. **Cons:** No refresh, lots of rework, easy to misalign columns.**3) Use pivot tables directly in Sheets**Once you have all data in one master sheet:- Step 1: Select your data range.- Step 2: Click `Insert > Pivot table`.- Step 3: In the Pivot editor, add `Campaign` to Rows and `Cost`, `Conversions`, `Revenue` to Values.- Step 4: Add `Source` (Google Ads, Meta, etc.) to Columns to compare channels.- Docs: official pivot table help: https://support.google.com/docs/answer/1272898**Pros:** Fast aggregation and drilldown. **Cons:** Still limited by how often you manually refresh data.**4) Use Sheets functions to pull from other Sheets**If different teams maintain their own spreadsheets:- Step 1: Ask for view access to each source sheet.- Step 2: In your master sheet, use `IMPORTRANGE` to pull data: - `=IMPORTRANGE("sheet_url","TabName!A:Z")`- Step 3: Aggregate imported ranges using `QUERY` or `SUMIFS`.- Docs: IMPORTRANGE: https://support.google.com/docs/answer/3093340 and QUERY: https://support.google.com/docs/answer/3093343**Pros:** Always-on links to other Sheets. **Cons:** Still not connected to the original SaaS tools; schema changes can break things.---### 2. No-code automation methodsManual work collapses once you add no-code automation on top of Google Sheets. Think of this as installing pipes between your tools and Sheets, so you are not dragging buckets of data every week.**1) Use no-code ETL tools to feed a warehouse, then Sheets**Platforms inspired by Integrate.io, Solvexia, or Databricks-style ETL let you:- Connect 100+ sources (ad platforms, CRMs, payment processors) with prebuilt connectors.- Define transformations (rename columns, map currencies, filter dates) in a visual editor.- Load aggregated tables into a data warehouse.From there you can:- Use the official BigQuery connector to link Google Sheets to a curated table of metrics.- Docs: connect BigQuery to Sheets: https://support.google.com/docs/answer/9137168**Pros:** Central, governed data; scalable; strong security. **Cons:** Needs some data engineering help, plus warehouse costs.**2) Use no-code workflow tools to push data into Sheets**Automation tools like Zapier, Make, or n8n can:- Trigger when a new row appears in your CRM or a new sale appears in Stripe.- Transform data (for example compute net revenue, normalize country codes).- Append rows into a Google Sheet in real time.Typical setup for a sales leader:- Trigger: “New Closed Won deal in CRM”.- Actions: calculate ACV, tag channel, send to a Deals sheet.- Aggregation: use pivot tables in Sheets to summarize revenue by channel, rep, or industry.- Docs: Google Sheets API usage overview for automations: https://developers.google.com/sheets/api/guides/concepts**Pros:** Great for near real-time operational dashboards. **Cons:** Zaps or scenarios can proliferate and become hard to manage at large scale.**3) Spreadsheet add-ons for live connectors**Add-ons like Coefficient or Supermetrics embed data aggregation directly into the Sheets UI:- You choose a source (for example Google Ads) from a sidebar.- Configure metrics, dimensions, and filters.- Schedule automatic refreshes (for example every hour).From there, you layer Sheets’ own tools:- `QUERY` for flexible SQL-like aggregation.- Pivot tables and charts for reporting.- Docs: overview of using functions in Sheets: https://support.google.com/docs/answer/3093990**Pros:** Designed for business users; minimal engineering. **Cons:** Each add-on is another vendor to manage and pay for.---### 3. AI agents for aggregation at scale (with Simular)No-code tools remove a lot of friction, but they still depend on APIs and fixed connectors. In reality, your data is not only in well-behaved APIs; it is in CSV attachments, niche SaaS tools, custom admin panels, and internal dashboards.This is where a desktop-level AI computer agent like Simular Pro changes the game. Instead of just moving data between APIs, Simular acts like a tireless analyst operating your entire computer.**Method 1: Agent as a cross-platform data collection specialist**Imagine you want a daily sheet of campaign performance across:- Google Ads, Meta Ads, LinkedIn Ads.- Your subscription billing tool.- A custom internal dashboard.With Simular Pro you can:- Describe the workflow in natural language: open each platform, apply filters, export reports, clean columns, paste into the Master Performance sheet, refresh pivot tables.- Let the agent navigate UIs, handle logins, download files, and upload cleaned data into Google Sheets.**Pros:**- Works even when no API or connector exists.- Handles thousands to millions of UI steps with production-grade reliability.- Every action is transparent and editable, so operations leads can audit the process.**Cons:**- Requires an initial investment to design and test the workflow.- Best value when you have recurring, complex tasks rather than tiny one-offs.**Method 2: Agent for web research and enrichment into Sheets**For agencies and growth teams:- Give Simular a list of URLs or brand names in Google Sheets.- Ask it to visit each site, collect pricing, tech stack, or contact info, and write the results back into new columns.- Use Sheets’ pivot tables to aggregate competitors by category, average prices, or feature coverage.**Pros:**- Turns messy web data into structured rows without manual copying.- Perfect for lead enrichment, market maps, or competitive analysis.**Cons:**- Web layout changes can require updating the agent’s instructions.**Method 3: Agent as the last-mile orchestrator on top of existing tools**You might already have a data warehouse and BI tool. Simular Pro can:- Log into your BI dashboards.- Export a weekly summary.- Paste key metrics into an executive Google Sheet and distribute it via email or Slack.Here, the AI agent is a glue layer between tools that were never designed to talk to each other, giving leadership a single, narrative-friendly view without adding more APIs.Across all these methods, the pattern is the same: let Google Sheets be the familiar, shared canvas for your numbers, and let an AI computer agent handle the drudgery of collecting, cleaning, and refreshing the data behind the scenes.