

Every agency owner and marketer knows this scene: it’s 10 p.m., you’re still in Excel or Google Sheets, wrestling with broken SUMIFS, hidden rows, and #DIV/0! errors while a client waits for a "simple" weekly report. Aggregating data isn’t hard in theory, but in the real world you’re stitching together exports, skipping error cells, and replaying the same clicks hundreds of times.Learning how to aggregate data properly in Excel (with tools like AGGREGATE, SUMIFS, PivotTables, and Power Query) gives you clean, reliable rollups: totals by channel, averages by campaign, quartiles of deal size, and more. You can ignore hidden rows, filter out errors, and summarize millions of rows without crashing your file.But the real unlock comes when you hand this work to an AI agent. Instead of burning hours cleaning sheets, the agent opens files, applies the right formulas, refreshes queries, and pastes results into dashboards on schedule. Your role shifts from spreadsheet janitor to strategist, reviewing insights instead of building them.
### 1. Manual aggregation methods (Excel & Google Sheets)Before you automate, you need solid fundamentals. Here are core manual techniques your team already uses — and that your AI agent will eventually mimic.#### 1.1 Basic SUM, AVERAGE, COUNT**Excel**1. Select a cell where you want the total.2. Type `=SUM(` and drag over the numeric range (for example, `=SUM(D2:D500)`).3. Press Enter.4. Repeat with `=AVERAGE(range)` or `=COUNT(range)` for averages and counts.Official docs: https://support.microsoft.com/en-us/office/sum-function-043e1c7d-7726-4e80-8f32-07b23e057f89**Google Sheets**1. Click the result cell.2. Use `=SUM(B2:B500)`, `=AVERAGE(B2:B500)`, or `=COUNT(B2:B500)`.3. Drag the fill handle down to reuse formulas across rows.Docs: https://support.google.com/docs/answer/3093991Pros: Simple, transparent. Cons: Fragile with errors, hidden rows, and large datasets.#### 1.2 Conditional aggregation with SUMIF / SUMIFS**Excel**1. To sum spend for a single channel: `=SUMIF(A:A,"Google Ads",D:D)` where column A is Channel and D is Spend.2. For multiple conditions (e.g., Channel + Region): `=SUMIFS(D:D, A:A, "Google Ads", B:B, "US")`.Docs: https://support.microsoft.com/en-us/office/sumifs-function-c9e748f5-7ea7-455d-9406-611cebce642b**Google Sheets**Use the same syntax: `=SUMIFS(D:D, A:A, "Google Ads", B:B, "US")`.Docs: https://support.google.com/docs/answer/7014145Pros: Great for dashboards, simple reporting. Cons: Hard to maintain when criteria change; very repetitive across many tabs/clients.#### 1.3 Excel AGGREGATE to ignore errors/hidden rowsWhen sheets get messy, AGGREGATE saves you.Example in Excel:- `=AGGREGATE(9,6,D2:D500)` - `9` = SUM - `6` = ignore error values - `D2:D500` = rangeNow your totals skip #DIV/0! and #NUM! without complex IFERROR wrappers.Docs: https://support.microsoft.com/en-us/office/aggregate-function-43b9278e-6aa7-4f17-92b6-e19993fa26dfPros: Powerful, handles dirty data. Cons: Less known to teams; still manual setup.#### 1.4 Pivot tables (Excel & Sheets)**Excel**1. Select your table (Ctrl+A inside the data).2. Go to **Insert → PivotTable**.3. Place the pivot on a new sheet.4. Drag *Channel* to Rows, *Month* to Columns, *Spend* to Values.5. Change Value settings (Sum, Average, Count) as needed.Docs: https://support.microsoft.com/en-us/office/create-a-pivottable-to-analyze-worksheet-data-a9a84538-bfe9-40a9-a8e9-f99134456576**Google Sheets**1. Select your data.2. Click **Insert → Pivot table**.3. Choose a destination.4. Add Rows, Columns, Values, and Filters.Docs: https://support.google.com/docs/answer/1272900Pros: Fast multi-dimensional aggregation. Cons: Still needs a human to refresh, tweak, and copy results into client-facing reports.---### 2. No-code automation for recurring aggregationAt some point, your Monday reporting ritual shouldn’t require opening 12 files by hand.#### 2.1 Scheduled imports into Google SheetsUse built-in features plus add-ons:1. Connect your CRM or ad platforms via a connector (e.g., from the Google Workspace Marketplace).2. Configure a scheduled refresh (daily/weekly).3. Build pivot tables or QUERY-based summaries on top of the raw tab.Query example:`=QUERY('Raw Data'!A1:F, "select C, sum(F) where A >= date '2025-01-01' group by C", 1)`Docs: https://support.google.com/docs/answer/3093343 (QUERY)Pros: Fully browser-based, good for agencies living in Sheets. Cons: Connectors cost money; complex queries are hard for non-technical staff.#### 2.2 Power Query in ExcelPower Query turns raw exports into repeatable pipelines.1. In Excel, go to **Data → Get Data** (From File, From Web, From OData, etc.).2. Load your source (CSV, database, OData feed like Northwind).3. In Power Query Editor, group and aggregate: - Right-click a column like *Order_Details*. - Click the expand icon, choose **Aggregate**. - Pick Sum, Average, Count, etc.4. Click **Close & Load** to push the aggregated table back into Excel.Docs: https://support.microsoft.com/en-us/office/aggregate-data-from-a-column-power-query-14b85bee-aec4-4816-96d7-372a1439cf5bPros: Excellent for large datasets and repeatable transformations. Cons: Setup takes time; still needs someone to open the workbook and hit Refresh (or configure more advanced refresh setups).#### 2.3 Workflow tools (Zapier, Make, etc.)You can:1. Trigger on a schedule (every day at 7 a.m.).2. Pull data from your CRM/ads tool.3. Append it into a Google Sheets or Excel Online table.4. Let built-in formulas/pivots recalc the aggregates.Pros: No code, connects many tools. Cons: Logic lives outside the spreadsheet; debugging can be tricky, and you’re still limited to formula-based aggregation.---### 3. Scaling aggregation with AI computer agentsManual and no-code methods get you partway. But for agencies and teams managing dozens of client workbooks, the real pain is orchestration: opening files, checking if data synced, fixing new errors, copying charts into decks.This is where a desktop-class AI computer agent such as **Simular Pro** becomes your reporting analyst.#### 3.1 Agent-driven Excel and Sheets aggregationYou can configure an AI agent to:- Open Excel workbooks and Google Sheets in a browser.- Import the latest CSV exports or trigger Power Query refresh.- Insert or adjust formulas like AGGREGATE, SUMIFS, or QUERY.- Rebuild PivotTables if schemas changed.- Copy summarized tables into a clean "Client Report" tab or a Google Slide.**Pros**- Hands-free execution: the agent does the clicks, keystrokes, and navigation across desktop, browser, and cloud.- Works with messy, real-world interfaces (not just APIs).- Transparent: every action is logged and replayable.**Cons**- Requires initial setup and clear instructions.- Best value at scale (multiple clients, many reports).#### 3.2 Multi-step, high-volume workflowsBecause Simular’s agents are designed for workflows with thousands to millions of steps, you can:- Run nightly aggregation across **all** client folders.- Let the agent detect errors (failed formulas, #VALUE!, broken links) and repair them using AGGREGATE or alternate logic.- Post results into CRMs, project tools, or email summaries.Example weekly workflow for an agency:1. Agent opens each client’s Google Sheet and linked Excel model.2. Refreshes data sources (API connectors, Power Query, CSV imports).3. Validates key metrics (e.g., total spend vs. platform numbers).4. Regenerates pivot tables and charts.5. Exports PDFs and drops them into a shared folder or sends by email.You get the reliability of production-grade automation and the flexibility of a human analyst — without anyone babysitting spreadsheets at midnight.
For large datasets, the most reliable and scalable method is to use PivotTables in Excel and pivot tables or QUERY in Google Sheets, often combined with structured data sources.In **Excel**, format your data as a table (Ctrl+T), then choose **Insert → PivotTable**. Place fields like Date or Campaign in Rows, Metrics (Spend, Leads, Revenue) in Values, and optional dimensions (Region, Channel) in Columns or Filters. This lets you change aggregation type (Sum, Average, Count) without rewriting formulas. For extra reliability, feed the pivot from Power Query so the underlying data is cleaned and de-duplicated before it ever hits the pivot.In **Google Sheets**, use **Insert → Pivot table** on a clean range or use QUERY: `=QUERY(A1:F, "select C, sum(F) group by C", 1)`. QUERY is effectively SQL for your sheet and is very stable once defined.Layering these with a consistent data model (same column names and types) makes your aggregation durable and repeatable, even as volume grows.
In **Excel**, the AGGREGATE function is designed exactly for this. Its syntax is `=AGGREGATE(function_num, options, ref1, [ref2])`. For example, to sum a range while ignoring errors and hidden rows, use:`=AGGREGATE(9, 7, D2:D500)`Here, `9` means SUM and `7` tells Excel to ignore both hidden rows and error values. If you only want to ignore errors but not hidden rows, use option `6` instead. You can also apply AGGREGATE with functions like MAX, MIN, MEDIAN, LARGE, and SMALL by changing the `function_num`.In **Google Sheets**, there is no AGGREGATE function, but you can emulate it. Wrap your range in FILTER to remove bad values: `=SUM(FILTER(D2:D500, ISNUMBER(D2:D500)))` to ignore errors, or filter on a helper column that marks visible rows. While this is more manual than Excel’s AGGREGATE, it achieves the same outcome: clean, trustworthy rollups despite messy source data.
To aggregate across many sheets or files, avoid manually copy-pasting. Instead, centralize data via references or query tools, then aggregate on top.In **Excel**, if all sheets share the same structure, you can use Power Query:1. Go to **Data → Get Data → From File → From Workbook** and import each file, or point to a folder.2. In Power Query, append all tables into one combined query.3. Perform grouping and aggregation (Home → Group By) to sum or count across all sources.4. Load the aggregated result into a report tab and optionally into a PivotTable.In **Google Sheets**, you can use `IMPORTRANGE` to pull data from multiple spreadsheets into a master file, then run QUERY or pivot tables on the combined range. Example: `=QUERY({IMPORTRANGE(url1,"Data!A:F"); IMPORTRANGE(url2,"Data!A:F")}, "select Col1, sum(Col6) group by Col1",1)`.This approach keeps each source file isolated but gives you a single source of truth for all aggregation.
The best approach mixes solid spreadsheet structure with automation.1. **Standardize your schema**: Ensure every client or business unit uses the same column names (Date, Channel, Spend, Leads, Revenue). This lets you reuse formulas and queries.2. **Centralize raw data**: In Excel, use Power Query to pull in CSV/platform exports into one or more staging queries. In Google Sheets, set up scheduled imports via connectors or IMPORTRANGE into a master sheet.3. **Build reusable aggregation logic**: Use PivotTables (Excel) or pivot tables/QUERY (Sheets) to compute KPIs like total spend, CPA, ROAS by channel and week. Avoid hard-coding dates; instead filter on dynamic ranges (e.g., “last 7 days” in QUERY or slicers in Excel).4. **Automate refresh**: On Excel desktop, pair Power Query with an AI agent or scheduled task that opens the workbook and refreshes all queries/pivots. In Sheets, rely on connector refresh schedules.Once this is stable, you can push PDFs or summary tables to clients without touching raw data.
An AI computer agent turns your aggregation playbook into an executable workflow that runs without you.Instead of manually opening Excel and Google Sheets every Monday, refreshing Power Query, fixing broken formulas, and exporting PDFs, you teach the agent those exact steps once. A platform like Simular Pro can:- Launch your browser and desktop apps.- Navigate to each workbook or Google Sheet from a list of client URLs.- Trigger data refreshes (connectors, Power Query, or CSV imports).- Validate key formulas (AGGREGATE, SUMIFS, QUERY) and repair simple issues.- Rebuild or refresh PivotTables and charts.- Save updated files, export PDFs, and drop them into shared drives or email drafts.Because every action is logged and inspectable, you keep full control: you can replay a run, see where a data issue occurred, and refine the instructions. Over time, the agent becomes your tireless reporting analyst, freeing your sales, marketing, and ops teams to interpret the numbers instead of chasing them.