WorkBuddy Hands-on: Compress Excel into Reviewable Tables Before Letting AI Generate Reports in 3 Minutes

WorkBuddy hands-on: Don't rush to let AI generate reports in three minutes; first compress Excel into reviewable tables.

Last night I saw a tutorial titled "Python + DeepSeek automated Excel data analysis, generate the report your boss wants in 3 minutes." I've used WorkBuddy for a month and run Pandas as well, so my first reaction was skepticism. What bosses really want is for wrong data not to be packaged as a real report within three minutes.

My judgment has shifted increasingly toward this view: AI office tools should primarily leave an evidence trail in the reporting process, recording why each row was changed, deleted, or which fields didn't match. After a month with WorkBuddy, I prefer placing it in the role of report quality assurance. This workflow suits beginners and may not apply to all companies, but the path can be copied.

First, don't connect Feishu (Lark); break the report down into rejectable inputs. I'm still new to Feishu, DingTalk, and WeCom, so I dare not open permissions recklessly. Any AI tool should avoid connecting to company-wide docs initially. Create a local test folder with three subfolders: raw for original files, output for cleaned results, and audit for logs and difference tables. Open WorkBuddy, click New Task on the left, select Local File Processing at the top, and do NOT choose auto-sync cloud drive. Drag Excel or CSV files into the input area; CSV is comma-separated values, suitable for further calculation. Select Table Cleaning as the task type; use the Sales Detail template if available, otherwise choose Generic Table. Only check Field Recognition, Format Unification, Deduplication, Generate Cleaned Table, and Generate Audit Table. Do not check Auto-Summary or Generate Report yet. Set key parameters: unify dates to YYYY-MM-DD, split amounts into numeric columns with unit 'Yuan', do not auto-fill nulls with 0 (mark as NA), and use business primary keys like Order ID + Customer ID for duplicate detection. Choose CSV for output format, checking Retain Original Row Numbers and Generate Audit File. Click Dry Run, observe affected rows and high-risk fields, then click Execute. Take this step slowly to avoid blame later.

WorkBuddy leaves a trace of decisions. After cleaning, WorkBuddy typically outputs two files: cleaned.csv and audit.csv. The former is the clean table; the latter is the audit file telling you which rows were modified, deleted, or had missing fields. I usually check deleted and merged rows in audit.csv first; if the count exceeds ~1% of the original data, I stop and check the source. Next, check date columns for issues like writing 2026/9/8 as 2026-09-08 or mixing text like "N/A" into numeric columns. Finally, check amount columns; don't directly delete outliers. Flag amounts >3x median or <0 in yellow but don't auto-process. If the boss needs charts urgently, I feed cleaned.csv into Quick BI or Excel Pivot Tables. I trust it more for pre-report QA now; the prettier the chart, the easier wrong data fools people.

Regarding permissions and ops, after a month with WorkBuddy, I set three rules. Only I or the data owner can write to raw files; business colleagues have read-only access to output. Every run includes a run_id, e.g., 2026-09-08_sales_v1, for traceability. Check field mappings weekly; field mapping means unifying customer names, client IDs, and customer_name across different systems into one column name. Here's a pitfall: I initially tried processing Excel, PDFs, and email attachments simultaneously, resulting in unstable OCR for scanned dates. Later, I separated them: handle structured tables first, process PDFs via document extraction separately, and keep a manual review column.

This workflow truly saves three hours of explaining why the report was wrong. If you're new to WorkBuddy, don't let it take over reporting immediately. Treat it as a lock on metrics before generation: if fields, primary keys, and anomalies don't align, no conclusions are allowed.