Excel weekly dashboards at scale

Excel weekly dashboards at scale

PURPOSE

Designs refreshable Excel dashboards (Power Query + structured tables + validation + pivot reporting).

WHEN TO USE

• -TRIGGERS:

- Build me a Power Query pipeline for this file so it refreshes weekly with no manual steps.

- Turn this into a structured table with validation lists and clean data entry rules.

- Create a pivot-driven weekly dashboard with slicers for year and ISO week.

- Fix this Excel model so refresh does not break when new columns appear.

- Design a reusable KPI pack that updates from a folder of CSVs.

• -DO NOT USE WHEN…

- You need advanced forecasting/valuation modeling (this skill is for repeatable reporting pipelines).

- You need a BI tool build (Power BI/Tableau) rather than Excel.

- You need web scraping as the primary ingestion method.

INPUTS

• -REQUIRED:

- Source data file(s): CSV, XLSX, DOCX-exported tables, or PDF-exported tables (provided by user).

- Definition of ‘week’ (ISO week preferred) and the KPI fields required.

• -OPTIONAL:

- Data dictionary / column definitions.

- Known “bad data” patterns to validate (e.g., blank PayNumber, invalid dates).

- Existing workbook to refactor.

• -EXAMPLES:

- Folder of weekly CSV exports: exports/2026-W02/*.csv

- Single XLSX dump with changing columns month to month

OUTPUTS

• -If asked for plan only (default): a step-by-step build plan + Power Query steps + sheet layout + validation rules.
• -If explicitly asked to generate artifacts:

- workbook_spec.md (workbook structure and named tables)

- power_query_steps.pq (M code template)

- refresh-checklist.md (from assets/)

Success = refresh works after adding a new week’s files without manual edits, and validation catches bad rows.

WORKFLOW

1. Identify source type(s) (CSV/XLSX/DOCX/PDF-export) and the stable business keys (e.g., PayNumber).

2. Define the canonical table schema:

- required columns, types, allowed values, and “unknown” handling.

3. Design ingestion with Power Query:

- Prefer Folder ingest + combine, with defensive “missing column” handling.

- Normalize column names (trim, case, collapse spaces).

4. Design cleansing & validation:

- Create a Data_Staging query (raw-normalized) and Data_Clean query (validated).

- Add validation columns (e.g., IsValidPayNumber, IsValidDate, IssueReason).

5. Build reporting layer:

- Pivot table(s) off Data_Clean

- Slicers: Year, ISOWeek; plus operational dimensions

6. Add a “Refresh Status” sheet:

- last refresh timestamp, row counts, query error flags, latest week present

7. STOP AND ASK THE USER if:

- required KPIs/columns are unspecified,

- the source files don’t include any stable key,

- week definition/timezone rules are unclear,

- PDF/DOCX tables are not reliably extractable without a provided export.

OUTPUT FORMAT

When producing a plan, use this template:

WORKBOOK PLAN
-Sheets:
  - Data_Staging (query output)
  - Data_Clean (query output + validation flags)
  - Dashboard (pivots/charts)
  - Refresh_Status (counts + health checks)
-Canonical Schema:
  - <Column>: <Type> | Required? | Validation
-Power Query:
  - Query 1: Ingest_<name> (Folder/File)
  - Query 2: Clean_<name>
  - Key transforms: <bullets>
-Validation rules:
  - <rule> -> <action>
-Pivot design:
  - Rows/Columns/Values
  - Slicers

If asked for artifacts, also output:

• -assets/power-query-folder-ingest-template.pq (adapted)
• -assets/refresh-checklist.md

SAFETY & EDGE CASES

• -Read-only by default: provide a plan + snippets unless the user explicitly requests file generation.
• -Never delete or overwrite user files; propose new filenames for outputs.
• -Prefer “no silent failure”: include row-count checks and visible error flags.
• -For PDF/DOCX sources, require user-provided exported tables (CSV/XLSX) or clearly mark extraction risk.

EXAMPLES

• -Input: “Folder of weekly CSVs with PayNumber/Name/Date.”

Output: Folder-ingest PQ template + schema + Refresh Status checks + pivot dashboard plan.

• -Input: “Refresh breaks when new columns appear.”

Output: Defensive missing-column logic + column normalization + typed schema plan.