Replace Tableau Creator seats with Data Studio (Looker Studio) dashboards on Google Sheets

Here are the header row and a 50-row sample from my Google Sheets tab "{{tab_name}}" (CSV):

{{sample_csv}}

One row should represent one {{row_grain}}. Identify:
1. Columns with mixed types or text where a number/date is expected.
2. Inconsistent category spellings (list the variants you see).
3. Rows that look like subtotals, blanks or duplicates.
4. Whether the table is 'wide' and should be reshaped long.
For each problem, give the exact Google Sheets formula for a helper column that fixes it (assume data starts in row 2). Don't guess about rows you haven't seen; tell me what to check in the full sheet.

Write a calculated field for Google Data Studio (formerly Looker Studio). My data source fields (name: type) are:
{{field_list}}

Metric I want, in words: {{metric_definition}}

Give: (1) the formula, (2) whether it must be created in the data source or the chart, (3) the field type/format to set, (4) one way to sanity-check the result against the source sheet. Use only functions that exist in Data Studio; if what I want needs a blend or can't be done, say so. If my previous attempt failed, here is the formula and error: {{previous_error}}

I'm the {{role}} at a {{business_type}}. Below is a table exported from our dashboard (CSV). {{metric}} changed from {{previous_value}} in {{previous_period}} to {{current_value}} in {{current_period}}.

{{exported_csv}}

1. Show a working table that reproduces both values from the data. If you can't reproduce them, stop and tell me why.
2. Break the change down by {{breakdown_dimension}}: which segments drove it (absolute and % contribution).
3. Note anything that looks like a data problem rather than a business change.
4. Write a 4-sentence summary for the owners in plain English, no jargon, no speculation beyond the data.

DATA-PREP CHECKLIST — do this in Google Sheets before connecting Looker Studio / Data Studio

Shape
[ ] One table per tab, starting at A1, ONE header row (Data Studio uses the first row as field names)
[ ] One row = one event/record (one order, one deal, one ticket) — no subtotal rows, no blank spacer rows, no merged cells
[ ] Long, not wide: a "Month" column + a "Value" column, NOT one column per month
[ ] Every column has a single data type all the way down (no "N/A" text in a number column — leave blank)
[ ] Stable, unique ID column (Order ID, Deal ID) for COUNT_DISTINCT and joins (blends)

Types and formats
[ ] Dates are real dates (right-aligned in Sheets), one format, no text like "Q3"
[ ] Currency columns are plain numbers; currency code in its own column if you have several
[ ] Categories spelled consistently (use data-validation dropdowns at the source; =UNIQUE(C2:C) to spot variants)
[ ] Percentages stored as numbers (0.25), not text ("25%")

Hygiene
[ ] Raw data tabs are append-only; calculations live in Data Studio calculated fields or a separate "model" tab
[ ] Hidden/filtered rows: Data Studio INCLUDES hidden and filtered cells by default — delete junk rows instead of hiding them
[ ] Freeze the header row and protect it (Data → Protect sheets and ranges) so nobody renames a column
    (renaming breaks fields; after any column change, Resource → Manage data sources → Edit → Refresh fields)
[ ] Store the sheet in a normal Drive folder (the connector docs say shared/Team Drive files aren't supported)
[ ] Volume check: if a tab is approaching hundreds of thousands of rows or reports feel slow, move the data to BigQuery

AI help (paste a 50-row sample, never the full sensitive dataset)
[ ] Ask the assistant to list type problems, inconsistent categories and outliers in the sample
[ ] Ask for the exact Sheets formulas to fix them in a helper column, then paste values

DASHBOARD SPEC — fill this in before building anything (one page per dashboard)

1. Purpose
   Audience: ______ (e.g. owners + 2 account managers)
   Decision it supports: ______ (e.g. "which clients need attention this week")
   Review cadence: ______ (e.g. Monday 9:00, emailed PDF)

2. Data sources
   | Source (sheet/tab) | Grain (one row = …) | Refresh (manual/form/import) | Owner | Rows today |
   |--------------------|---------------------|------------------------------|-------|-----------|

3. Metrics (max 6 on page 1)
   | Metric | Definition in words | Data Studio formula | Target / good-bad | Format |
   | Revenue | Sum of invoice amount, excl. credit notes | SUM(Amount) with filter Type = Invoice | ≥ 40k/mo | Currency, 0 dp |
   | Win rate | Won deals ÷ closed deals | SUM(CASE WHEN Stage = "Won" THEN 1 ELSE 0 END) / SUM(CASE WHEN Stage IN ("Won","Lost") THEN 1 ELSE 0 END) | ≥ 30% | Percent |
   | Avg deal size | Won value ÷ won deals | SUM(Won value) / SUM(Is won) | | Currency |

4. Dimensions & filters
   Date field: ______  Default range: ______ (e.g. last 12 months, compare to previous period)
   Filter controls: ______ (e.g. Owner, Client segment)
   Breakdowns: ______ (e.g. by month, by service line)

5. Layout (top → bottom)
   Row 1: scorecards (metric + comparison)
   Row 2: trend line (metric by month)
   Row 3: breakdown bar chart + table with conditional formatting
   Page 2+: detail tables for drill-down

6. Access & delivery
   Viewers: ______  Editors: ______  Data credentials: owner's / viewer's
   Scheduled email: recipients ______, frequency ______, pages ______

7. Acceptance test
   Three numbers checked against the source sheet by hand: ______ / ______ / ______

CALCULATED-FIELD COOKBOOK (Data Studio, formerly Looker Studio) — add via Resource → Manage data sources → Edit → Add a field
Field names below assume headers: Date, Amount, Stage, Client, Owner, Type, Deal ID.

Flag won deals (number, so it can be summed)
  Is won        = CASE WHEN Stage = "Won" THEN 1 ELSE 0 END
Closed deals
  Is closed     = CASE WHEN Stage IN ("Won", "Lost") THEN 1 ELSE 0 END
Win rate (aggregate field — set type to Percent)
  Win rate      = SUM(Is won) / SUM(Is closed)
Won value
  Won value     = CASE WHEN Stage = "Won" THEN Amount ELSE 0 END
Deal count (unique)
  Deals         = COUNT_DISTINCT(Deal ID)
Revenue excluding credit notes
  Net revenue   = CASE WHEN Type = "Credit note" THEN -Amount ELSE Amount END
Simple bucketing
  Deal size band = CASE WHEN Amount >= 10000 THEN "10k+" WHEN Amount >= 2500 THEN "2.5k–10k" ELSE "<2.5k" END
Cleaning inconsistent text
  Client clean  = TRIM(UPPER(Client))
Age in days
  Days open     = DATE_DIFF(TODAY(), Date)

Tips
- Monthly trend: change the Date field's type to "Year Month" in the chart, rather than a formula.
- Put row-level CASE fields in the data source; ratios like Win rate must aggregate (SUM(...)/SUM(...)), never average a per-row ratio.
- If a formula errors, paste the error and your field list into the "Write a calculated field" prompt.