The problem

Same data. A dozen different names.

Every source arrives in its own shape — different column names, different codes, different conventions. Standardizing it by hand costs days and quietly introduces errors.

Current reality3 sources · 3 schemas
vendor_a.xlsx
Record_IDCategory_TypeTotal_Amt
vendor_b.csv
RecordNumCategoryTypeAmount
vendor_c.json
Rec_NumberItemTypeCost
One dictionarystandardized
record_number
← Record_ID · RecordNum · Rec_Number
category_type
← Category_Type · CategoryType · ItemType
total_amount
← Total_Amt · Amount · Cost

All sources → one consistent format

The manual tax

Getting from chaos to consistency

The work isn't hard so much as endless — and it resets the moment the next batch of files lands.

  1. 01Copy-paste between files
  2. 02Map each column by hand
  3. 03Fix format inconsistencies
  4. 04Translate source-specific codes
  5. 05Re-validate every quality rule
Result2–3 days, every cycle

Why it persists

Why this is hard to solve

Enterprise tools are built for repeatable problems with consistent formats. When the inputs keep changing, the usual approaches break down.

Custom pipelines don't scale

Building ETL for each new source is expensive. When every deal or submission brings a different format, the ROI never arrives.

Sensitive data stays local

PII, financial records, and regulated data can't be uploaded to cloud tools — so the work falls back to the people who own the data.

Excel becomes the fallback

Without a better option, teams hand-map columns, translate values, and check quality in spreadsheets — every single cycle.

There's a faster way to reconcile.

Datally maps, translates, and validates the messy parts for you — and saves the recipe for next time.

See how Datally solves this