Data Cleansing Services
Find, correct and reject bad data before it damages a database, migration or report.
Data cleansing services from ETL Tools fix practical data defects: duplicates, invalid values, inconsistent text, incorrect dates, invalid codes, missing fields, corrupted rows and obsolete records. Bad data is not improved by loading it faster. It just becomes wrong at scale.
Data cleansing starts with identifying what is actually wrong
We profile the source data before changing it. That means checking values, formats, lengths, nulls, duplicates, date ranges, code lists and structural errors. Guessing from the first ten rows is how production systems learn new swear words.
- Inaccurate data
- Duplicate data
- Invalid values
- Inconsistent text and casing
- Incorrect or ambiguous dates
- Invalid codes
- Missing required data
- Corrupted records
- Obsolete records
Cleansing rules should repair safely and reject honestly
Duplicate removal
Match rows using keys, normalised values, timestamps and survivorship rules. Uncertain matches can be reported instead of merged silently.
Text cleaning
Trim spaces, normalise casing, remove control characters, standardise labels and prepare values for matching.
Lookup-based correction
Use reference tables to correct known values such as codes, branches, product groups, statuses and approved labels.
Fuzzy matching
Identify likely matches for names, addresses or labels where exact matching is too strict. Review thresholds matter here.
Error identification
Find invalid dates, impossible numbers, missing keys, broken references and rows that shifted because a file structure is damaged.
Reject handling
Write bad rows to an exception file, table or report with the reason clearly recorded.
Cleansing belongs before mapping, import and reporting
Cleansing is usually done before database import, migration, integration or reporting. The workflow can keep raw data unchanged, write cleansed data to staging, validate the result and then load the target.
For the background concepts, read the Data Cleansing in ETL guide and the Data Transformation Services hub.
Send enough detail for a useful first answer
For a quote, include sample data if possible and describe the current source, required output and how often the process runs. A tiny example file is often more useful than a long meeting. Nobody has ever said that about a long meeting.
- Sample data or representative field list
- Source format and destination format
- Approximate record volume
- Transformation, validation and mapping requirements
- Processing frequency and delivery deadline
- Required output files, tables, reports or exception logs
Frequently Asked Questions
Can you remove duplicate records?
Yes. We can design duplicate detection and removal rules using keys, normalised values, dates, confidence rules and exception reports.
Can corrupted records be fixed automatically?
Some can. Rows with structural damage are usually safer to quarantine and report, especially when columns have shifted or encoding is broken.
Do you change the original data?
In most projects, no. The raw source is kept unchanged and corrected output is written separately.
Can cleansing run every month?
Yes. Recurring cleansing is a good fit for an automated ETL workflow.