Field profiling
Measure missing values, inconsistent formats, and outliers in the agreed sample.
Data preparation
Make source data fit for search and analysis
Search and analysis depend on the records beneath them. This service profiles a chosen dataset, identifies duplicates and inconsistent fields, and establishes practical rules for cleanup and future checks.
01 / Scope
What Data Quality covers
Identity and duplicates
Define when two records represent the same entity and when they should remain separate.
Repeatable checks
Create validation rules that catch known errors as new data arrives.
02 / Method
A clear path through the work.
- 01
Inspect the sample
Review schema, examples, known defects, and downstream uses.
- 02
Agree on rules
Resolve ambiguous fields with business owners and document exceptions.
- 03
Apply and verify
Run a pilot cleanup and compare results with accepted examples.
03 / Outputs
Know what your project includes.
- Data quality report
- Cleaning and matching rules
- Pilot dataset or implementation plan
Specific scope, compatibility, schedule, and commercial terms are confirmed in a project agreement.
04 / Questions
A few useful details.
Will you fix all historical data?
The scope defines which records and defect types are addressed.
Can deduplication be fully automatic?
Some matches require human decisions, especially when identifiers are weak.
Will this change our source system?
Any write-back or migration is separately designed and approved.
Your next useful answer
Bring us your business question.
We’ll connect your information requirements to the right Nakkeb product, service, or implementation scope.