Does this sound familiar?
If you recognise any of these situations, it is worth spending 30 minutes reviewing whether Data cleansing and validation is the right solution for your organisation.
Data cleansing and validation
Clean, reliable data — for the long term.
We systematically identify and resolve issues in existing datasets — duplicates, missing and conflicting values, outdated records — and implement built-in rules to ensure data remains clean over time.
measured data quality · careful review · maintained quality
30 minutes · online · no obligation
Not a one-time clean-up where the data becomes messy again six months later. Built-in validation ensures the data is not only clean today, but stays clean in the future as well.
THE PROBLEM
Poor data becomes accepted and worked around
Everyone knows about the problem of “duplicate items” or “outdated records”, but nobody owns it and there is no metric attached to it.
“the same item appears under multiple names and item codes” — duplicates distort reporting
“many fields are empty or outdated” — incomplete data leads to poor decisions
“everyone records data in a different format” — data becomes difficult to compare and search
WHAT IT DOES IN PRACTICE
A family of capabilities, assembled as needed
Data quality audit
We first show the problem with numbers — not assumptions.
Duplicate management
Identifying and merging records that represent the same entity.
Missing and incorrect data
Completion, flagging and correction of clearly incorrect values.
Normalisation and standardisation
Dates, addresses, phone numbers and names — converted into a consistent format.
Validation rules
Errors can be detected at the point of entry — helping data remain clean over time.
Continuous monitoring
Recurring checks ensure deterioration is identified early.
Data cleansing and validation — detailed product overview
Download the complete product overview — suitable for offline reading and easy sharing with teams and decision-makers.
You know there are issues in the data — you just do not know how serious they really are.
The most common situation is that everyone senses there is a data quality problem, but nobody can clearly see its scale. Through a data quality audit, we quantify the issues and show where to start improving.
THE PRINCIPLE
Duplicate detection is not deletion
“Remove the duplicates” sounds simple, but deciding what truly represents the same entity requires experience and a careful, controlled approach.
We start with auditing and measurement
We quantify the issues first — without this, cleansing becomes guesswork.
No deletion without review
Anything that cannot be determined with certainty is flagged and reviewed rather than deleted — helping prevent data loss.
WHY OMNIT
What makes us different
Measurable data quality
We begin with an audit, show the issues with numbers and measure the improvement as well — making the results visible.
Maintained, not one-time
Most cleansing projects become messy again within six months. We implement rules to keep the data clean.
15+ years of data engineering and data quality experience
We know the most common data quality issues and how to handle them carefully without causing data loss.
FREQUENTLY ASKED QUESTIONS
Before you start
It might be — which is why we start with an audit that shows the actual condition through numbers. We do not assume; we measure.
That is valuable — but without validation rules, data quality deteriorates again as new records arrive.
A fair question — which is why every uncertain case is reviewed and discussed separately.
BOOK A demo
What is the real condition of your company’s data?
A data quality audit shows the actual situation with numbers — and identifies the first step that delivers the greatest improvement.
Bad data only helps AI make bad decisions faster.
CONTACT
First step
Fill in the form below and we will contact you within 24 hours. Or download the detailed “Data cleansing and validation” product overview.
Related posts
See all
- AI Building Blocks
- 8 mins
One of the biggest challenges users face with AI tools is AI hallucinations. AI hallucinations are responses created by the AI to satisfy the user, even when they are not based on facts. These hallucinations often...
- AI Building Blocks
- 5 mins
You are constantly exposed to text—emails, reports, contracts, chat messages, documentation, and presentations. Natural Language Processing (NLP) is a set of methods and tools designed to help you manage and understand all this text more efficiently....
- AI Building Blocks
- 8 mins
You're constantly exposed to text — emails, reports, contracts, chat messages, documentation, and presentations. Large Language Models (LLMs) are tools created to help manage large amounts of text more easily. They can assist you in summarizing,...
- AI In Business
- 7 mins
AI isn’t here to steal jobs — it is here to take the boring stuff off your plate. According to the Edelman Trust Barometer, more people are worried that machines will replace their jobs through automation....