For two years we have worked on qualifying a database that today exceeds 30,000 users.
It has not been a clean process.
Not fast.
Not automatic.
It has been a mix of method, tools and quite a bit of manual work.
The problem: data that exists, but doesn't work
Most organizations do not have a volume problem.
They have a utility problem.
- incomplete records
- inconsistent information
- duplicates
- lack of clear criteria
The result is well known: large databases, but useless for working.
The approach: qualifying is making decisions
It's not about cleaning data.
It's about deciding which data is valid and which is not.
To reach 30,000 qualified users we combined:
- manual qualification
- automated processes
- structured forms
- internet research
- phone calls
- crosses with external databases (census)
- Use of AI to automate crossing processes
There is no single way.
There are many, and you have to use them all.
The reality: not everything is automated
Especially in the case of the self-employed, the information is unclear.
This is where they come in:
- rules
- criterion
- manual review
AI helps.
But it does not replace the decision.
The sentinel: keeping the data alive
The biggest mistake in these types of projects is thinking that they end when the base is qualified.
It doesn't end.
If it is not maintained, it deteriorates.
That's why we incorporated the sentinel function:
- periodic review of data quality
- update existing records
- incorporation of new contacts
- maintenance of criteria and nomenclature
It's not a complex technical role.
It is a constant function that prevents the base from returning to the starting point.
The result: a foundation that can be worked on
More than 30,000 qualified users.
But the important thing is not the number.
Because now it exists:
- consistency in the data
- real segmentation
- activation capacity
The database ceases to be a file.
It becomes a tool.
What comes next
This is where the mistake is usually made.
Thinking that the project is over.
It doesn't end.
Begin.
Because now it's possible:
- launch targeted campaigns
- automate business processes
- working with AI models on real data
- build assistants that add value
And, above all:
Keep the database updated so that it remains useful over time.
Conclusion
The data is not the problem.
The problem is not being able to use them.
And neither is qualifying them once.
The real challenge is keeping them.
Because a database only has value if it is still valid tomorrow.
Closing
If you have a database that you're not using, you don't need any more records.
You need to be able to work with the ones you already have… and keep them alive.
