Quality assessment
Mapping sources, critical fields, business rules, duplicates, gaps, and discrepancies.
Valiant's core01
Data Cleansing
We turn fragmented sources into complete, consistent data ready to generate value.
Talk to ValiantWhat we solveContext, method,
Context, method,
and execution.
Data cleansing is the foundation for reliable reports, safe automation, and better decisions. Valiant maps sources and business rules, identifies inconsistencies, and creates a controlled process for cleansing, standardization, reconciliation, enrichment, and continuous monitoring.
Areas of work
The service in practice.
Cleansing and standardization
Correcting formats, naming, domains, and structures to establish consistency.
Deduplication and reconciliation
Identifying equivalent records and consolidating a single, trusted view.
Enrichment and validation
Completing attributes and applying automated rules to improve completeness and accuracy.
Integration and traceability
Pipelines across legacy systems, spreadsheets, APIs, logs, and decentralized databases, with data lineage.
Governance and monitoring
Indicators, alerts, and controls that preserve quality after the initial delivery.
How we workFrom the need
From the need
to continuous evolution.
Map
We understand sources, owners, rules, and business impacts.
Treat
We apply cleansing, standardization, reconciliation, and enrichment rules.
Validate
We compare results, record evidence, and obtain approval from responsible areas.
Sustain
We automate controls and monitor the evolution of quality indicators.
What may be included
in the scope.
- Quality assessment, source map, critical fields, and business rules.
- Cleansing, standardization, deduplication, reconciliation, and enrichment routines.
- Pipelines, automated validation, indicators, alerts, and traceability.
- Applied-rule documentation and a continuous monitoring plan.
The context calls for
this service.
- Reports, integrations, or departments show different figures.
- Duplicates, gaps, incomplete records, or inconsistent formats exist.
- BI, AI, migration, or automation projects require a trusted foundation.
- Data quality must remain controlled after initial delivery.
Choices aligned
with your environment.
- Quality rules and data catalog
- ETL, APIs, and automated pipelines
- Lineage, audit, and indicators
- LGPD and security from the source
Value perceived
in operations.
- A single, consistent view
- Less rework and fewer errors
- Reliable reports and integrations
- Data ready for AI, BI, and automation
Data quality questions
Data cleansing: frequently asked questions.
Clear answers for companies that need to correct inconsistent databases and establish a trusted source of information.
What is data cleansing?
Data cleansing is the process of diagnosing, correcting, standardizing, deduplicating, and validating information. It also establishes rules and controls so the same issues do not return after the initial cleanup.
When does a company need data cleansing?
It is useful when reports disagree, records are duplicated or incomplete, integrations fail, or BI, CRM, automation, and artificial intelligence initiatives cannot trust the available data.
How does a data quality project work?
Valiant maps sources, owners, and business rules, measures issues, applies controlled treatments, validates results with stakeholders, and automates indicators for continuous quality monitoring.
Does data cleansing help BI and artificial intelligence projects?
Yes. Indicators, predictions, and automation depend on complete, consistent, and traceable data. Cleansing reduces analytical errors and creates a safer foundation for BI and AI.
Next step
Discuss this service Let's understand your scenario.
Share the challenge, current environment, and result you need to achieve. The conversation starts with context.