Even the best reports and predictive models are useless if the data is incomplete, inconsistent, or outdated.
That’s why data quality is not just a technical issue – it’s the foundation of trust, efficiency, and effective operations.
Data mismatches between systems
Reports showing different values than Excel
Missing or outdated data
Errors in product codes, customers, or dates
Difficulty determining which data source is reliable
We implement data validation, monitoring, and cleaning processes
We create quality control rules (e.g., missing values, duplicates, incorrect formats)
We help build data governance and data dictionaries
We introduce automated data alerts and audits
We support organizations in establishing data owners and roles (Data Owner, Steward)
Microsoft Fabric Data Quality and Dataflows
Power Query, Power BI, SQL
Azure Data Factory + validating pipelines
Data Profiling and data quality rules in the data warehouse
Have multiple data sources and integrations
Are building or expanding a data warehouse
Want to avoid wrong decisions based on inaccurate data
Are building a data-driven culture
Data quality rule system for a logistics company – validating GPS data, delivery times, and costs
E-commerce data quality audit – cleaning product codes and incorrect categories
Automated quality alerts for management – email notifications about anomalies in sales data
Data dictionaries and field standardization – eliminating discrepancies between systems
Managing data quality is an investment that pays off quickly – in the form of better decisions, fewer errors, and greater trust in reports.
Let’s start with a quick diagnosis – we’ll show you what needs improvement.