The importance of data quality in the digital age: how businesses can make the most of their data.
Data quality is essential to ensure that decisions based on data are accurate and reliable. Several factors contribute to data quality, including:
Accuracy: Data must be accurate and error-free.
Completeness: Data must be complete, i. e. important values must not be missing.
Consistency: The data must be consistent with each other, i. e. they must follow the same rules of formatting and nomenclature.
Timeliness: Data must be up-to-date to reflect the current situation.
Integrity: Data must be protected against unauthorized changes.
To ensure data quality, it is important to implement processes and tools to detect and correct errors, as well as regularly monitor and update data. In addition, it is important to have a team of professionals trained to handle and analyse the data.
Microsoft offers several tools that can help ensure data quality:
SQL Server: It is a database management system that allows you to store, manage and analyze large volumes of data. It includes features to detect and correct data errors, as well as to ensure data integrity.
Power BI: It is a data visualization tool that allows you to create reports and dashboards to analyze and monitor data. It allows you to connect and combine different data sources, and has features to detect and correct data errors.
Azure Data Factory: A cloud service for data integration, transformation and management. It makes it possible to automate data processes and ensure data quality by implementing data quality rules.
Azure Data Catalog: It’s a cloud service for capturing, discovering and using metadata. It enables users to better understand data and ensure data quality by implementing data quality rules.
Azure Machine Learning: It’s a cloud service for machine learning. It includes features to detect and correct data errors and ensure data quality by implementing data quality rules.
In short, data quality is essential to ensure that decisions based on data are accurate and reliable, and this requires the implementation of processes and tools to detect and correct errors, as well as regular monitoring and updating of data.
