Data management

Incorvus’ data management follows a logical path through the data lifecycle to prepare your data before poor quality or suitability distorts information architecture & analytics’ visualisation – or beyond that AI or ML! Looking after data repositories, infrastructure, processing and practices, as distinct from the stewardship of the data itself, should create organisational confidence. But…

Data validation

Data validation tests the accuracy, consistency (of format and standards), quality and integrity of the data (& associated metadata) for onward information engineering, data provisioning and curation. Note that ‘reliable’ data still needs to be validated. Even though this is not its primary purpose, data anomalies revealed by validation techniques can reveal hitherto unnoticed business…

Data transformation

It seems obvious, but migrating to cloud can be a frustrating process if your data is not suitably transformed before you try the ETL process. As data volumes get bigger; formats more varied and growth, exponential, data transformation techniques are vital. Routine transformations ensure harmony of data format and target destination. But more importantly, data…