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 quality

Poor data quality, taking a reactive, rather than proactive, stance to data errors is a massive own goal. DQ negatively impacts the business so that: Financial data, trades & transactions, are highly regulated and subject to time stamping. Delays and mistakes are not only corrosive, they have much more immediate and damaging implications. The European…