Data Quality

The discipline of data quality assurance ensures that data is "fit for purpose" in the context of existing business operations, analytics and emerging digital business scenarios. It covers much more than just technology. It includes program management, roles, organizational structures, use cases and processes (such as those for monitoring, reporting and remediating data quality issues). It is also linked to broader initiatives in the field of enterprise information management (EIM), including information governance and master data management (MDM). Our data quality tools focuses on innovative technologies and approaches intended to meet the needs of end-user organizations. As digital business requires innovations in data quality tools, we are competing fiercely by enhancing existing capabilities and creating new capabilities in eight key areas: audience, governance, data diversity, latency, analytics, intelligence, deployment and pricing.