Compare Blazent vs. DataBuck

Achieve a remarkable 99% accuracy rate in your CMDB data and ensure that it remains consistently high. Eliminate the time taken to determine source systems for incidents, effectively bringing it down to zero. Attain full visibility into risks and SLA exposure to better manage potential issues. Streamline service billing processes to avoid under billing and clawbacks, while also minimizing the need for manual billing and validation efforts. Cut down on maintenance and licensing expenses related to decommissioned and unsupported assets. Foster trust and transparency by significantly reducing major incidents and accelerating outage resolution times. Address the constraints of Discovery tools and enhance integration across your entire IT infrastructure. Promote collaboration between ITSM and ITOM teams by merging various IT data sets into a cohesive framework. Achieve a comprehensive understanding of your IT landscape through ongoing CI validation from the widest array of data sources. Blazent ensures data quality and integrity through a commitment to 100% data accuracy, transforming all your IT and OT data from the most extensive sources in the industry into reliable, trusted information. This holistic approach not only optimizes your operations but also empowers your organization to make informed decisions with confidence.

Big Data Quality must always be verified to ensure that data is safe, accurate, and complete. Data is moved through multiple IT platforms or stored in Data Lakes. The Big Data Challenge: Data often loses its trustworthiness because of (i) Undiscovered errors in incoming data (iii). Multiple data sources that get out-of-synchrony over time (iii). Structural changes to data in downstream processes not expected downstream and (iv) multiple IT platforms (Hadoop DW, Cloud). Unexpected errors can occur when data moves between systems, such as from a Data Warehouse to a Hadoop environment, NoSQL database, or the Cloud. Data can change unexpectedly due to poor processes, ad-hoc data policies, poor data storage and control, and lack of control over certain data sources (e.g., external providers). DataBuck is an autonomous, self-learning, Big Data Quality validation tool and Data Matching tool.