Compare Ab Initio vs. DataBuck

Information flows in from various sources, increasing in both volume and intricacy. Within this information lies valuable knowledge and insights brimming with potential. This potential can only be fully harnessed when it influences every decision and action taken by the organization in real-time. As the landscape of business evolves, the data itself transforms, yielding fresh knowledge and insights. This establishes a continuous cycle of learning and adaptation. Sectors as diverse as finance, healthcare, telecommunications, manufacturing, transportation, and entertainment have acknowledged the opportunities this presents. The journey to capitalize on these opportunities is both formidable and exhilarating. Achieving success requires unprecedented levels of speed and agility in comprehending, managing, and processing vast quantities of ever-evolving data. For complex organizations to thrive, they need a high-performance data platform designed for automation and self-service, capable of flourishing amidst change and adjusting to new circumstances, while also addressing the most challenging data processing and management issues. In this rapidly evolving environment, organizations must commit to investing in innovative solutions that empower them to navigate the complexities of their data landscapes effectively.

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.