Compare DataBuck vs. Golden Record

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.

Organizations accumulate vast amounts of data in various systems and applications spread across multiple departments, including CRMs, marketing automation tools, POS systems, digital product catalogs, event ticketing platforms, and more. Golden Record is a data matching tool that facilitates the integration of business data across these diverse systems, enabling deduplication, record matching, and real-time synchronization. Instead of manually exporting data into spreadsheets and CSV files, which can be time-consuming as you compare fields and create corrected versions for re-importing, you have the option to collaborate with a data specialist to assess your requirements and databases, leading to the development of a tailored application that consolidates everything efficiently. Alternatively, you might invest in costly data integration software and spend months configuring it to connect your current systems effectively. Ultimately, the right approach can significantly streamline your data management process and enhance operational efficiency.