Compare DataBuck vs. Trackingplan

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.

Trackingplan serves as a marketing observability solution aimed at streamlining tasks for analysts and tagging professionals. By consistently monitoring web and app traffic, it proactively alerts users to potential issues before they escalate, facilitating swift troubleshooting to uncover the source of any problems. Designed for ease of use, Trackingplan is a plug-and-play tool that automatically detects your entire analytics, product, and marketing ecosystem, commencing its monitoring process after a brief learning period. Additionally, each day, users receive a comprehensive summary report detailing the status of their websites and applications, covering aspects such as traffic associated with campaigns and the performance of data transmitted to marketing pixels. This daily overview not only enhances awareness but also supports informed decision-making, ensuring that businesses can maintain optimal performance.