Relish Data Assure Reviews

Propel is a modern, AI-powered cloud platform built for how manufacturers work today. It unifies Product Lifecycle Management (PLM), Quality Management System (QMS), and Product Information Management (PIM) into one intelligent system, giving teams a connected, always-accurate view of their products. With AI embedded across the platform through Propel One, manufacturers can automate routine work, surface insights faster, and make better decisions with confidence. AI continuously learns from product data to proactively flag risks, highlight trends, and guide next best actions across teams. Instead of relying on spreadsheets, siloed tools, and manual handoffs, teams manage product data, changes, and quality processes in a single governed environment with full traceability. Propel creates a true digital product record that spans design, development, manufacturing, commercialization, and continuous improvement. Built-in workflows standardize change management, streamline quality events, and support compliance without slowing teams down. Trusted by medical device, high tech, and industrial manufacturers, Propel helps organizations reduce errors, shorten cycle times, and improve collaboration across engineering, quality, operations, supply chain, and product teams. The result is faster launches, higher product quality, and a more resilient foundation for growth as products and processes become more complex. Propel has been recognized by Deloitte as a fast-growing technology company and named to the Inc. 5000 list of fastest-growing private companies, reflecting strong customer adoption and momentum helping manufacturers modernize how they build, manage, and deliver products. Designed to scale with growing products, teams, and regulatory demands.

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.