SoftwareReviews recently released a list of the most satisfying big data software vendors, based on user reviews, and Amazon Web Services Big Data, IBM InfoSphere, and Microsoft Azure Big Data topped the list.
However, SoftwareReviews found that big data software users were satisfied only with the top vendors’ quality of features, ease of IT administration, and smooth data integration capabilities. Training, intuitiveness and usability of the software turned out to be the factors that the users were least satisfied with. The company says perhaps this is an opportunity for big data software vendors to focus on providing excellence in education, in addition to excellence in features.
Software users provide emotional response ratings in product impact, innovation, strategy, service, conflict resolution, and negotiation. SoftwareReviews calls this insight the net emotional footprint because the company says these ratings create a powerful indicator of overall feeling of the user toward the software vendor and its product from the viewpoint of the software users.
Each of the three winning big data software vendors bagged a gold medal in the 2020 SoftwareReviews Data Quadrant Awards, with their net emotional footprint scores – ranging between 82 per cent and 85 per cent – sitting higher than the average score of 77 per cent.
They were chosen to be the leaders according to the answers of software users to questions that were focused on user satisfaction, developed by seasoned IT industry analysts and backed by 22 years of IT research.
The users of Amazon Web Services Big Data liked its data security, data science tools and analytics the best, and so the software earned top-product spot for each of those. The users of IBM InfoSphere liked its metadata management the best, and so it earned top product for that. The software also bagged the second spot for its real-time capabilities. Microsoft Azure Big Data took the top-product spot for analytics and reporting, and workload management and monitoring.
“Managing big data is quite different from building a traditional data warehouse: the volume and complexity of data, its variable velocity – as well as unpredictability of analytical use cases – requires multi-phased and modularized architecture that is flexible enough to adapt without rebuilding everything from the ground up,” said Igor Ikonnikov, research advisor at Info-Tech Research Group. “A vendor’s ability to provide a complete toolkit for multi-phased and multi-faceted data management and advanced analytics solution implementation – either with own technology or via seamless integration with other technologies – has become the main differentiator in the big data space,” he explained.