The IDC MarketScape: Asia/Pacific excluding Japan (APeJ) Analytic Data Platforms for Decision Support 2023 Vendor Assessment, identified AWS, Google, Microsoft, and Oracle as “Leaders” among the vendors in the software category, Analytic Data Platforms for Decision Support. IDC also listed (in alphabetical order) Alibaba, Cloudera, Huawei, IBM, Snowflake, and Teradata as major players in the market.
In the current volatile economic landscape, the ability to move ahead with business priorities largely depends on an organisation's data-driven decision-making capability — its enterprise intelligence (EI).
Improving EI or data-driven decision-making will often require concerted investments and actions at multiple levels: from data platforms (to enable more openness, flexibility, scale, and connectivity) and pipelines and processes (to enable more effective and consistent data processing to make data insight-ready) to tools (to build and deliver analytics and insights), decision-making and action-taking processes, and culture.
Modernising decision support systems is the key to data-driven decision-making and must evolve to keep up with the rapid pace of innovation. Organisations must leverage high-performance and scalable analytic data platforms that can work on a variety of data types and deliver results for multiple user personas.
Organisations globally and in Asia/Pacific are increasingly relying on cloud-based data platforms to modernise data workloads and report optimal benefits. This could be crucial for organisations exploring options to future-proof their EI and decision-support capabilities.
"Given the global economic downturn, businesses need to continue to leverage analytics and AI across the board to improve productivity and operational efficiencies," says Deepika Giri, associate vice president for artificial intelligence and analytics strategies at IDC Asia/Pacific.
"This has accelerated the need for a robust, yet nimble decision support platform that can balance and serve the needs of the user from scalability, performance, reliability, and time-to-value perspectives, depending on the use case it is designed to deliver."
Deepkia Giri
