The term “Big Data and analytics” has grown in prominence over the past decade, as companies of all sizes increasingly rely on data-driven insights for virtually every facet of decision-making. Data Infrastructure solution providers help organizations access and unlock insights hidden in the massive volumes of data they generate and collect through interactions with suppliers and customers from multiple sources, including social media interactions, point-of-sale systems, and supply chain platforms. These vendors offer solutions for consolidating data from disparate sources, transforming and storing it in a usable format, and providing tools for analysis and visualization. They also help curate and validate the data needed to build machine-learning and AI applications.
Over the years, Data Infrastructure platforms have evolved to keep up with advances in technologies, such as AI, which has enabled autonomous data integration, real-time data processing, and predictive analytics. In addition, the recent introduction of GenAI has led to the proliferation of new tools that aim to democratize data analysis, enabling employees—regardless of their technical skills—to gain insights from data in a conversational way.
Today, businesses generate large amounts of data as part of their operations; as a result, software that supports the integration and analysis of this data can become powerful tools that help these businesses gain insights that may not have been otherwise possible. Data infrastructure and analytics solutions are being deployed across a wide range of industries, with use cases ranging from self-serve, real-time analytics for multiple business functions to solutions for specific company workflows, such as financial analysis, working capital management, and product development.
These solutions also help organizations better manage and leverage their data by implementing advanced search capabilities and improving data quality and streaming capabilities.
We have identified key data infrastructure use cases below: