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:
The Data Infrastructure & Analytics industry includes companies that provide solutions for unifying and integrating data from disparate sources into data storage platforms. They also provide tools for analyzing stored data, deriving insights, creating visualizations, and ensuring data quality. The Integration Solutions segment received the most funding, as data integration is one of the most challenging and time-consuming tasks faced by data scientists and analysts.
Incumbent activity was also observed across all segments, with companies offering either end-to-end data analytics platforms or point solutions that can be customized to meet specific organizational needs. In addition, incumbent offerings also provide a seamless integration with their existing cloud computing stack and machine-learning infrastructure solutions.
Many startups in the Data Infrastructure sector have been established over the last decade, with most of them in the go-to-market or expansion stages. A majority of companies also operate across multiple segments, providing unique solutions that cater to different aspects of the data infrastructure value chain or serving as an end-to-end product. The analytics and modeling tools stands out as the highest funded, accumulating over USD 13.8 billion in funding as of September 2024, accounting for a little over 29% of the total industry funding.
Databricks, a provider of a data lakehouse platform with embedded integration and analytics capabilities, was the highest-funded startup in the space, having raised over USD 4.2 billion as of September 2024 with a valuation of USD 43 billion (attained in September 2023). Notably, many startups across all the segments have already attracted large sums of funding exceeding USD 100 million.
Incumbents in the Data Infrastructure and Analytics space encompass major cloud vendors, like Microsoft, Amazon, and Alphabet, alongside software giants, such as Salesforce, SAP, and HCL Technologies. These key players operate throughout the entire Data Infrastructure value chain, offering solutions that seamlessly integrate with their cloud computing stack and machine-learning infrastructure solutions. They provide end-to-end platforms for data analysis or point solutions that can be tailored to meet the specific needs of organizations.
While these incumbents primarily develop solutions in-house, the industry also witnesses a significant trend of acquisitions aimed at augmenting and consolidating product capabilities. 2019 was particularly remarkable for M&As in this space, propelled by the USD 15.3 billion Salesforce-Tableau and USD 2.6 billion Google-Looker deals.
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