Figure 1 Big Data And GenAI Are Increasing Storage Needs The primary culprit in the growth of stored data is the embracing of new big-data techniques that create and use more data, such as genAI. Twenty-seven percent of enterprise infrastructure hardware decision-makers that we surveyed indicated that big-data techniques were one of the main reasons their firm was increasing its storage (see Figure 2).

The early 2000s marked the explosion of big data, massive volumes of unstructured and semi-structured data generated by the web, social media, sensors, and machines. Traditional data warehouses couldn’t scale or adapt, leading to the emergence of the data lake, which was a low-cost, schema-on-read architecture designed to ingest all data in its raw format.

Beagle Data Beagle Data is a data infrastructure and AI platform provider that specializes in data modeling and risk analysis for financial institutions. It was founded in 2015 and is headquartered in Beijing. Strategy. Beagle Data aims to become the supplier of core AI tools; over the years, it has expanded its offerings from big data and predictive AI to generative and agentic AI products.

This graphic has an associated spreadsheet that includes all the data presented. Please access the spreadsheet for details. Figure 2 Top Use Cases We’ve identified the following core use cases for this market: backup, archive, and compliance storage; big data and AI/ML analytics storage; cloud-native and container app data; data foundation for AI inferencing; and high-performance object storage.

Alibaba Cloud offers traditional functionality from compute, storage, database, and big data and emerging AI functionality like AgentBay that supports automation and remote control across browsers, desktops, mobile devices, and code. Top Software Vendors Have The Largest Margins But Also See The Most Variability A boxplot depicts the average margins and their variability for public software, IT services, and computer equipment companies from 2023 to 2024.

As data volumes, sources, and use cases expanded, these tightly controlled models became harder to scale and slower to change. The rise of big data and cloud platforms necessitated more flexible, iterative approaches that could accommodate diverse data types and meet faster processing demands.

Market Maturity The practice of customer analytics goes back decades, but the emergence of a market for customer analytics technologies began as companies sought to extract value from the growing volumes of big data they were capturing. As customer centricity took hold, organizations scrambled to understand increasingly empowered customers and turned to analytical platforms to uncover meaningful signals buried in their data (see Figure 1).

It is a cloud-native platform built on big data infrastructure that prioritizes analyst experience to enable high-quality detection; comprehensive investigation; and fast, effective response. Customers use XDR platforms to improve their detection, investigation, and response capabilities. XDR enables customers to augment their detection engineering work, as the XDR vendor provides broad visibility and deep threat research and expertise.

Multimodel Data Platforms Power Modern Data Needs The rise of agentic AI is exposing a hard truth: Faster databases and bigger lakes can’t power autonomous decision-making by themselves. Enterprises now require a single, intelligent foundation that natively handles every data model, delivers real-time analytics, and embeds governance within the platform.

Become Metadata-Driven To Scale With Data As A Service Data fabrics are evolving with cloud-native capabilities to deploy data as a service. DaaS platforms create headless and decoupled architectures for data at scale and enable adaptability that traditional data integration and delivery lack. And as enterprises are buying in, big data integration is the most important component for data and analytics.