The Data and Analytics (D&A) and AI market continues to grow rapidly, fueled by expanding enterprise data and AI-ML advances. Organizations are turning toward D&A and AI solutions to drive digital transformation, enhance decision-making, and scale operational efficiency. At re:Invent 2024, AWS showcased its commitment to advancing the D&A and AI ecosystem with impactful updates to its platforms and tools and introducing new generative AI foundational models. These enhancements emphasize scalability, multi-model flexibility, and enterprise readiness, addressing the complete AI life cycle, from data processing to model deployment, while maintaining a strong focus on security and governance. In this report, we analyze AWS’ latest announcements from re:Invent 2024, identifying growth opportunities, enterprise challenges, and the company’s competitive positioning. The report further delves into AWS’ key offerings, major differentiators, and areas where additional refinements could enhance its value proposition. Scope All industries and geographies The assessment is based on Everest Group’s participation in and monitoring of announcements made at the AWS re:Invent 2024 event Contents In this report, we: Key themes driving and inhibiting enterprise demand for D&A and AI AWS’ current positioning in the D&A and AI market Announcements from AWS re:Invent 2024 Membership(s) Data & Analytics Artificial Intelligence (AI) Sourcing and Vendor Management

The Data and Analytics (D&A) and AI market continues to grow rapidly, fueled by expanding enterprise data and AI-ML advances. Organizations are turning toward D&A and AI solutions to drive digital transformation, enhance decision-making, and scale operational efficiency. Microsoft has solidified its position as a key provider in the D&A and AI ecosystem, offering end-to-end solutions that seamlessly integrate into enterprise environments. At Ignite 2024, Microsoft unveiled innovations designed to streamline data workflows, advance generative AI capabilities with a focus on AI agents, and reinforce security and governance to meet enterprise-grade requirements. These updates aim to improve productivity, drive automation, and enable context-aware decision-making across industries. In this report, we analyze Microsoft’s latest announcements from Ignite 2024, identifying growth opportunities, enterprise challenges, and the company’s competitive positioning. The report further delves into Microsoft’s key offerings, major differentiators, and areas where additional refinement could enhance its value proposition. Scope All industries and geographies The assessment is based on Everest Group’s participation in and monitoring of announcements made at the Microsoft Ignite 2024 event Contents In this report, we examine: Key themes driving and inhibiting enterprise demand for D&A and AI Microsoft’s current positioning in the D&A and AI market Announcements from Microsoft Ignite 2024 Membership(s) Data & Analytics Artificial Intelligence (AI) Sourcing and Vendor Management

The Data and Analytics (D&A) and AI market is rapidly expanding, driven by growing data volumes and AI-ML advances. Enterprise demand for D&A and AI primarily stems from their need to accelerate digital transformation, optimize operations, and drive scalability. IBM plays a key role in the D&A and AI landscape, enabling enterprises to efficiently build, deploy, and govern AI models while driving AI-powered analytics and ensuring stringent data privacy and compliance. Innovations and updates announced at the TechXchange 2024 event focus on enhancing generative AI efficiency, bolstering AI governance, and accelerating business automation with agentic workflows. In this report, we examine IBM’s latest updates from TechXchange 2024, outlining growth opportunities, challenges enterprises face, and IBM’s current positioning and new offerings in this space. The report also highlights IBM’s key differentiators and potential improvement areas. Scope All industries and geographies The assessment is based on Everest Group’s participation in, and monitoring of announcements made at the IBM TechXchange 2024 event Contents In this report, we examine: Key themes driving and inhibiting enterprise demand for D&A and AI IBM’s current positioning in the D&A and AI market Announcements from IBM TechXchange 2024 Analyze the new release’s strengths and areas of improvement Membership(s) Data & Analytics Sourcing and Vendor Management

Snowflake is a cloud-based data platform that unifies data management, analysis, and sharing. It provides a centralized location for structured and unstructured data, enabling data-driven insights and innovation. The platform’s architecture supports scalability, concurrency, and data sharing and incorporates data security and governance features. In this report, we examine Snowflake’s latest updates from its Data Cloud Summit 2024, explore growth areas and challenges in the field, evaluate Snowflake’s offerings, highlight their unique features, and suggest what could be better. Scope All industries and geographies This assessment is based on Everest Group’s tracking of Snowflake’s Data Cloud Summit 2024 Contents In this report, we examine: Key enterprise demand themes Snowflake’s current positioning in data and analytics Snowflake’s Data Cloud Summit 2024 announcements Everest Group’s review of Snowflake’s Data Cloud Summit 2024 product announcements Membership(s) Data & Analytics Sourcing and Vendor Management

Databricks, a key player in the data and analytics market, delivers a cloud-based platform that enables enterprises to manage, analyze, and harness the value of their data. Its data intelligence platform aims to provide a unified solution for data storage and an AI-led innovation toolkit while maintaining enterprise data security. In this report, we examine Databricks’ latest updates from its 2024 Data + AI Cloud Summit, explore growth areas and challenges in the field, examine Databricks' offerings, highlight their unique features, and suggest what could be better. Scope All industries and geographies The assessment is based on Everest Group’s tracking of Databricks’ Data + AI Cloud Summit 2024 Contents In this report, we examine: Key enterprise issues and objectives Databricks' current positioning in data and analytics Databricks’ Data + AI Cloud Summit 2024 announcements Everest Group’s review of Databricks’ Data + AI Cloud Summit 2024 product announcements Membership(s) Data & Analytics Sourcing and Vendor Management

The data and analytics market is evolving rapidly due to a surge in data volume and advances in AI and ML. Businesses are increasingly using data analytics to gain insights, improve decision-making, and drive innovation. Key trends include integrating big data, real-time analytics, and predictive modeling. Google is among the top three cloud providers, offering platforms such as BigQuery for data storage, AI-driven analytics, and ML platforms. Innovations and updates from Google Next 2024 will directly impact how enterprises manage data and streamline their analytics processes. In this report, we examine Google’s latest updates from Google Next 2024 on data and analytics. It explores growth areas and challenges in the field, outlines Google’s current offerings, highlights unique features, and suggests ways Google can further improve its offerings. Scope All industries and geographies The assessment is based on Everest Group’s participation and tracking of announcements at the Google Next 2024 Contents In this report, we examine: Key enterprise issues and objectives Google’s current positioning in data and analytics Google Next 2024 announcements Everest Group’s review of Google Next 2024 product launch Membership(s) Data & Analytics Sourcing and Vendor Management

Driven by the exponential growth of data and the impact of the pandemic, enterprises have rapidly adopted Artificial Intelligence (AI) as a strategic tool to gain a competitive edge and enhance their business models. Recognizing its potential, they seek to leverage AI to reduce dependency on human workforce and unlock new revenue streams while cutting costs. As a result, enterprises are striving to develop improved AI tools and technologies. However, implementing AI has its own set of challenges such as availability of high-quality curated data and responsible AI implementation. In this report, we discuss the importance of high-quality curated data in the success of enterprises’ AI initiatives. We explore different aspects of preparing high-quality data such as data annotation, using synthetic data when real data is insufficient, incorporating a human-in-the-loop approach, and ensuring data inclusivity and mitigation of biases. Additionally, the report examines emerging trends in AI data services that enterprise should consider before making implementation decisions. Scope  All industries and geographies Contents In this report, we examine: AI / Machine Learning (ML) life cycle Features and benefits of data annotation and labeling Importance of AI-assisted data annotation and synthetic data Human workforces’ role in the AI life cycle Data annotation and labeling services ecosystem Emerging trends in AI data services Membership(s) Artificial Intelligence (AI) Sourcing and Vendor Management

Data is universally accepted as a key business asset across enterprises, irrespective of size, scale, industry, and geography. Today, organizations are recognizing the value that data investments can offer and are, therefore, more open to sourcing/buying data from external data providers. As enterprises increasingly operate in networks and ecosystems, the need to look beyond internal data will only rise. In fact, industries are already examining different ways to use external data. External data maximizes business value by improving insights and enables enterprise stakeholders to make better data-driven decisions related to evolving market dynamics, especially in instances where historical data has limited use. To capture the opportunities related to external data adoption, the supply ecosystem – both data providers and tech enablers – has expanded in recent times. In this report, we examine the current state of external data adoption, the booming supply-side ecosystem, and enterprises’ need to reimagine data sourcing and consumption. Scope: All industries and geographies Contents: This report examines: The role of external data in enterprise analytics and penetration across industries Key drivers of external data adoption The booming supply side and the rich ecosystem of external data providers Challenges of external data adoption Enterprise considerations to maximize value from external data investments Membership(s) Data & Analytics Sourcing and Vendor Management

The advent of Robotic Process Automation (RPA) helped enterprises automate documents with structured data sources; however, content-centric documents – accounting for almost 80-85% of enterprise document load – cannot be automated using conventional rules-based solutions. Consequently, manual processing of these documents often leads to issues such as long turn around time, high cost of operations, high incidence of errors, and difficulty in automating heterogenous data. Driven by these factors, enterprises, today, are looking for solutions that incorporate elements of Artificial Intelligence (AI), so as to process documents with unstructured data sources. The AI-based solutions, often called Intelligent Automation (IA) solutions, possess capabilities such as computer vision, machine learning, and NLP that can be integrated with RPA and BPM workflow to provide an end-to-end automation experience. Technological advancements, such as transfer learning, are further easing the barriers or inhibitions that enterprises have while adopting any intelligent automation solution. This viewpoint covers the entire document processing conundrum in detail, with details on applicability of AI to provide an end-to-end automation experience. The sections covered in the viewpoint include: Limitations of RPA and OCR / template-based solutions in processing content-centric data The role of AI in document processing How AI augments RPA and BPM to provide an end-to-end process automation experience Key technologies powering AI capabilities, including transfer learning A case study explaining how an enterprise utilized an intelligent automation solution to automate document processing Membership(s) Service Optimization Technologies (SOT)