In the AI and generative AI era, data has evolved from a byproduct of business processes to a strategic asset driving innovation and decision-making. Despite its importance, enterprises face significant challenges in effectively managing and utilizing data, including data silos, low data quality, inefficient discovery processes, and compliance risks. Data catalogs have emerged as a foundational tool to address these issues. By centralizing metadata and providing detailed data inventories, they enable enterprises to discover, understand, and trust their data assets. Modern data catalogs go further by incorporating AI-driven capabilities such as semantic search, knowledge graph visualization, and intelligent recommendations, transforming how organizations access and use data. In this report, we explore data catalogs’ evolution, capabilities, role in solving enterprise data challenges, adoption strategies, and market trends. Scope All industries and geographies Contents In this report, we examine: Data management challenges and business implications for data leaders Data catalogs’ evolution Modern data catalog solutions’ core components Technology provider landscape and innovations The evolving data catalog supplier ecosystem Membership(s) Data & Analytics Sourcing and Vendor Management

The global spend on AI is growing as enterprises across various sectors increasingly leverage its transformative potential. With the emergence of generative AI / large language models, enterprises worldwide are adopting AI at an accelerating pace to unlock new revenue streams, streamline operational costs, enhance user experiences, and establish significant differentiation within their respective industries. However, the foundation of AI development hinges on large training datasets, often tailored to the unique requirements of each business. Data Annotation and Labeling (DAL) services play a vital role in curating these high-quality training datasets essential for AI development. However, maintaining in-house DAL capabilities proves to be costly, time-consuming, and labor-intensive for enterprises. As a result, businesses are turning to external DAL service providers to implement DAL solutions. In their pursuit, enterprises seek partners capable of expediting their time-to-market by executing annotation projects at scale and speed without compromising data quality. They prioritize providers that prioritize building strong relationships, cost-effectiveness, agility, and a steadfast commitment to delivering tangible business impact and RoI at every stage of their transformation journey. Providers with a trained workforce and advanced annotation platform capabilities can efficiently guide these enterprises through the DAL landscape. This compendium provides detailed snapshots of 20 DAL solutions providers featured on Everest Group’s DAL solutions for AI/ML PEAK Matrix® Assessment 2024. Each profile offers a comprehensive overview of the provider’s operational overview, delivery locations, solutions offered, investments, and market success. Scope All industries and geographies The assessment is based on Everest Group’s annual RFI process for the calendar year 2024, interactions with DAL service providers, client reference checks, and an ongoing analysis of the DAL services market Contents In this report, we examine: DAL service providers landscape DAL PEAK Matrix® characteristics Providers’ leadership, presence across geographies and industries, global FTE and revenue estimates, buyer size, and overall practice structures DAL delivery locations and intellectual property overview, along with an analysis of flagship IP and key partnerships across the DAL value chain Provider investments in DAL across talent, infrastructure (centers of excellence / labs), acquisitions, research, academic partnerships, and solutions Recent case studies and projects won, with a detailed description of solutions provided Providers’ key strengths and limitations Enterprise sourcing considerations Membership(s) Artificial Intelligence (AI) Outsourcing Excellence

This report underscores synthetic data’s vital role in addressing data challenges during AI initiatives. It highlights the significance of leveraging synthetic data to train and optimize AI models effectively. Additionally, it explores best practices for using synthetic data and its effectiveness in mitigating privacy concerns and biases commonly associated with using real-world data The report also explores key industry trends, use cases and adoption patterns related to synthetic data. It examines different buyers offering synthetic data solutions, providing insights into the diverse provider landscape to assist technology buyers in making informed decisions. Furthermore, the report will educate professionals developing AI/ML solutions on the benefits of synthetic data. It offers a comprehensive understanding of synthetic data’s working principles, highlights key nuances, and equips professionals with the knowledge necessary to leverage synthetic data effectively, thereby enhancing their AI/ML projects. Contents: In this report, we examine: An overview of synthetic data Synthetic data’s role in the AI development cycle Key applications and adoption of synthetic data across different industries The synthetic data supplier ecosystem Scope: Industry: cross-industry Geography: global Membership(s) Artificial Intelligence (AI) Outsourcing Excellence

The global spend on AI is soaring as enterprises across diverse sectors increasingly tap into its transformative potential. With the emergence of generative AI / Large Language Models (LLMs), enterprises worldwide are swiftly adopting AI to unlock new revenue streams, reduce operating costs, enhance user experiences, and gain significant industry differentiation. However, at the core of AI development lies the need for large training datasets, a challenge met by Data Annotation and Labeling (DAL) services. However, managing DAL capabilities in-house appears to be costly, time-intensive, and resource-draining for enterprises. As a result, businesses are outsourcing DAL solutions to external providers. In their quest, enterprises seek partners capable of accelerating their time-to-market with annotation projects delivered at scale and speed without compromising data quality. They prioritize providers that emphasize relationship-building, cost-effectiveness, agility, and a steadfast commitment to deliver tangible business impact and RoI throughout their transformation journey. Equipped with trained workers and robust annotation platforms, these providers efficiently guide enterprises through the DAL landscape. In this report, we assess 19 providers featured in the Data Annotation and Labeling (DAL) solutions for AI/ML PEAK Matrix® Assessment 2024. Each profile offers a comprehensive overview of the provider’s strengths and limitations, enabling enterprises to make informed decisions as they navigate the evolving DAL landscape. Scope All industries and geographies The assessment is based on Everest Group’s annual RFI process for the calendar year 2024, interactions with DAL solutions providers, client reference checks, and an ongoing analysis of the DAL services market Contents In this report, we examine: DAL solutions providers’ landscape DAL PEAK Matrix® characteristics Enterprise sourcing considerations Membership(s) Artificial Intelligence (AI) 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

Enterprises looking to adopt Artificial Intelligence (AI) initiatives are struggling to implement them at scale due to data-related challenges, the inability to acquire skilled talent, advanced IP, and lack of AI and cloud capabilities. As a result, they are turning to analytics and AI services specialists to serve their needs. In turn, these providers are improving their capabilities through investments in talent, products and platforms, partnership, industry expertise, and AI-based solutions tailored to serve specific client needs. In this compendium, we provide detailed and fact-based profiles of 22 analytics and AI services specialists featured on Everest Group’s Analytics and AI Services Specialists PEAK Matrix® 2022. Each profile presents a comprehensive overview of the provider’s service focus, key Intellectual Property (IP) / solutions, domain investments, and case studies. Scope Industry: data and analytics Geography: global The assessment is based on Everest Group’s annual RFI process for calendar year 2021 and 2022 H1 (January-June), interactions with leading analytics and AI services specialists, client reference checks, and an ongoing analysis of the analytics and AI services market Contents In this report, we examine: Specialist providers’ leadership, presence across geographies and industries, and global revenue estimates Partnership overview along with key IP and overall IP strategy Technology provider investments across talent, infrastructure (centers of excellence / labs), acquisitions, research, academic partnerships, and solutions Recent case studies Key strengths and limitations Membership(s) Data & Analytics Artificial Intelligence (AI) Sourcing and Vendor Management

Enterprises looking to adopt Artificial Intelligence (AI) initiatives are finding it difficult to implement them at scale due to data-related challenges, inability to acquire skilled talent, advanced IP, and lack of AI and cloud capabilities. Hence, they are turning to analytics and AI services specialists to serve their needs. In turn, these providers are improving their capabilities through investments in talent, products and platforms, partnership, industry expertise, and AI-based solutions designed to serve specific client needs. In this report, we present an assessment and detailed profiles of 22 analytics and AI services specialists featured on the analytics and AI services specialists PEAK Matrix®. Each provider profile presents a comprehensive picture of its service focus, key Intellectual Property (IP) / solutions, domain investments, and case studies. The assessment is based on Everest Group’s annual RFI process for calendar year 2021 and 2022 H1 (January-June), interactions with leading analytics and AI services specialists, client reference checks, and an ongoing analysis of the analytics and AI services market. Scope Industry: data and analytics Geography: global Contents This report provides a detailed analysis of 22 analytics and AI services specialists and includes Everest Group’s PEAK Matrix® evaluation of analytics and AI service providers and their categorization into Leaders, Major Contenders, and Aspirants An overview of enterprise analytics and AI priorities and key challenges in scaling AI Key analytics and AI services trends A detailed assessment of the strengths and limitations of the providers in terms of their market impact and vision & capability Membership(s) Data & Analytics Sourcing and Vendor Management