AI and Machine Learning (ML) technologies’ rising adoption has revolutionized how enterprises use data, elevating the technologies’ significance. Today, data is integrated into every aspect of operations to facilitate decision-making and drive innovation, increasing data access and consumption. However, the ever-evolving landscape of high-volume data and intricate interdependencies between platforms have highlighted challenges for data teams. These challenges hinder teams’ visibility into data movement, quality, reliability, and operational costs. To address these challenges, enterprises are adopting data observability. It offers end-to-end data stack visibility, enabling organizations to proactively identify and diagnose data-related issues and optimize the underlying data’s overall health. This compendium provides accurate, comprehensive, and fact-based snapshots of 20 data observability technology providers. Each profile offers a detailed overview of the provider’s operations, product capabilities, investments, integrations, and market success. Scope All industries and geographies This assessment is based on Everest Group’s annual RFI process for the calendar year 2023, interactions with leading data observability technology providers, client reference checks, and an ongoing analysis of the data observability market Contents In this report, we examine: The data observability technology providers’ landscape Enterprises’ sourcing considerations Providers’ leadership, case studies, product capabilities, and presence across geographies and industries Key investments and integrations Providers’ key strengths and limitations Membership(s) Data & Analytics Sourcing and Vendor Management

The rising adoption of AI and ML has revolutionized how enterprises use data, elevating its importance to unprecedented levels. Today, data is integrated into every facet of operations to facilitate decision-making and drive innovation, resulting in a substantial increase in data access and consumption. However, the ever-evolving landscape of high-volume data, coupled with intricate interdependencies between platforms, has amplified challenges for data teams. These challenges hinder their visibility into data movement, quality, reliability, and the cost of data operations. To address these challenges, enterprises are embracing modern data management practices such as data observability. Data observability offers end-to-end visibility of data estates, enabling organizations to proactively identify and diagnose data-related issues while optimizing the underlying data’s overall health. In this report, we assess 20 providers featured on the Data Observability Technology Provider PEAK Matrix® 2024. Each profile offers a comprehensive picture of the provider’s key strengths and limitations. Scope: All industries and geographies The assessment is based on Everest Group’s annual RFI process for the calendar year 2023, interactions with leading data observability technology providers, client reference checks, and an ongoing analysis of the data observability market Contents: In this report, we examine: Data observability technology provider landscape Data Observability Technology Provider PEAK Matrix® characteristics Enterprise sourcing considerations Membership(s) Data & Analytics Outsourcing Excellence

The rising adoption of AI and ML has transformed the way enterprises use data, elevating its importance to an unprecedented level. Today, enterprises integrate data into every aspect of their operations to facilitate decision-making and drive innovation, thereby significantly increasing data access and consumption. However, the constantly changing dynamics of high-volume data, coupled with the complex interdependencies between platforms, have heightened the challenges faced by data teams, blocking their visibility over data movement, quality and reliability, and the cost of data operations. To overcome these challenges, enterprises are embracing modern data management practices such as data observability. Data observability provides end-to-end visibility of data estates, enabling organizations to proactively identify and diagnose data-related issues while optimizing the underlying data’s overall health. In this report, we explore the need to implement data observability, provide an overview of the practice, and list key data management use cases covered within its scope. Moreover, the report assists enterprises and technology providers in comprehending the evolving supplier landscape and highlights the emerging trends poised to shape the future of data observability. Scope All industries and geographies Contents In this report, we examine: Technical and business challenges that enterprises face due to modern data architectures Key characteristics of data observability and its current scope of functionalities Key enterprise considerations influencing the adoption of data observability How data observability drives business benefits for enterprises Technology provider landscape and the growing investment capital raised by providers Membership(s) Data & Analytics Sourcing and Vendor Management

In recent years, technology has evolved exponentially to offer hitherto unimaginable opportunities. The accelerating pace of change makes innovation far more accessible and scalable. At the heart of this change lies data and information. Organizations have gained tremendous confidence in data’s ability to transform their processes and interactions with different stakeholders, including the society and government. Based on this confidence, and, as suggested by a recent Everest Group survey, 72% of enterprises have forecasted double-digit growth in spending on their data and analytics initiatives. At the same time, technology does not provide answers to how businesses, governments, and the society at large must adapt to succeed in a data-driven future. Such a future will be governed by how technology is built and consumed, the business models that subsequently emerge, organizational changes as the relationship between business and technology gets blurred, and how societies, governments, and businesses redesign the social contract between themselves. This viewpoint helps the reader understand emerging technologies, evolving business models, organizational changes, and transforming social contracts, driven by the pursuit of a data-driven future. Membership(s) Data & Analytics