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