Data mesh is an emerging architecture that furthers data fabric objectives. The data fabric architecture addresses the rising complexity of data management by intelligently integrating and connecting an organization’s data and making reusable data assets available for consumption. The objectives of these logical architectures are to scale the delivery of data to satisfy diverse use cases and, most importantly, provide the agility to respond quickly to changing business needs. Previous approaches to overcome these challenges include semantic layers, data virtualization, and data as a service (a data management strategy aiming to leverage data as a business asset for greater business agility). Although these challenges are not entirely new to the data landscape, they have assumed greater importance as organizations strive to accelerate digital transformation. These challenges hinder organizations from becoming truly data-driven and responding to business demands fast. Lack of trust in data resulting from multiple copies of varying instances of the same data.Lack of collaboration – Data developers siloed from the domains where the data originates or is used for decision making.Lack of agility – Inability to meet data demands in a timely manner.Data proliferation and complexity – Increasing data domains (e.g., Product, Supply Chain, Marketing), data consumers, and data sources.Organizations relying on legacy architectures are struggling to scale data and analytics given the following challenges: Both data fabric and data mesh architectures aim to abstract data management complexity and deliver data to the business with agility and scale. Monolithic, legacy architectures and centralized data platforms thwart business’ agility, making it difficult to quickly adjust to the constantly changing data landscape that demands new views, new aggregations, and new projections of the data (aka data products). CEO Perspective: Introducing a New CloudĪpplication Integration & Hyperautomation.
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