Understanding The Right Fit for Your Organization: Data Fabric or Data Mesh?

whitePaper | June 27, 2023

Data mesh is an innovative approach to data management that offers a solution to the most common challenges organizations face when dealing with large-scale data management. Data mesh concept consists of four key principles for large-scale data management in a multi-tiered, multi-domain organizational structure: domainoriented ownership, data as product, federated computational governance and a self-service data platform. These all promote a more agile, efficient and scalable data architecture. However, one of the central challenges to implementing data mesh is designing decentralized governance mechanisms that align with its core principles.

whitePaper | September 8, 2022

Enterprises today have to contend with exponentially increasing volumes of batch and streaming data, comprising a variety of structured, unstructured, and semi structured data types, and originating from an expanding number of disparate sources located on-premises, in the cloud and at the edge.

whitePaper | August 20, 2021

While finding and creating insights can often feel like luck, there are proven methodologies you can use to ensure you’re seeing regular insight from your on-hand data. Access this short e-book to learn how you can start seeing regular, valuable insight success from you big data and analytics investments.

whitePaper | March 12, 2020

Operational Acceptance Testing (OAT) is the penultimate phase of Acceptance Testing in a Software Testing Life cycle and is the last defence line between a software development project and deployment of software on production. FFor decedes, Operational Acceptance has been undermined and misunderstood. Where User Acceptance has been written about and hailed as a final phase in testing before production. User Acceptance is but one side of the coin, Operational Acceptance is the other.

whitePaper | June 16, 2022

It seems like the business world has gone crazy for machine learning (ML) and artificial intelligence (AI), viewing the technologies as the essential keys to the very future of the enterprise. In fact, a recent survey from ESIThoughtLab found that two-thirds of business leaders see AI as critically important for their future. And yet, the respondents report that only 25% of AI projects are in widespread deployment and 40% of all projects are generating negative or no returns.

whitePaper | November 30, 2022

Modern cloud analytics can help financial services organisations to gain greater insight into their ever-increasing volumes of data, enabling them to improve customer experience, boost productivity and generate new revenue streams.