The Why, What And How Of Composable Data And Analytics

Analytical teams are constantly coming up with ways to make sense of the millions of bits of
information that flow into an organization, sometimes on an hourly basis. Before the Covid-19 pandemic, data analytics took a more rigid approach with a monolithic data architecture, which can be described as a “generalist, all-in-one” solution. That method, however, lacked the expertise to manage all elements of the data infrastructure effectively.

As a counter to that rigid system, a new, trending way today is the deployment of composable data analytics. This is a process by which organizations combine and consume analytics capabilities from various data sources across the enterprise for more effective, intelligent, and above all, faster decision-making.

The aim of composable data and analytics is to use various data, analytics, and artificial
intelligence (AI) solutions to link data insights with business actions faster. Through the
introduction of low-code and no-code capabilities, organizations can develop tailored analytics experiences with analytics capabilities that are modular rather than monolithic applications.

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