Rethinking data management for faster time to value

Enterprises should abandon traditional ETL approaches and strategies and adopt future state architectures that distribute the data transformation burden.

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As enterprises move ahead in their digital transformation journey, the massive trail of data from digital transactions is growing steadily, but for many organizations extracting intelligence from data remains a pipe dream.

According to analyst firm IDC's Worldwide Global DataSphere Forecast, 2021–2025, business and consumer data has been amassing at a compound annual growth rate (CAGR) of about 23% since last year, with a 28% CAGR attributed to enterprises, and is expected to reach 180 zettabytes by 2025.  Data created in the cloud is also growing at 36% annually, while data collected at the edge through various IoT and sensing devices is growing at 33% annually and will make up 22% of the total global datasphere by 2025.

For enterprises, the task of making data compute‐ready becomes more complex as the amount of data grows, and companies are spending little time and effort developing effective data management processes and platforms to make that data easily actionable. For example, many companies collect massive amounts of digital transaction data pertaining to their customers, orders, product use, install base, service tickets, crash logs, and market intelligence, but have no good way of creating a 360 degree view of each customer or their business—despite having more technology choices available to them than ever for extracting intelligence from data.

Many enterprises have reached a state where it is clear to them that the amount of data they possess neither provides a constant competitive advantage nor allows them to easily unlock value from it.  At the same time, this expanded data ownership raises confidentiality concerns and enforcement costs and also adds to complexity of the environments.

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