The age of digital transformation is over
The last great shift was about "going digital”. The next great shift is about “getting smart”.
AI, machine learning, and smart automation will drive 70% of GDP growth over the next decade. In this new "Machine Economy", data will continue to increase in both volume and value, but many data teams are already struggling to keep up.
Traditionally, the data preparation process has relied on a highly-complex stack of tools, a growing list of data sources and systems, and months spent hand-coding each piece together to form fragile data “pipelines”.
Then came data management “platforms” that promised to reduce complexity by combining everything into a single, unified, end-to-end solution. In reality, these platforms impose strict controls and lock you into a proprietary ecosystem that won’t allow you to truly own, store, or move your own data.
It’s clear that the old ways of doing data management simply cannot meet the needs of modern organizations in the Machine Economy.
Data teams are in desperate need of a faster, smarter, more flexible way to prepare their data for analysis, AI, and machine learning.