How Automation Makes Analytics Agile
How agile data modeling is driving the rise of adaptive analytics fabrics
How do you go from “lots of data” to “big data” that you can access and mine for insights?
In an ever-accelerating information age, the companies most likely to succeed are the ones that not only glean the most profitable insights from their data, but do it faster and more nimbly than their competitors.
How are they achieving this? Through a more holistic approach that is based on agile data modeling and leveraging an adaptive analytics fabric.
This paper outlines the requirements for next-gen agile data modeling, and will show you:
- The requirements for a successful data and analytics pipeline
- How autonomous data engineering delivers optimizations beyond what humans are capable of
- How an adaptive analytics fabric can reverse engineer queries and data models used to create legacy reports – without having to rebuild
- How to enable modeling collaboration, data security & governance, and much more
All of this can be achieved by adopting an adaptive analytics fabric with autonomous data engineering – you can empower business users across your organization to quickly and easily uncover previously unseen insights in your data, ensuring you remain agile and competitive in a world that will only grow more data-driven.