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Create a strong data foundation for AI

Chapter 01
4 min read

Unlock your data for AI

Data is the lifeblood of every modern organization, and it’s being created, stored and analyzed at an unprecedented rate across industries. By 2025, global data creation is estimated to reach a staggering 180 zettabytes.1 This explosion of data presents enormous opportunities for businesses that are prepared to take advantage of it — from generating new offerings and revenue streams to protecting against regulatory risk. Those organizations that fail to address their data management will find this influx of data to be more a challenge than a chance for innovation.

But it isn’t enough to simply collect and store large volumes of data; businesses need to seamlessly and securely access, govern and use this data to drive digital transformation and successful AI adoption.


According to Forrester, 80% of firms expect the number of AI use cases to increase within the next 2 years.2


90% of companies have difficulty scaling AI across their enterprises.3

To create a robust data foundation for AI, businesses need to solve data management complexity in 3 key areas:

Lock and files indicating data access
Given the growing volume, variety and velocity of data, organizations need quick access to data spread across complex hybrid cloud environments that include multiple clouds, data stores, locations and vendors, as well as disparate data types.
Shield representing data governance
They must contextualize and classify this data to ensure they’re getting relevant, high-quality information to the right people at the right time, with self-service access to reduce the time to value for AI initiatives.
Checkmark indicating data privacy
Privacy and compliance
They also need to identify and protect personal and confidential information while ensuring regulatory compliance so that sensitive data can be safely used for AI and analytics.

Addressing common challenges in your data foundation helps set the stage for more accurate AI outcomes and successful AI implementations.

The DataOps methodology and the modern architectural pattern of a data fabric can help businesses address data access, governance and privacy throughout the AI lifecycle. Together, these approaches enable self-service data consumption and automation of complex and tedious data engineering tasks to help you unlock the full value of your data estate and establish a strong data foundation for AI success.