Over 90% of enterprises have already embarked on digital transformation ‒ the
transition to more data-centric business models. While IT organizations will maintain a
number of legacy workloads when they make this transition, they will also be adding many
more next-generation applications that are being developed and deployed specifically to meet
the requirements of the new digital era. Big data analytics applications leveraging
artificial intelligence (AI) will drive better business insights, fueled by the massive
amounts of data that enterprises will be collecting from their products and services,
employees, internal operations, and partners going forward. The massive amounts of data
required, delivered with an increasingly real-time orientation, demand performance,
availability, and scalability that legacy information technology (IT) infrastructure will be
hard pressed to meet.
AI is made up of a number of different workloads, each of which generates a different I/O
profile and has different storage requirements. To make the most effective use of AI-driven
big data analytics, enterprises will need to create an "end to end" AI strategy that is well
integrated across three different deployment models ‒ from edge to core datacenter to
cloud. Because of the many new requirements of this hybrid, multicloud strategy, almost 70%
of IT organizations will be modernizing their IT infrastructure over the next two years. IDC
has released the "artificial intelligence plane" model to help customers better understand
how to create the right ecosystem to maximize the contribution AI-driven workloads deliver.
The underlying storage infrastructure is a key component in that model, and it is already
clear from end-user experiences over the past several years that legacy architectures will
generally not provide the right foundation for long-term AI success.
