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A
ccording to IDC, spending on data-
intensive AI systems in the Middle
East and Africa (MEA) region will
grow at a CAGR of 32% between 2016 and
2021, reaching US$114.22 million in 2021.
Projects range from automated customer
service agents, shopping and product
recommendations to health and safety
use cases such as automated cyberthreat
detection and AI-powered medical research,
diagnosis and treatment.
Some have referred to this opportunity as
the Fourth Industrial Revolution. That’s a
massive understatement. The last industrial
revolution was driven by the assembly line –
a feat of strategic engineering that helped
build a car faster. Today we’re talking about
feats of engineering that allow cars to drive
themselves. It’s less apples to oranges and
more apples to atoms. A new generation of
tools, fuelled by an ability to ingest, store and
deliver unprecedented amounts of data, are
driving a tidal wave of innovation, previously
relegated to the realms of science fiction.
Data’s role in the future of business
cannot be overstated. According to a
survey conducted by MIT Technology
Review, commissioned by Pure Storage,
an overwhelming 87% of leaders across
MEA say data is the foundation for making
business decisions and 80% believe that
it is key to delivering results for customers.
But acknowledging the importance of data
and putting data to work are two separate
things. To put the latter in perspective, a
recent study conducted by Baidu showed
its dataset needed to increase by a factor
of 10 million in order to lower its language
model’s error rate from 4.5 to 3.4%. That’s
10,000,000x more data for 1% of progress.
All this research points to one thing –
to innovate and survive in a business
environment that is increasingly data-
driven, organisations must design their IT
infrastructure with data in mind and have
complete, real-time access to that data.
Unfortunately, mainstream storage
solutions were designed for the world of
disk and have historically helped create
silos of data. There are four classes of silos
in the world of modern analytics – data
warehouse, data lake, streaming analytics
and AI clusters. A data warehouse requires
massive throughput. Data lakes deliver
www.intelligentcio.com
James Petter, VP, EMEA at Pure Storage
scale-out architecture for storage. Streaming
analytics go beyond batched jobs in a
data lake, requiring storage to deliver
multi-dimensional performance regardless
of data size (small or large) or I/O type
(random or sequential). Finally, AI clusters,
powered by tens of thousands of GPU cores,
require storage to also be massively parallel,
servicing thousands of clients and billions of
objects without data bottlenecks.
As a consequence, too much data today
remains stuck in a complex sprawl of silos.
Each is useful for its original task, but in a
data-first world, silos are counter-productive.
Silos mean organisational data can’t do
work for the business, unless it is being
actively managed.
Modern intelligence requires a data hub – an
architecture designed not only to store data,
but to unify, share and deliver data. Unifying
and sharing data means that the same data
can be accessed by multiple applications
at the same time with full data integrity.
Delivering data means each application has
the full performance of data access that it
requires, at the speed of today’s business.
Data hub is a data-centric architecture
for storage that powers data analytics
and AI. Its architecture is built on four
foundational elements:
• High-throughput for both file and
object storage. Backup and data
INTELLIGENTCIO
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