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AI and machine learning
challenges in the availability and preparing of data.
A business cannot become data-driven, if it doesn’t
understand the information it has and the concept of
‘garbage in, garbage out’ is especially true when it comes to
the data used for AI.
With many organisations still on the starting blocks, or
having not yet entirely finished their journey to become
data driven, there appears to be a misplaced assumption
that they can quickly and easily leap from being in the
process of preparing their data to implementing AI and ML,
which realistically, won’t work. To successfully step into the
world of AI, businesses need to firstly ensure the data they
are using is good enough.
AI in the data centre
Over the coming years, we are going to see a tremendous
investment in large scale and High-Performance Computing
(HPC) being installed within organisations to support data
analytics and AI. At the same time, there will be an onus on
data centre providers to be able to provide these systems
without necessarily understanding the infrastructure that’s
required to deliver them or the software or business output
needed to get value from them. We saw this in the realm of
big data, when everyone tried to swing together some kind
of big data solution and it was very easy to just say we’ll
use Hadoop to build this giant system. If we’re not careful,
the same could happen with AI. There have been many
conversations about the fact that if we were to peel back the
layers of many AI solutions, we’ll find that there are still a
lot of people investing a lot of hard work into them, so when
it comes to automating processes, we aren’t quite in that
space yet. AI solutions are currently very resource heavy.
There’s no denying that the majority of data centres are
now being asked how they provide AI solutions and how
they can assist organisations on their AI journey. Whilst
organisations might assume that data centres will have
everything to do with AI tied up. Is this really the case? Yes,
there is a realisation of the benefits of AI, but actually how
it is best implemented, and by who, to get the right results,
hasn’t been fully decided.
Solutions to how to improve the performance of largescale
application systems are being created, whether that’s
by getting better processes, better hardware or whether it’s
reducing the cost to run them through improved cooling or
heat exchange systems. But data centre providers have to be
able to combine these infrastructure elements with a deeper
understanding of business processes. This is something
very few providers, as well as Managed Service Providers
(MSPs) and Cloud Service Providers (CSPs) are currently
doing. It’s great to have the kit and use submerged cooling
systems and advanced power mechanisms but what does
that give the customer? How can providers help customers
understand what more can be done with their data systems?
How do providers differentiate themselves and how can they
say they harness these new technologies to do something
different? It’s easy to go down the route of promoting that
‘we can save you X, Y, Z’ but it means more to be able to
say ‘what we can achieve with AI is..X, Y, Z‘. Data centre
providers need to move away from trying to win customers
over based solely on monetary terms.
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