AI and machine learning: data centres need to differentiate themselves to survive

Peter Ruffley, Chairman at Zizo, discusses how the promise of AI...

The promise of AI

At present, the IT industry is doing itself no favours by promising the earth with emerging technologies, without having the ability to fully deliver them, see Hadoop’s story with big data as an example – look where that is now. There is also a growing need to dispel some of the myths surrounding the capabilities of AI and data led applications, which often sit within the c-suite, that investment will give them the equivalent of the ship’s computer from Star Trek, or the answer to the question ‘how can I grow the business?’ As part of any AI strategy, it’s imperative that businesses, from the board down, have a true understanding of the use cases of AI and where the value lies.

If there is a clear business need and an outcome in mind then AI can be the right tool.  But it won’t do everything for you – the bulk of the work still has to be done somewhere, either in the machine learning or data preparation phase.

AI ready vs. AI reality

With IoT, many organisations are chasing the mythical concept of ‘let’s have every device under management’. But why? What’s the real benefit of doing that? All they are doing is creating an overwhelming amount of low value data. They are expecting data warehouses to store a massive amount of data. If a business keeps data from a device that shows it pinged every 30 seconds rather than a minute, then that’s just keeping data for the sake of it. There’s no strategy there. The ‘everyone store everything’ mentality needs to change.

One of the main barriers to implementing AI is the 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 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’s been a lot of conversations about the fact that if we were to peel back the layers of many AI solutions, we’ll find that there is 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 large-scale 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.

Education and collaboration

When it comes to AI, there has to be an understanding of what the whole strategic vision is and looking at where value can be delivered and how a return on investment (ROI) is achieved. What needs to happen is for data centre providers to work towards educating customers on what can be done to get quick wins.

Additionally, sustainability is riding high on the business agenda and this is something providers need to take into consideration. How can the infrastructure needed for emerging technologies work better? Perhaps it’s with sharing data between the industry and working together to analyse it. In these cases, maybe the whole is greater than the sum of its parts. The hard bit is going to be convincing people to relinquish control of their data. Can the industry move the conversation on from being purely technical and around how much power and kilowatts are being used to how is this helping our social corporate responsibility/our green credentials?

There are some fascinating innovations already happening, where lessons can be learnt. In Scandinavia for example, there are those who are building carbon neutral data centres, which are completely air cooled, with the use of sustainable power cooling through solar. The cooling also comes through the building by basically opening the windows. There are also water cool data centres out there under the ocean.

Conclusion

We saw a lot of organisations and data centres jump in head first with the explosion of big data and not come out with any tangible results – we could be on the road to seeing history repeat itself. If we’re not careful, AI could just become another IT bubble.

There is still time to turn things around. As we move into a world of ever-increasing data volumes, we are constantly searching for the value hidden within low value data that is being produced by IoT, smartphone apps and at the edge. As the global costs of energy rise, and the numbers of HPC clusters powering AI to drive our next generation technologies increase, new technologies have to be found that lower the cost of running the data centre, beyond standard air cooling.

It’s great to see people thinking outside of the box on this with, with submerged HPC systems and full, naturally aerated data centres, but more will have to be done (and fast) to meet up with global data growth. The appetite for AI is undoubtedly there but for it to be able to be deployed at scale and for enterprises to see real value, ROI and new business opportunities from it, data centres need to move the conversation on, work together and individually utilise AI in the best way possible or risk losing out to the competition.

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