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IT infrastructure is key to successful adoption of AI

DATA is the building block that drives Artificial Intelligence (AI). Thus, the heart of AI's success would be a solid data storage strategy commensurate with the burgeoning data growth.

Yet, one in five IT decision-makers across Asia-Pacific are not currently ready to handle the volume and velocity of data that is coming their way. This finding is based on a study by Seagate, Data Pulse: Maximising the Potential of Artificial Intelligence, which surveyed 600 senior IT professionals in Australia, China, India, Singapore, South Korea and Taiwan.

While the study revealed that at least six in 10 organisations have implemented AI in one form or another, 95 per cent of respondents say that they require even more robust data storage solutions to enhance and support AI applications and workloads.

Respondents to our study cited the lack of a clear strategy facilitating AI adoption within the organisation as one of two biggest hurdles to successful implementation of AI. The other big challenge is having an IT infrastructure sufficiently robust for AI deployment, especially as businesses look to harness insights and information for real-time data processing.

To that end, having an IT infrastructure that can efficiently address the increased workload from AI deployment is critical. These can be seen typically in two common scenarios - one to pre-process data to convert it into the right format for machine learning algorithms, and another to process data in real time.

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Consider healthcare research as an example of the former scenario. Scientists at Singapore's Agency for Science, Technology and Research (A*STAR) are using AI technology to scan entire genomes of 212 tumours and determine the root of gastric cancer. A traditional approach to this analysis would have taken 30 years. With AI, the scientists were able to complete this task in just a few months.

Organisations such as A*STAR require mountains of raw data to pre-process, analyse and generate their groundbreaking insights. Behind the scenes, data centres have stored this tremendous volume of data for AI to sieve through; it also means that data storage, access and availability are all critical elements in the data centre architecture for these organisations to deploy AI effectively.

On the other hand, consider banks for example, where high volumes of customers are involved and real-time insights are valued. The bank's (and its customers') need for speedy access to data means that much of the data should ideally be stored near the bank (ie at the edge) to facilitate faster transactions. Machines and applications powering AI, as much as the users accessing and processing data at the bank, will require fast access to data to be effective.


To better work alongside humans and better understand our needs, AI needs data. The constant creation, storage and processing of data mean your database will grow in unprecedented ways.

Yet, our Data Pulse survey found almost half (46 per cent) of the respondents from Singapore believe that their organisations have yet to invest sufficient man-hours and budgets in AI development and implementation.

According to IDC, the amount of data subject to analysis globally will experience a 50x growth to 5.2ZB, and a 100x growth of AI-related data expanding to 1.4ZB by 2025. Remember, data is not merely created on our computers, but also generated by myriad devices in the real world - in the office, smart homes and smart-city infrastructure.

The Internet of Things implies that much of what AI needs - data and more data - is generated and collected from multiple sources, without human involvement, and increasing at unprecedented speeds. To generate insights and make decisions in real time via AI, this staggering amount of data will need to be integrated, managed and processed end-to-end.

The success of AI depends on many things. However, organisations need to ensure that their IT infrastructure - so often simply regarded as basic technology that "keeps the lights on" - needs to be AI-enabled to provide a backend (storage, access, compute, transfer) sophisticated enough to create optimised environments that ensure AI deployments are well-supported. That makes the difference between an AI that can serve up a solution with a "Here you go!" and one that constantly asks: "Can you please repeat the question?"

  • The writer is vice-president, Asia-Pacific sales, at Seagate Technology.

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