In recent years, we have seen how the Covid-19 pandemic has accelerated an interesting trend, especially among Asian consumers in terms of how quickly and easily we embrace a virtual lifestyle for work, school or entertainment.
In fact, Asian consumers will account for half of global consumption growth in the next decade — a US$10 trillion ($13 trillion) opportunity, as reported by management consulting firm McKinsey. As they become more digital and embrace a mobile-first lifestyle, more and more “super apps” are emerging, which offer a gamut of services on a unified platform.
The merger of GoJek and Tokopedia in Indonesia is a prime example of this ongoing battle. These two offer three main services — e-commerce, on-demand services and financial services — right at the fingertips of consumers. Interestingly, this development pushes other businesses to also go digital and produce similar digital value propositions to remain relevant and in tune with the Asian consumer.
Disruptions are changing the landscape in more ways than we have ever imagined. Asian businesses are finding ways to detect any emerging signals of change, to be adaptive or agile to pivot, or even stay ahead of the curve in some cases.
Across industries, data has become the greatest resource to support their digital-centric imperatives. Data has become much easier to collect, store and mine, and to support this, we are seeing another new trend: Businesses are turning to the cloud as the foundation for their business infrastructure.
Cloud computing: The promise and the challenge
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The numbers support this assertion. Research firm International Data Corporation (IDC) said that public cloud spending in Asia Pacific (excluding Japan) is expected to reach US$48.4 billion in 2021, while also becoming the leading mobile-first region globally, accounting for 64% of global app downloads.
In today’s mobile-first society, consumers hold high expectations for services, and brands are building innovative business models to quickly adapt and match their audiences’ behaviours. App-based companies such as Grab and GoJek operate with a data-driven, cloud-based approach. They gather and analyse data to track patterns and create real-time insights, scaling cloud data storage and management to best suit their needs.
These businesses are seeing the benefits when data is free to move across public and private clouds — to be utilised by the right staff at the right time. Cloud reliability, scalability, availability, and reduced capital expenditure (capex) spending empower enterprises to unlock new revenue streams and business opportunities.
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However, the challenge is not simply a shift to the cloud. Just as there are enterprises that have successfully taken advantage of their cloud infrastructure, IDC also noted that more than 85% of organisations in Asia Pacific (excluding Japan) are struggling to cross the cloud maturity chasm and gain agility in cloud adoption. There are those who face infrastructure complexities and costs, struggling to enjoy the benefits of their cloud investments such as simplicity, agility and meeting security or compliance requirements.
Can AI be the solution?
As with most cloud operations or migrations, bottleneck issues can happen when the data is required to traverse from the edge to the core, to the cloud, and back again, especially when they are involved in multiple or hybrid multi-cloud environments.
Data tiering is a process many organisations apply to data management — flagging data as either hot, cool, cold or frozen (like how actively they are needed and utilised by users, for example) and shifting from one storage tier to another as its state dynamically changes. With data sets and storage environments growing exponentially now, automated workflows and processes are a necessity.
Increasingly, some organisations have also started to apply AI to assist in their data operations and cloud operations. The combination of AI and cloud computing allows for an extensive network that learns and improves continuously, while holding massive volumes of data right across the cloud, regardless of where they are stored.
Machine learning algorithms train the AI to automate complex and repetitive tasks to boost productivity, perform data analysis with little human engagement, and manage and monitor core workflows. The AI performs the tedious, mundane tasks of data management, while the human IT teams focus more on strategic operations.
We have seen organisations that have been able to scale their operations using AI together with other cloud data management solutions, leading them to set new standards and drive innovation for themselves. For example, NISI is a Hong Kong-based start-up specialising in the development of surgical and diagnostic innovations such as non-invasive robotic technologies.
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They amass enormous amounts of unstructured data such as medical images and videos from hospitals and clinics. They then utilise deep learning algorithms to improve diagnostics, thereby helping the clinicians on their decision making. Having an increased data capacity meant that NISI not only has ample space to store its data, but also the capacity to grow its datasets and help its machine learning algorithms achieve even faster training times.
As the digital economy continues to grow across Asia, we will see more innovative, AI-integrated ways to manage and wield data. With the right automated management and migration tools, the hybrid multi-cloud approach clearly offers the utmost flexibility in managing data — wherever it is, whenever you need it, at the best cost possible. E
Sanjay Rohatgi is the senior vice president and general manager for Asia Pacific and Japan at NetApp
Photo: NetApp
