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The AI-Era is Rising in ASEAN
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April 8, 2022 Blog

 

Authored by: CK Tan, Senior Director, Qlik

The demand for data and artificial intelligence (AI) continues to accelerate. An executive survey on big data and AI found that 99 per cent of firms have now made investments in these critical areas. A survey by EDBI and Kearney of the ASEAN countries including Singapore, Malaysia, Indonesia, Thailand, Vietnam, and the Philippines found that if applied and executed well, AI could add $1 trillion to the region’s GDP by 2030.

However, despite the rush to embed data, analytics and AI into organisations’ day-to-day operations, enterprises must realise that a radically different approach to data architecture is needed if they are to successfully put intelligence at the heart of their response to every business moment.

Currently, just a fraction of data is properly used in analysis. One study estimated that only 10% of business-relevant data gets used. This is because many of the processes for how data is shared, analysed and consumed by users are rigid and inflexible, and don’t reflect how information today is generated. It creates blockages when the amount of data being created today is as vast both in volume and from the number of diverse sources it is generated from. It is no wonder that traditional approaches and processes, designed to accommodate standardised sets from specific sources, are overwhelmed.

Organisations need a new approach to data analysis and decision-making that is informed by continuous, hyper-contextual data. But this Active Intelligence can only be achieved when underpinned by an analytics data pipeline that is able to find and free data, before it is and analysed and actioned.

Find it and free it

First, raw data (whether internal, external or derivate), needs to be taken from wherever it is housed (such as cloud or on-premises storage and apps) and delivered to wherever it needs to be. This cannot be a one-and-done transfer, but a continuous flow of information that reflects real-time changes.

By doing this, enterprises can accelerate the discovery and availability of real-time, analytics-ready data to the cloud of their choice by automating data streaming, refinement, cataloguing, and publishing.

Once the data is freed, it can be profiled and catalogued, and in doing so becomes analytics ready. This means users can easily find data for consumption, safe in the knowledge that they can trust both the source of and the data itself. Take one of Indonesia’s leading banks, PT Bank Tabungan Pensiunan Nasional Tbk (BTPN), for example. To leverage the vast amounts of data it was sitting idly on, BTPN worked with Qlik to enable real-time processing of its data. This allowed them to quickly analyse data to form actionable insights and drive business growth, like providing faster and more personalised customer service.

Understand it

At the next stage in the data journey, data needs to shift from becoming analytics ready to being business ready. This is critical if users are to understand the data and apply timely business logic and context in a governed manner for insights generation.

During the disruption of the last year, for example, while most organisations had analytics ready data, they were not always able to get answers that empowered them to make opportune decisions. Data was often not reflective of the business moment as conditions and dynamics changed rapidly around organisations.

To achieve Active Intelligence, organisations need to be able to give non-technical users access to data in a way that they will be able to read, understand and take action from the information available to them in the business moment. Moreover, this provision of data needs to be integrated into existing workflows and processes to make the process seamless for the user and encourage its use to inform decision-making. That applies whether it is a CEO or a front-line healthcare worker.

Action it

Finally, business-ready data needs to drive informed action.

The system must embed analytics into automated workflows, deliver sophisticated, context-aware alerts in real-time, and, as change happens, trigger informed and automated business actions. This stage enables intelligent systems to surface analytics-based signals and take governed actions much earlier, even before a dashboard is built.

At the height of the pandemic in March 2020, the Tasmanian Health Service (THS) built a state-wide COVID-19 tracking focus board in less than four days. The dashboard provided access to each hospital’s command centre and the state command centre, enabling them to respond quickly to urgent situations like outbreaks in hospitals.

Tackling the insurmountable challenge of traditional data approaches   

Most organisations are facing an insurmountable challenge in trying to drive informed actions with the traditional data pipelines and architectures. We are operating in the AI era, where the very concept of data is fluid and evolving. There needs to be a move towards a process of hybrid-automated (with human in the loop) informed actions that can be taken by a system, powered by data.

The journey described above is what data goes through in an intelligent analytics data pipeline, an agile way of moving data from raw to actionable. It is a framework with a set of architecture and tools that go beyond traditional data movement, automation and transformation, and it is the only way enterprises are going to realize the potential value of their data today.

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