In today’s app-driven world, organisations are drowning in a high volume and variety of data. The challenge they face is extracting critical insights from these applications’ operational data using real-time analytics. This is supported by our research that reveals that only 17 percent of enterprises polled have the ability to perform real-time analytics on large volumes of data.
When coupled with AI, real-time analytics could open the door to hyper-personalised applications that adapt to a user's evolving situation to provide them with relevant and timely customer experiences in real-time. This provides the kind of customised, dynamic and responsive user experiences that customers are increasingly demanding.
How Singapore is showing the way by investing in enhancing AI and analytic capabilities
Finding the right tools and technologies is the first step to carving out a competitive advantage in an increasingly AI-centric universe.
Let’s look at some examples: Across Asia Pacific, a country doing this is Singapore, which is set to invest over US$743 million over the next five years to enhance its AI capabilities. The allocation, which includes the implementation of a National AI Strategy 2.0, demonstrates the Singapore government's commitment to fostering a responsible AI ecosystem and the ambition to be a trusted hub for AI collaboration.
As a small nation with limited resources, Singapore relies on technology to remain competitive and is rapidly integrating AI across key sectors like government, healthcare, transport, and finance.
In this context, real-time data analytics has become a pivotal component of Singapore's digital strategy. The government's open data initiative, Data.gov.sg, serves as a one-stop portal where over 90 local government agencies upload their data in real-time for public access, covering areas such as environmental metrics and public transportation statistics. This approach enhances transparency and enables businesses and researchers to leverage up-to-date information for real-time decision-making and innovation.
To dive deeper, the finance sector in Singapore also heavily relies on data analytics to manage risks, detect fraud, and enhance customer experiences. By monitoring transactional data in real-time, banks can flag suspicious activities, protecting customers from fraud.
Similarly, in public transportation, real-time analytics are utilized to improve efficiency and commuter satisfaction. For instance, Singapore’s Land Transport Authority's SG Traffic Watch integrates real-time data with historical analysis to manage road traffic effectively, leading to a 92 per cent reduction in bus services with crowding issues and a 3 to 7 minute decrease in average waiting times on popular routes.
By embedding real-time analytics into its AI initiatives, Singapore not only strengthens its position as a technological leader, but also ensures that its digital transformation is data-driven, efficient, and responsive to the needs of its citizens and industries.
Why real-time matters: Learning from Apple
The app economy is big business. Apple’s App Store ecosystem alone generated a sizeable $1.1 trillion in total billings and sales for developers in 2022. And as users demand more relevant, personalised, and immediate experiences, the attention on real-time analytics was a critical stepping stone to success.
This is especially true for a new cohort of dynamic apps that are capable of adjusting behaviour and features in real-time based on factors such as user preferences, environmental conditions, data inputs, and changing circumstances.
A retail app that’s equipped with analytics and AI capabilities might enable businesses and advertisers to offer the right products and services to the right target audience at the right time while keeping track of inventory, delivery details, and more.
Similarly, a booking app might be regularly updated based on real-time travel information, events, and user history to suggest personalized journeys and deals.
This hyper-personalisation and responsiveness are supercharged by the power of AI. Integrating generative AI with real-time analytics offers numerous benefits, including enhanced predictive capabilities, personalised user experiences, improved operational efficiency, and the ability to respond to events in real-time. This significantly enhances use cases ranging from fraud and anomaly detection to customer service and retail checkout experiences. By leveraging these technologies, businesses can gain deeper insights, respond faster to changes, and deliver better products and services to their customers.
Four mistakes organisations are making with real-time analytics
Yet despite the obvious benefits, adoption remains slow outside of established legacy enterprises, with many businesses yet to fully exploit the benefits of real-time data. The following four common mistakes may be compounding these challenges:
- Too much focus on speed over accuracy and data quality
As their moniker suggests, timeliness is critical to these applications. But speed shouldn’t come at any cost. The old adage, “garbage in, garbage out,” applies here. If a service draws on poor-quality data, it will not deliver the intended outcomes.
Outdated or incomplete datasets will only lead to inaccurate insights and erode customer trust in the application. Organisations should instead prioritise data validation checks and cleaning, as well as regular audits, to maintain data integrity and accurate results.
- Rahul Pradhan, VP Product and Strategy, AI and Data, Couchbase
- Ignoring the importance of context
Real-time data requires broader context and correlation to help derive accurate insights. That’s why organisations must dig deeper to uncover the true relationship between variables. For example, a sudden spike in sales of an item may be due to increased consumer demand, macroeconomic conditions such as a shortage of complementary goods, climate-related indicators, or perhaps promotional campaigns. Correlation does not imply causation.
- Choosing the wrong tools
Not all analytics tools are created equal. It’s critical that organizations choose technologies tailor-made for real-time data processing and visualisation, including a database that offers analytic, AI, AI agent development services, mobile and edge, operational, and vector search support on a unified platform. Failure to do so could lead to bottlenecks, latency, and accuracy issues.
- Failing to clearly define objectives
Analytics projects will rarely reap the desired rewards without specific measurable goals. Organizations must therefore, define clear objectives, such as improving customer retention by a certain amount within a set timeframe. This will help guide data collection and analysis efforts.
Without clear goals, it’s difficult to identify actionable insights or measure success.
Time for real-time analytics
There are potentially serious business consequences to getting real-time analytics wrong in this context. Four of out of 10 enterprises we polled said they could go out of business within three years if their apps no longer meet user expectations.
Close to half (46 percent) believe they’ll lose out to the competition if this happens. While these capabilities are already being used by a few mature businesses, the vast majority of organisations struggle to get hold of the right tools and know-how to leap barriers like siloed data systems.
Fortunately, modern developer data platforms can address these challenges if they are able to integrate both operational and analytical workloads in a unified environment. This avoids having to move data from databases to data warehouses, reduces the need for costly Extract-Transform-Load (ETL) processes in OLTP and OLAP systems, and minimizes latency.
Real-time analytics offer organizations a vital edge in meeting today’s dynamic customer needs. By overcoming common pitfalls and leveraging modern developer data platforms, businesses in Singapore and the region can make timely, data-informed decisions that improve customer satisfaction in an increasingly competitive landscape.
Rahul Pradhan is VP Product and Strategy, AI and Data at Couchbase
