In today’s app-driven world, organizations are drowning in growing amounts and types of data. The challenge they face is extracting critical insight from these applications’ operational data using real-time analytics. When coupled with AI, real-time analytics could open the door to hyper-personalized and immediate customer experiences that adapt to a user’s changing circumstances.
These provide the kind of customized, dynamic, and responsive user experiences that customers are increasingly demanding. So why do just 17% of enterprises today have the ability to perform real-time analytics on large volumes of data?
The Philippines invests in enhancing AI and analytic capabilities
The artificial intelligence (AI) market in the Philippines is experiencing significant growth, with projections indicating a market size of $772.1 million in 2024 and an annual growth rate of nearly 29%, reaching approximately $3.5 billion by 2030. This rapid expansion is accompanied by an increasing adoption of real-time analytics across various sectors, as businesses recognize the value of immediate data insights for decision-making.
The Analytics Association of the Philippines estimates that by 2028, the country will require 500,000 analytics professionals, underscoring the growing demand for data analytics expertise. This surge in demand highlights the critical role of real-time analytics in driving business performance and competitiveness.
Recognizing the need for more data analytics professionals, the Asian Institute of Management (AIM) launched its Master in Data Analytics (MDA) programming in 2024, helping to address the skills gap of data analytics professionals.
Furthermore, the Philippines is positioning itself as an emerging hub for business analytics in Southeast Asia, with continuous growth expected over the next 5 to 10 years. This trend reflects the transformative potential of AI and real-time analytics in enhancing operational efficiency and economic development across the nation.
Why real-time matters
The app economy is big business. Apple’s App Store ecosystem alone generated a staggering $1.1 trillion in total billings and sales for developers in 2022. As users demand more relevant, personalized, and immediate experiences, attention has focused on real-time analytics as a critical stepping stone to success.
This is especially true for a new cohort of dynamic apps that are capable of adjusting behavior 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 with adaptive functionality might be regularly updated based on real-time travel information, events, and user history to suggest personalized journeys and deals.
In the Philippines, banks and insurance companies are focusing on hyper-personalized, data-driven services by consolidating customer data and automating workflows to keep pace with rapidly evolving customer expectations.
This hyper personalization and responsiveness are supercharged by the power of AI. Integrating generative AI with real-time analytics offers numerous benefits, including enhanced predictive capabilities, personalized 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 organizations 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:
1.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. Organizations should instead prioritize data validation checks and cleaning, as well as regular audits, to maintain data integrity and accurate results.
2. Ignoring the importance of context
Real-time data requires broader context and correlation to help derive accurate insights. That’s why organizations 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.
3. 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 visualization, 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.
4. 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. Some 41% of enterprises claim they could go out of business within three years if their apps no longer meet user expectations. An even greater share (46%) believe they’ll lose out to the competition if this happens.
Yet while these capabilities are already being used by a few mature businesses, the vast majority of organizations 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 solutions, businesses in the Philippines can make timely, data-informed decisions that improve customer satisfaction in an increasingly competitive landscape.
The author is the VP for Product and Strategy, AI, and Data at Couchbase
