By Srinivasulu Grandhi
In the bustling Indian business landscape, where competition is fierce and plentiful, the quest for a competitive edge is more than just a necessity—it’s a survival strategy. Navigating this dynamic and rapidly evolving market, businesses must leverage every tool at their disposal and one such powerful tool that has emerged as a game-changer is real-time data.
Real-time data, with its ability to provide instantaneous insights, has become an invaluable asset for businesses striving to stay ahead. It’s like possessing a compass that guides businesses to make swift and informed decisions.
It is during this expedition to gain a competitive advantage using real-time data that the role of stream processing becomes pivotal. It equips organizations with the ability to handle and process vast volumes of data in real time—an attribute that is not just beneficial, but crucial for success in today’s data-driven Indian business environment.
Most companies operating in 2023, cannot afford to rely on static, outdated information that has lost its relevance. Slower processing done in batches might suffice when time constraints are not a concern or when certain tasks can be deferred. However, for enterprises requiring immediate action based on real-time information, even a brief delay of a few minutes or seconds is unsatisfactory. Such entities thrive on accessing live, continually updated data that is always in motion. The vitality and immediacy of real-time data is what fuels the decision-making processes and ultimately drives success.
Managing Data Resources in The Indian Market
According to the statistics, India accounted for 46 percent of the worldwide real-time payments in 2022, with digital payment transactions surpassing the combined total of the other top four countries. Handling an astronomical number of digital transactions daily, it relies heavily on rapid data processing. Detecting and preventing fraud in the split seconds before the completion of a transaction is of utmost importance for banks and other financial institutions.
For financial institutions like banks, data streaming enables them to seamlessly integrate advanced algorithms and machine learning models, allowing for the continuous monitoring of transactional patterns and the swift detection of any anomalies or suspicious activities. This rapid and efficient data processing not only strengthens their fraud detection mechanisms but also enhances overall customer security and trust, enhancing the firm’s reputation and reliability.
For retailers, real-time identification and prevention of fraudulent transactions at the point of sale, both online and in-store, are crucial for ensuring business integrity and security in the face of sophisticated cyber threats.
To the IT teams managing these operations, the payments are not simply regarded as ‘transactions’ but as individual ‘events’. From their perspective, each transaction comprises a series of diverse ‘events’ where millions of transactions per second translate into billions of ‘events’. All of these have to be processed in real time, and this gargantuan task can only be done effectively using event-driven architecture.
Empowering Real-Time Data Insights and AI Synergy in India’s Tech Landscape
In the vibrant tech landscape of India, event-driven architecture is propelled by two key components: storage and processing, which is covered by Apache Kafka and Apache Flink respectively. These have emerged as the bedrock for efficient stream processing. Apache Kafka paves the way for real-time data pipelines, while Apache Flink stands tall as a robust open-source framework for stream processing.
With the aid of these tools, businesses in India are empowered to efficiently streamline and manage data, thereby unlocking a significant competitive advantage. However, the success of Apache Kafka and Apache Flink is not solely guaranteed by the agility they implement. It also hinges on the company’s ability to analyze the data, interpret the information, and respond effectively to the insights generated.
Adding to a firm’s competitive advantage, the impact of stream processing in enhancing the capabilities of AI models cannot be overlooked. In an era where immediacy is the norm, AI models that can seamlessly incorporate a constant influx of information are essential for delivering precise and timely responses.
Stream processing a game-changer for AI models
While traditional AI models rely on historical and static data, envision an AI model that can learn from and respond to data that’s constantly being updated. Such a model could generate responses that mirror the changes in the data, marking a groundbreaking shift.
This symbiotic relationship between stream processing and AI equips businesses with the power to make informed decisions rooted in the freshest and most pertinent data.
For those in pursuit of a competitive edge driven by data, utilizing the most recent and relevant information becomes imperative. After all, the world undergoes constant change, and data should reflect its dynamic nature.
Consider two retail websites, both employing an AI-backed recommendation system based on weather conditions. However, their approach to data collection differs significantly. The first website relies on batch data, generated and processed overnight. Consequently, visitors receive a recommendation to purchase an umbrella, based on a weather forecast from 24 hours prior.
In contrast, the second website constantly updates its data through latest insights provided by meteorologists. When an overnight shift in weather results in a prediction of sunshine, the website swiftly changes its offers to a range of sunscreens instead.
This exemplifies the advantage of stream processing, underscoring its capacity to enable businesses to make informed decisions, leveraging the most accurate and current information. It is this capability that grants companies the much-needed competitive edge, compelling an increasing number of businesses to turn to stream processing for this crucial advantage.
As more companies in India recognize the transformative potential of stream processing, its adoption continues to grow, cementing its position as an indispensable tool for businesses striving to stay ahead in the data-driven world.
(The author is Srinivasulu Grandhi, Vice President of Engineering and Site Leader at Confluent, and the views expressed in this article are his own)