Harnessing the power of AI and ML for time-critical decision-making and business success

Along the same lines, Deb Dutta, General Manager - Asia Pacific & Japan, DataStax, recently spoke to Dataquest

Supriya Rai
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Deb Dutta General Manager Asia Pacific Japan DataStax 550x300

In today's fast-paced world, where every second counts, AI and ML technologies enable organizations to analyze vast amounts of data, identify patterns, and make informed decisions quickly. By leveraging AI and ML algorithms, businesses can tackle time-sensitive challenges such as detecting fraudulent activities, optimizing supply chains, and attracting customers. These technologies not only provide the ability to choose the best course of action swiftly but also mitigate disruptions and ensure seamless operations. With their ability to process data rapidly and generate valuable insights, AI and ML have become indispensable tools in driving efficiency, enhancing customer experiences, and gaining a competitive edge in the dynamic business landscape. Along the same lines, Deb Dutta, General Manager - Asia Pacific & Japan, DataStax, recently spoke to Dataquest.


DQ: What is the significance of AI/ML in becoming a data-driven Enterprise?

Deb Dutta: In a world where seconds count to ensure business success, artificial intelligence (AI) and machine learning (ML) must be at the forefront of the decision-making process. Indeed, India is expected to become the third-largest spender on AI solutions in the Asia Pacific region at USD3.6 billion by 2026, according to a report by IDC. AI/ML helps solve time-critical challenges, such as finding malicious actors, attracting customers, and improving supply chains. Not only does this enable organisations to choose the best course of action quickly, but it also prevents issues from disrupting operations.

AI and ML have also become foundational in powering data-driven technologies. In edge computing solutions, AI can process data close to the source to generate insights faster. It can also empower chatbots to create more engaging conversations that can attract new or existing customers. Similarly, AI-driven personalisation for cloud-native apps, microservices, and mobile apps can deliver unique experiences, leading to increased retention and loyalty.


Besides that, they also hold the potential to give the edge to businesses as they strive to uncover new trends and opportunities. For example, organisations can employ emerging platforms and technologies to effectively market their products and gain a competitive foothold over other major brands. They can also capitalize on festivities and current events to connect with customers on a personal level.

DQ: The advantages of real-time analytics for organisations that collect IoT data

Deb Dutta: Since Internet of Things (IoT) environments have numerous devices situated across 'edge' environments, real-time analytics can provide organisations with more data points to gain actionable insights and drive growth. This comprehensive view of customer behaviour, operational performance, and external developments enables the right improvements to be made or unique services to be delivered to customers.


Secondly, organisations will be able to resolve issues quickly and maintain operational continuity. Data processing components that are located close to the source will be able to transform raw information into insights at a faster pace without relying on data centres. This is beneficial, especially if organisations are operating in remote locations where network signals are poor and purchasing infrastructure can be a costly and inefficient affair. As a result, organisations can accelerate innovation and position themselves ahead of their rivals, no matter where they are operating from.

Lastly, real-time analytics boosts business' predictive capabilities to anticipate demand peaks and troughs for easy scaling. For example, retail organisations can order a larger quantity of products so that there is enough stock during seasons where demand spikes.

DQ: What is the competitive edge that real-time data provides and how businesses can maintain their lead in the upcoming years? 


Deb Dutta: Real-time data allows businesses to see what is happening in the present and make informed decisions that will boost their resilience and growth, while also engaging customers with the right offer or content at the right time. It also helps businesses prepare for future disruption. Without real-time data, they will be forced to rely on historical data that could be outdated and derail projects.

By enabling businesses to create compelling, in-the-moment digital experiences, real-time data can shed light on search activity and buying behaviour, providing a window into what products and experiences excite and attract customers. Simultaneously, by analysing feedback, they can add or change features to ensure customer satisfaction.

Finally, real-time data empowers automation so that employees can focus on work that actually requires human expertise or tasks that deliver business value. For example, AI-powered chatbots can help answer simple and frequently-asked queries, which can lighten support teams' burdens and allow them to focus on tasks that need the human touch. These features can provide businesses with the speed and performance necessary to stay one step ahead of the competition.


Real-time data is key to enhancing business operations and helping organizations overcome today's challenges. However, harnessing data effectively requires organisations to keep pace and tap into them directly as they move from source to storage. With our data streaming technologies, organisations will be able to harness advanced observability and data processing that will contribute to sustained growth over time.

DQ: Tell us about DataStax’s cloud solution for managing large amounts of real-time data?

Deb Dutta: DataStax Astra DB  provides organisations with capabilities to build and deploy real-time data applications without the need to manage operations. With numerous programming languages, including JSON, REST, and GraphQL, developers can automate query responses or build critical functions that complement existing operations. Scalability and replication are made easy thanks to the serverless architecture of Astra DB (our managed service built on Apache Cassandra®), which eliminates performance issues. What's more, Astra DB ensures that applications are compatible with commonly-used cloud services, including Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure.

In addition, Astra Streaming enables our customers to harness data in-motion. Designed to solve complexity and fragmentation challenges by breaking down silos and unifying data sets, it offers a comprehensive view of the latest trends and status. It also includes in-stream event data processing and fully managed Apache Pulsar connectors to create efficient pipelines capable of extracting insights instantaneously. Real-time actions become visible across all applications, giving teams the ability to work with the same intelligence.

Last but not least, we support our customers with their real-time AI initiatives. As businesses look to fuel their growth and innovation capabilities that are essential to driving businesses forward. With support for Kaskada OSS through our Luna ML offering, customers can design real-time ML and AI initiatives that provide deeper and more accurate predictions so they’re able to act on things like changing customer behaviours, manufacturing operations, supply chains, and more. This feature is paired with unique, time-based approaches that can help employees address critical issues quickly.