Data-to-outcome platforms powered by Gen AI are the future of analytics, streamlining processes and providing industries with a competitive edge over traditional methods of deriving insights and building AI applications.
This is an exclusive interview conducted by the Editor Team of CIO News with Amarjeet Singh Khalsa, Solutions Architect, Gathr Data Inc.
How do you perceive the role of data to outcome platforms in shaping the future of industries dependent on data analytics?
Enterprises are driven by the insights derived from data. To generate insights in real time, it’s essential to have the right set of tools to capture, transform, and analyze the data. The data-to-outcome platform serves as a unified solution for a complete data journey. It enables users to consume data from various sources on a massive scale; this way, users can squeeze insights out of every source of data that is available in enterprises.
Another important point to highlight is the extensibility of these platforms, allowing enterprises to continuously incorporate additional sources, transformations, and targets to capture data and derive insights. This adaptability ensures their resilience to future needs and advancements in data analytics.
In what ways do you believe data-to-outcome platforms are revolutionizing traditional approaches to data analytics within various industries?
The Data to Outcome Platform is a one-stop shop for all your data needs. They have features to support users at every step of their data journey. Right from ingesting raw data by integrating with a heterogeneous source to applying transformation, creating and managing a data catalog, and deriving actionable insights, all in one place. As these are present on a single platform, the transition from one stage to another is seamless. When compared to traditional systems, the data-to-outcomes platform makes actionable insight generation self-served, faster, and more scalable.
From your perspective, what are the key opportunities for different industries as they integrate data-to-outcome platforms?
Many industries are leveraging these platforms to address a variety of use cases. Here are a few examples from the BFSI and airline industries where the data-to-outcome platform plays a crucial role by providing real-time, accurate, and actionable insights:
BFSI:
- RT insider thread detection: As insider threats surge in frequency and complexity, banks are moving from static rule-based alerts to real-time processing of internal activities to detect anomalous patterns and threats.
- Customer lead generation: Send the right offer to the right customer at the right time, and improve customer acquisition.
- Customized pitch generation for customers: Analyse the vast amount of customer data to generate customer clusters and a customized pitch for each customer to provide a personalized experience.
Airlines:
- Agent on Demand: A virtual agent on the Airlines Mobile app that routes multichannel communications to an agent anywhere in the world as needed based on dynamic conditions.
- Travel Ready Center: Power the Airlines mobile app experience to help passengers check in directly with all required documents without involving an on-field agent.
How does the recent launch of Gen AI Fabric contribute to the growth and development?
Recent advancements in Gen AI have prompted all enterprises to seek opportunities to extract insights and build Gen AI solutions from diverse unstructured data sources. Now these solutions could be chat applications, Gen AI agents, creating personalized customer experiences, recommendation engines, and so on. Gen AI Fabric is designed for developing enterprise-grade generative AI solutions, integrating robust data engineering practices, a self-service framework, and comprehensive end-to-end security and compliance measures.
It provides users with the production-ready building blocks for creating Gen AI solutions, with the flexibility to choose from a variety of LLM providers like OpenAI, Azure OpenAI, AWS Bedrock, and so on, or bring their own LLMs. Its integration with a Data to Outcome platform consolidates data acquisition, data preparation, Gen AI integration, prompt refinement, deployment, and monitoring of AI solutions, streamlining the entire process.
Looking ahead, how do you foresee the continued evolution of data to outcome platforms impacting the competitive landscape across different industries?
Data-to-outcome platforms powered by Gen AI are the future of analytics, streamlining processes and providing industries with a competitive edge over traditional methods of deriving insights and building AI applications.
By consolidating all functions into one platform, these solutions simplify the tech stack, making management easier and significantly reducing operational costs while greatly enhancing productivity.
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