How is DataQuark evolving data analytics in 2025 for large and medium enterprises in India?

Vinay: The data analytics landscape in India is evolving at a very fast pace, with enterprises recognising that their ability to extract value from data will define their competitive edge. With the market projected to exceed USD 21.3 billion by 2030, businesses are eager to leverage AI-driven analytics to unlock real-time insights, automate decision-making, and drive measurable impact. However, despite this growing dependence on data, many enterprises continue to struggle with fragmented ecosystems, outdated analytics models, and an inability to process and activate insights at scale. The biggest challenge is not a lack of data but the inability to unify, process, and activate it in a way that actions intelligent, real-time business actions.

DataQuark’s mission is to lead this transformation by helping enterprises to move away from static, siloed, and retrospective analyticstoward real-time, AI-powered decision intelligence. Traditional analytics platforms emphasise descriptive insights, delivering a backward-looking perspective with limited strategic value. In contrast, DataQuark empowers businesses to move beyond traditional analysis, embracing predictive and prescriptive analytics to activate data in real time. This transformation is driven by cloud-native, AI-powered architectures that effortlessly ingest, process, and refine structured and unstructured data from diverse sources. Instead of waiting for batch-processed reports, businesses can now access real-time, event-driven intelligence that dynamically adjusts to market shifts, consumer behaviours, and operational needs.

Beyond speed and scalability, DataQuark is also solving one of the biggest pain points enterprises face today, data privacy and compliance. With regulations like India’s DPDP Act, GDPR, and CCPA setting new benchmarks for how businesses handle consumer data, companies need AI-driven solutions that ensure compliance while still delivering deep insights. DataQuark’s Data Clean Room technology allows enterprises to collaborate on insights without exposing sensitive data, enabling compliance-first analytics that meets global regulatory standards.

As Indian businesses accelerate their adoption of Generative AI, hyper-automation, and intelligent data orchestration, DataQuark will ensure that enterprises do not just collect and analyse data but activate it in a way that creates tangible business outcomes. By eliminating silos, enabling real-time, privacy-first AI analytics, and ensuring seamless integration with cloud ecosystems, DataQuark is setting the benchmark for the next era of data-driven transformation.

How does DataQuark position itself against much larger competitors in the data analytics and AI-driven insights space?

Vinay: The data analytics industry is filled with large-scale enterprise players offering extensive but rigid, one-size-fits-all solutions that struggle to adapt to the unique requirements of businesses. These legacy platforms often require significant customisation, heavy infrastructure investments, and long deployment cycles, making them impractical for businesses that require agility, real-time insights, and domain-specific intelligence. Many enterprises find themselves locked into static, retrospective analytics, where they spend more time managing data rather than extracting value from it.

DataQuark disrupts this conventional model by offering an AI-native, highly adaptable, and industry-specific approach to analytics. Unlike large players that focus on broad, generic analytics platforms, DataQuark has built an ecosystem where data is collected, processed, and activated in real-time, ensuring that insights do not remain trapped in dashboards but are actively shaping business strategies, marketing campaigns, and operational efficiencies. This agility is made possible by serverless, cloud-native architectures that enable dynamic scaling, automated data transformation, and self-optimising AI models. Rather than forcing businesses to adapt to rigid systems, DataQuark’s infrastructure evolves in real-time, continuously learning and improving without the need for manual intervention.

One of the key differentiators of DataQuark is its deep industry specialisation, ensuring that businesses in sectors such as BFSI, Retail, FMCG, and Manufacturing do not need to build custom AI models from scratch. Instead, DataQuark provides pre-built AI accelerators that are designed specifically for the challenges these industries face, drastically reducing implementation timelines and unlocking rapid time-to-value. While global competitors struggle with privacy-first analytics and data-sharing compliance, DataQuark’s Data Clean Rooms provide a secure, regulation-compliant framework for AI-driven collaboration, ensuring businesses can generate actionable intelligence without compromising data privacy or security.

By combining AI-driven automation, real-time insights, and regulatory compliance with a strong industry focus, DataQuark is not just another analytics provider, we are reshaping how enterprises leverage AI to drive impact at scale.

How does the “Collect – Collate – Activate” methodology drive measurable marketing results across industries?

Vinay: In today’s hyper-digital landscape, the ability to collect data is no longer a challenge but activating it in real time is. Businesses struggle with siloed, disconnected data sources that lead to delays in decision-making, incomplete customer insights, and ineffective marketing campaigns. DataQuark’s “Collect – Collate – Activate” methodology is designed to bridge this gap, ensuring that data flows seamlessly from ingestion to action without delays or inefficiencies.

The process starts with Collect, where DataQuark aggregates data from multiple sources, including web analytics, CRM systems, IoT devices, social platforms, and advertising networks, consolidating it into a unified real-time data lake. The Collate phase applies AI-driven identity resolution, data normalisation, and predictive analytics, creating a single, accurate, and structured dataset. The Activate phase ensures that insights are not trapped in reports but are automatically fed into marketing, sales, and customer engagement platforms, triggering AI-powered, real-time decision-making.

This methodology delivers measurable impact across industries. In retail and eCommerce, it enables hyper-personalised campaigns and real-time demand forecasting. In BFSI, it enhances predictive lead scoring and customer retention strategies. In FMCG and CPG, it optimises marketing attribution and inventory planning. By eliminating the barriers that slow down data-driven execution, DataQuark ensures that enterprises move faster, engage better, and drive greater marketing ROI.

Generative AI and machine learning are pivotal in your strategy. Could you share a concrete example where these technologies have significantly enhanced campaign effectiveness or customer targeting?

Vinay: Generative AI and machine learning are no longer just enhancements to marketing strategies, they are becoming the foundation of effective customer engagement and business growth. Traditional segmentation models, which rely on static demographic attributes, struggle to capture the nuance of consumer behaviour, real-time intent, and long-term value potential. To stay competitive, businesses need AI-driven intelligence that continuously learns, adapts, and refines customer targeting strategies.

A good example of this transformation is a leading life insurance company in India that partnered with DataQuark to address its challenges in customer acquisition and policy sales. Despite having extensive data across CRM systems, call centres, and digital touchpoints, the company lacked a unified, actionable view of its customers. The fragmentation of data led to inefficiencies in lead conversion, redundant marketing efforts, and an inability to personalise offerings at scale.

DataQuark implemented a machine learning-powered segmentation and predictive analytics framework, designed to extract 39 distinct parameters from 30+ data sources. Advanced clustering techniques, including K-Means, hierarchical models, and psychographic segmentation, enabled the creation of multi-dimensional customer cohorts. These cohorts allowed for precise identification of high-value leads, churn-prone customers, and conversion-ready segments.

The results were transformative. By integrating AI-powered segmentation into its acquisition and engagement strategy, the insurer achieved:

Higher lead-to-policy conversion rates through targeted engagement with high-intent customers.

Improved marketing efficiency, reducing spend on ineffective outreach while reallocating budgets based on real-time AI-driven insights.

Enhanced personalisation through tailored product recommendations and dynamic customer communication, optimised for relevance and impact.

Proactive churn management, identifying at-risk policyholders early and implementing retention strategies powered by predictive analytics.

This case study endorses the strategic shift AI is driving in customer engagement, from reactive, rules-based marketing to dynamic, self-optimising systems that continuously refine customer interactions. Businesses that embrace this intelligence layer are no longer just running campaigns, they are building AI-powered decision engines that drive measurable ROI, sustainable customer relationships, and long-term market leadership.

What was the need to launch a new vertical? What is the expansion vision for DataQuark, and how does it align with LS Digital’s broader growth strategy?

Prasad: The launch of DataQuark was driven essentially by the shift in the way businesses approach AI, data intelligence, and digital transformation. While LS Digital has already built a strong reputation for its expertise in digital business transformation (DBT), it became clear that enterprises needed a dedicated AI-driven analytics entity capable of managing the growing complexity of data ecosystems. Businesses today are no longer satisfied with traditional, static analytics dashboards; they require real-time, AI-driven insights that power automation, personalisation, and predictive decision-making.

This need has been amplified by the increasing complexity of consumer journeys across multiple digital touchpoints. Brands today must navigate fragmented data sources, shifting regulatory landscapes, and rising expectations for seamless, hyper-personalised experiences. Privacy regulations such as India’s DPDP Act, GDPR, and CCPA are making compliance-driven data strategies a necessity. Traditional analytics tools, designed for batch processing and siloed data workflows, are proving inadequate for modern businesses that require real-time, AI-powered intelligence.

Recognising these challenges, we introduced DataQuark as a global AI-driven analytics powerhouse, expanding into markets including the US, UK, UAE, and Australia. This expansion is backed by significant investments in AI/ML research and development, industry-specific analytics frameworks, and cloud-native AI solutions that seamlessly integrate into enterprise ecosystems. Partnerships with AWS, Google Cloud, and Microsoft Azure further strengthen DataQuark’s ability to offer scalable, AI-embedded analytics solutions that do not just process data but actively shape business outcomes.

By aligning AI-powered analytics with LS Digital’s broader digital transformation strategy, DataQuark will ensure that enterprises do not just manage data but activate it at the speed of business, transforming how decisions are made, campaigns are optimised, and customers are engaged.

As LS Digital positions DataQuark as a key business unit, what investments in technology, talent, and infrastructure are being made to ensure it remains at the forefront of digital transformation and drives long-term ROI?

Prasad: As LS Digital strengthens its positioning in AI-driven data analytics, it is making strategic investments in technology, talent, and infrastructure to ensure DataQuark remains at the forefront of digital transformation. The global data analytics market is experiencing a major shift, with businesses demanding real-time, AI-powered decision making, privacy-first data management, and industry-specific AI accelerators. The rapid advancement of GenAI, hyper-automation, and cloud-based analytics is reshaping how organisations extract value from their data, making it imperative for companies to adopt scalable, secure, and intelligent data ecosystems.

Technology remains the backbone of DataQuark’s competitive edge. LS Digital has invested heavily in cloud-native, serverless data architectures that allow enterprises to process and activate insights in real time​. By integrating AI-powered tools, businesses gain end-to-end visibility into their data pipelines, ensuring high performance and governance. The company has also developed LLM-powered analytics engines, enabling enterprises to automate data storytelling, interpretation, and trend prediction​. To address growing regulatory concerns, DataQuark has built a privacy-first AI security framework that ensures full compliance with GDPR, CCPA, and India’s DPDP Act, allowing businesses to extract insights while safeguarding sensitive information​.

Talent remains an important pillar of DataQuark’s expansion strategy. The company is actively hiring top-tier AI/ML engineers, data scientists, and cloud architects to drive continuous innovation. A structured upskilling program focused on MLOps, cloud data frameworks, and next GenAI models ensures that DataQuark’s teams stay ahead of emerging technological advancements​. This focus on talent enables DataQuark to build industry-specific AI accelerators that cater to the unique data challenges of BFSI, Retail, Manufacturing, and FMCG enterprises​.

Investments in infrastructure are shaping DataQuark’s future as a global leader in AI. The company has built an AI-powered data fabric, ensuring enterprises can scale their data infrastructure without performance bottlenecks​. Strategic partnerships with AWS, Google Cloud, and Microsoft Azure have helped us to embed AI-driven analytics directly into enterprise cloud environments, resulting in seamless adoption​. Additionally, automated data pipelines and intelligent data coordination have been implemented, streamlining processes and enabling businesses to move from static reporting to real-time, AI-driven decision intelligence.

By aligning DataQuark’s AI-driven analytics with LS Digital’s broader digital business transformation expertise, the company is ensuring that enterprises no longer struggle with data complexity but leverage AI-powered insights for measurable business impact. These investments strengthen DataQuark’s position as India’s most advanced AI-driven analytics company, delivering high-impact, scalable, and privacy-compliant AI solutions that drive long-term ROI.