In this exclusive interview, Rajeev Ranjan, Editor, Digital Terminal exclusively talked to Dhiraj Narang, Director and Head of Partnerships-India, Snowflake, to explore how Snowflake’s robust partner ecosystem is driving data innovation and AI adoption across Indian enterprises. He also discusses the key challenges CIOs and CTOs face in navigating AI and cybersecurity, and offers valuable advice for organizations aiming to thrive in the AI era.
Rajeev: How has Snowflake’s partner ecosystem in India contributed to the company's growth?
Dhiraj: Snowflake’s growth is deeply rooted in the synergies that our partners drive with us. Snowflake’s partner ecosystem plays a pivotal role in helping businesses unlock the power of data. To power our growth, we have made significant investments in developing a robust Snowflake Partner Network (SPN), consisting of a broad array of cloud, services, technology, data providers, advisory firms, system integrators (SIs), and specialized resellers to scale customer outcomes.
Our partners are strategically aligned with Snowflake’s core goals: creating new routes to market, acquiring new customers, helping our joint customers extract value out of their Snowflake investments, and meeting industry-specific needs. Our partners play a critical role in helping organizations build robust data foundations—tackling challenges around governance, security, scalability, and collaboration— and helping unlock the full power of AI.
At Snowflake, we recognize that our success is closely linked to the growth and success of our partner ecosystem. To this end, we have introduced key initiatives e.g. building technical capabilities among partners, fostering innovation through app ecosystem development, and collaborating with resellers and cloud providers. Additionally, Snowflake has also introduced funding programs to help partners uncover new workloads and demonstrate the efficacy of Snowflake's modern data platform.
Rajeev: With nearly 50% of Snowflake’s APJ partner base rooted in India, how are local system integrators, SaaS providers, and ISVs enabling organizations to unlock faster time-to-value and drive meaningful business outcomes?
Dhiraj: For Snowflake, India is a strategic market, and we have many existing valued partners here. The fact that 50% of Snowflake’s APJ partner ecosystem is rooted in India reflects the vast opportunity the region offers and highlights India’s vital role in Snowflake’s global success. Our ecosystem includes advisory leaders like Deloitte, EY and KPMG, global system integrators such as LTIMindtree and TCS, and regional players like Quantiphi, Lumiq, Hoonartek, Kasmo, Rapyder, SaasWorx, and BluePi, who have enabled major enterprises to adopt Snowflake, successfully.
For example, partners such as Quantiphi leverage Snowflake’s platform to deliver customised solutions in areas like generative AI, document processing, and machine learning. Quantiphi’s Baioniq platform, for instance, uses Snowflake’s infrastructure to integrate AI capabilities securely, allowing businesses to fine-tune AI models and apply them in industry-specific contexts. Similarly, Kasmo Digital has built a robust data solution on Snowflake, helping clients unlock predictive analytics and optimise AI/ML workloads to enhance operational efficiency. They have also established a dedicated Snowflake COE focused on creating deep competency in Snowflake solutions/assets to accelerate data migration, analytics implementation, and Generative AI initiatives for our global customers. The COE helps customers to adopt Snowflake for their business-critical challenges by offering a 2-week proof of concept to showcase the value of the Snowflake AI Data Cloud.
We also value our partnerships with cloud providers like AWS and Microsoft and with NVIDIA to strengthen our AI capabilities. These collaborations drive customer success, fuel ecosystem growth, and empower partners to build leading applications and accelerate AI adoption.
We are also investing significantly in the Data Cloud Partnerships ecosystem and working closely with SaaS and ISV companies to build on Snowflake, unlocking new revenue streams through data sharing and the Snowflake marketplace. Through these partnerships, we also address key customer requirements across critical areas such as regulatory reporting, data observability, and customer analytics. We are also focused on building technical capabilities among partners, fostering innovation through app ecosystem development, and collaborating with resellers and cloud providers.
Rajeev: In what ways are Snowflake’s partners helping Indian enterprises modernize their data strategies and scale their AI adoption to build new revenue models?
Dhiraj: Indian businesses are at an inflection point in their journey towards leveraging AI for disruption and business growth. The key lies in adopting a modern data platform and having a robust data strategy. By connecting data securely, efficiently, and making it more accessible, companies can harness the power of AI. Given the rapid evolution of Snowflake as the pre-eminent AI Data Cloud, a major thrust for us has been partner enablement, where we invest significantly in training and certification programs to ensure our partners are equipped to leverage the Snowflake platform. Additionally, our partnership programs focus on the twin objectives of acquiring new customers and driving consumption of our platform, with significant investments going towards those These levers, around deep skilling and investments, enable partners to unlock significant value for our customers.
Our goal is to make the Snowflake Partner Network the number one ecosystem for data apps and AI. Snowflake’s cutting-edge capabilities, such as Cortex Analyst, Cortex AI, and Document AI, are truly helping Indian enterprises accelerate their AI journeys.
In order to make sure that our partners are equipped to be trusted advisors to customers, we are working with them to develop skills on our latest AI capabilities. We have launched a Snowflake AI speciality program for partners with a select group of champions through a 5-month deep dive program to build AI/ML specialization. This initiative blends hands-on learning with Cortex AI, ML, Horizon, Studio, and Document AI across real-world use cases. The program follows structured sessions, hands-on labs, and collaborative milestones with partner experts.This initiative aims to equip partners to deliver governed, production-grade AI solutions to our customers and earn Snowflake-certified Advanced credentials.
To give you an example, Kasmo, our regional implementation partner, is at the forefront of working with Snowflake and our customers in driving innovation. Our joint work with Grow Indigo is a great testimony of AI driving impact at the grassroots level. Before Snowflake, Grow Indigo struggled with slow BI reporting (100 hours monthly), and limited real-time analytics capabilities. After implementing Snowflake, the company achieved:
Near-instant BI report generation (down from 100 hours)
Aims 30% reduction in farmer enrollment time (from 40-60 minutes per farmer)
Centralized data management with real-time analytics
Cost savings through the elimination of replica databases
AI-powered document processing and real-time query capabilities
Scalable architecture supporting carbon program expansion
The company is now implementing Document AI to automate farm boundary validation and transitioning to AI-powered data bots for dynamic information queries, enhancing decision-making across departments.
This is one example of many, where Snowflake’s partners are enabling our customers’ AI journeys. The mutual benefit is evident in the value unlocked for enterprises and the substantial revenue generated for partners alongside Snowflake's expansion.
Rajeev: Can you share some impactful industry-specific use cases—particularly from sectors like BFSI, manufacturing, or retail—where Snowflake partners are delivering significant transformation?
Dhiraj: Our journey in India spans nearly five years, during which we have cultivated a robust and diverse customer base across various sectors such as BFSI, manufacturing, retail and CPG, digital enterprises, and startups.
A key example from the insurance sector is Snowflake’s collaboration with Bajaj Allianz General Insurance, in partnership with Lumiq.ai. Bajaj Allianz General Insurance is leveraging Snowflake’s AI Data Cloud to help its business teams leverage these insights from their data to understand metrics, make informed decisions, and respond swiftly to customer needs. Bajaj Allianz’s transition to Snowflake has augmented its data management approach by integrating data from diverse structured sources into a unified platform. This implementation will help create personalized insurance products, enhance fraud and anomaly detection, and optimize pricing in real-time across its businesses.
In the manufacturing sector, we are working with Deepak Fertilisers and Petrochemicals Corporation Limited (DFPCL), who have adopted Snowflake's AI Data Cloud for Manufacturing to accelerate its digital transformation and build a scalable AI ready data foundation. DFPCL is modernizing its data ecosystem by migrating from its legacy data warehouse to Snowflake. This transition aims to unify real-time insights, and enhance business intelligence, ultimately driving operational efficiency, faster artificial intelligence/machine learning (AI/ML) adoption, and more informed, data-driven decision-making across its various entities.
Snowflake’s AI Data Cloud for Manufacturing empowers DFPCL to collaborate with its ecosystem and customers in a secure and scalable way to drive greater agility and visibility across the value chain. DFPCL can optimize cloud costs using Snowflake’s decoupled storage and compute architecture with a pay-as-you-go model that ensures scalable and cost-effective data management with reduced manual maintenance overhead.
Our partner Quantiphi, along with Snowflake, is helping Chalo, India's leading bus transport technology company, harness AI and digitization to unlock massive value for both passengers and operators. Chalo uses ML and AI solutions and massive real-time and historical data to predict bus ETAs with accuracy. Their prediction model also takes into account the type of bus, the nature of the bus route, the time of the day, and other factors to improve the accuracy of the ETA predicted. Chalo migrated their entire reporting stack to Snowflake as a source of data. With that, they have seen significant improvements in the speed of delivering new reports, insights, and analysis to its customers — without worrying about price or performance.
It has also helped them to generate unified insights by getting app usage, ticketing data and GPS data under one umbrella, which enables them to optimize bus routes and personalize recommendations for the users. Additionally, Snowflake products like Streamlit and Cortex AI allow product and operations teams to query data in natural language. This simplifies complex data analysis tasks and empowers non-technical users to extract insights without the need of deep knowledge of SQL or database structures. Overall, it has helped them strengthen their data-driven culture.
Rajeev: What are the top three challenges that today’s CTOs and CIOs face as they navigate AI adoption and cybersecurity—both of which remain top strategic priorities in the enterprise?
Dhiraj: Data is more powerful — and more vulnerable — than ever. As businesses race to harness AI, integrate data across clouds, and collaborate across borders, they’re also navigating a fast-evolving security landscape shaped by rising threats, stricter regulations, and growing complexity.
Today’s organizations are faced with a variety of data security challenges:
Fragmented tools lead to risk: Many organizations rely on patchwork solutions for data protection — cobbling together services across vendors, regions, and clouds. This not only slows innovation but creates gaps in visibility, governance, and control.
Security gaps delay innovation: Without a unified security and governance foundation, scaling AI or enabling real-time data sharing becomes risky. Organizations are forced to choose between protection and progress.
Cross-cloud and cross-region infrastructure adds pressure: As data strategies span geographies and platforms, so do compliance requirements and potential points of failure.
AI raises the stakes: The rise of generative AI demands access to vast, diverse datasets, but also heightens the risk of data leakage, misuse, and regulatory violations. Without smart, built-in security controls, AI becomes more of a liability than an advantage.
Trust is non-negotiable: Customers, partners, and regulators expect data to be protected by default. Security and governance are no longer check-the-box—they’re critical to operational resilience, reputation, and growth.
Rajeev: Looking ahead, what are the key priorities/advice you want to share with IT Heads / CTOs/CIOs for strengthening their digital transformation journey in the AI era?
Dhiraj: As organizations embark on their digital transformation journeys in the AI era, IT Heads / CTOs/CIOs must prioritize a robust, secure, and scalable data foundation to unlock the transformative power of artificial intelligence (AI). Enterprise AI must be reliable, secure, and, most importantly, built on the organization’s governed data foundation. AI is only as effective as the quality of the data it relies on. Deploying AI without clean, governed and well-structured data is unlikely to succeed or deliver meaningful outcomes. To ensure AI systems are reliable, secure, and trustworthy, businesses must:
Establish strong data governance: Maintain full control over how data is accessed and used, ensuring transparency and trust in AI outputs.
Enforce enterprise-grade encryption: Protect data at rest and in transit using the highest security standards to safeguard sensitive information.
Implement robust access controls: Limit access to critical data and AI models through strict, role-based permissions to prevent unauthorized use.
Monitor and audit: Use real-time monitoring and anomaly detection to ensure AI systems operate safely and within defined parameters.
Ensure transparency and minimize bias: Build and deploy AI models that are explainable, auditable, and fair to reduce the risk of unintended outcomes.
Reduce hallucinations: Invest in systems that can recognize when not to answer a question, minimizing hallucinations and preserving decision-making integrity
Lastly, they must invest in skilling and upskilling their employees to stay ahead in today’s fast-evolving AI era. It is important to identify the right technology partners who can help them navigate the dynamic technology landscape.
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