Dive into how Promethium’s approach to AI-driven data discovery and governance accelerates business outcomes and self-service analytics, ensuring secure, scalable insights.
Welcome, Prat. Could you tell us more about your role at Promethium and how your professional journey has brought you here?
As CEO of Promethium, I work with our team to partner with enterprises to rapidly access and analyze data with trusted AI to build new outcomes that will grow their business. My previous journey with early stage, growth stage and/or public companies such as Cazena, Cloudera, Netezza or IBM has always revolved around innovation at the intersection of data, analytics, AI, and business. Prior to Promethium, I’ve held leadership roles in scaling SaaS and data-driven businesses, where I saw first-hand the challenges enterprises face in leveraging data. Our vision at Promethium addresses these challenges and provides instant access to insights through self-service and AI-driven solutions.
Depending upon your background, could you share the strategies that have consistently driven significant growth when leading a SaaS or data analytics portfolio?
In general, the key to delivering value and driving growth in SaaS or data analytics revolves around three themes: Run, Grow, and Protect. Each of these plays a distinct role:
- Run focuses on ensuring operational excellence and efficiency, enabling organizations to maintain their current systems and processes with reliability.
- Protect ensures that governance, security, and compliance are prioritized, safeguarding the organization’s data and trust.
- Grow is about creating new opportunities and accelerating business outcomes through innovation and insights.
At Promethium, we are squarely focused on the Grow aspect. Our goal is to enable organizations to gain insights they couldn’t before and to do so at unprecedented speed. By empowering companies to make data-driven decisions faster and more effectively, we’re not just helping them grow but also unlocking value that directly impacts their competitive edge. This focus on Grow aligns deeply with how modern organizations want to use data – not just to run and protect their business, but to drive innovation and transformation.
How does data governance adapt to support self-service analytics while ensuring data integrity and compliance, and what are the main challenges when trying to balance this?
Data governance must shift from being a restrictive gatekeeper to becoming an enabler of self-service analytics. A key theme here is embedded governance – the idea that governance should not be an afterthought or a separate layer but seamlessly integrated into the tools and workflows users rely on.
With embedded governance, compliance, access controls, data quality, and integrity checks are built directly into the user experience, ensuring that individuals can interact with data confidently and securely without needing to navigate complex governance processes. This approach allows organizations to democratize data access while maintaining trust and control.
The main challenge lies in balancing accessibility with security. Users need to explore and use data freely, but this must happen within a framework that safeguards compliance and ensures the reliability of insights. Achieving this balance requires intelligent automation, such as leveraging active metadata to provide context and visibility without overwhelming users with technical complexity.
At Promethium, embedded governance is core to our approach. By integrating governance into every layer of our data fabric, we empower users to access and analyze data confidently, regardless of their technical expertise, while ensuring the organization maintains enterprise-grade governance standards. This enables true self-service analytics without sacrificing control or compliance,
How do you at Promethium make sure that you address governance concerns while still enabling self-service for users of varying technical expertise?
At Promethium, we ensure governance and self-service go hand in hand through embedded governance. This means that governance is seamlessly integrated into the user experience rather than being a separate or restrictive process.
We inherit all existing access controls already defined by the organization, ensuring that the permissions and policies in place are respected without requiring additional manual configuration. On top of this, Promethium creates a governed layer specifically designed to interact with AI, ensuring that all AI-driven insights and interactions are secure, transparent, and compliant with enterprise standards.
By embedding governance into every layer of our platform, we enable users – whether technical or non-technical – to access and analyze data with confidence. Features like automated lineage tracking, role-based access controls, and real-time compliance checks are seamlessly built into the workflow. This ensures that users have the freedom to explore and leverage data while organizations retain full visibility and control over how data is accessed and used.
How do you see Gen AI and Co-pilot solutions transforming the way enterprises discover and analyze data? What key factors are required for scaling Gen AI and Copilot solutions in large enterprises?
Gen AI and Co-pilot solutions are set to revolutionize how enterprises discover and analyze data by acting as intelligent assistants that automate routine tasks, streamline data exploration, and democratize access to insights. These tools will not only make data more accessible to non-technical users but also empower technical teams to focus on high-impact strategic initiatives.
In 2025, we predict that data teams leveraging AI-driven copilots and agents will experience a significant boost in productivity – potentially doubling their output. These tools will close skill gaps, allowing data professionals to evolve into “data heroes” who seamlessly combine the roles of data analysts, analytics engineers, and business analysts. By blurring the boundaries between these roles, these heroes will lead strategic conversations and enable faster, more collaborative, and impactful decision-making across the organization.
Scaling these solutions in large enterprises requires three key factors:
- Seamless Integration with Existing Ecosystems: Copilots must connect securely and effectively to diverse and distributed data sources while respecting established access controls.
- Embedded Governance: Ensuring compliance and trust is critical, especially when interacting with AI tools.
- Iterative Learning and Adaptation: Copilots need to learn from user interactions and continuously improve, delivering increasingly relevant insights over time.
Promethium has already embraced this transformative potential with its Enterprise Data Copilot, currently in private preview. This innovative tool enables organizations to discover, access, and analyze disparate data using natural language, empowering users of all technical levels. By integrating AI-driven capabilities with our robust data fabric, Promethium ensures that enterprises can scale these solutions securely and effectively while unlocking unprecedented productivity and innovation.
How does Gen AI contribute to intelligent data discovery, and what role does it play in ensuring data quality and relevance?
Gen AI transforms data discovery by automating the process of identifying, contextualizing, and recommending the most relevant datasets. By understanding natural language queries and mapping them to the right data sources, it eliminates the complexity of manual searches and accelerates time to insight. Text-to-SQL is a core capability to democratize access to data and analytics.
Active metadata is a key enabler in the process. It provides rich context about data – such as origins, relationships, and usage patterns. Gen AI leverages this metadata to ensure the relevance of data surfaced during discovery, enabling users to focus on what truly matters.
At Promethium, AI is baked into every part of the workflow, from discovery to analysis. By integrating AI seamlessly into the platform, we enable organizations to unlock the full potential of their data while maintaining governance and trust, all with full explainability. This holistic approach ensures that data discovery is not only intelligent but also aligned with enterprise needs for accuracy, relevance, and compliance.
What key components are needed to build a robust data fabric that supports governance, and how does Promethium’s Data Fabric provide a scalable foundation for AI adoption in enterprises?
Building a robust data fabric requires a combination of foundational capabilities that address discovery, access, governance, and scalability. Key components include:
- Data Catalog for Discovery: A comprehensive and intelligent catalog is essential for helping users locate and understand available data. It should include active metadata, providing context such as data lineage, relationships, and quality, to guide users to the most relevant datasets.
- Immediate Query and Access Across Disparate Data Sources: The ability to virtualize access to data, regardless of its location or format, is critical as well. A data fabric should enable real-time queries without requiring data movement, ensuring faster insights while minimizing duplication and cost.
- Embedded Governance: Governance must be integrated directly into the data fabric, encompassing features like role-based access control, fine-grained policies, and end-to-end data lineage. This ensures data is accessed securely and used responsibly, even in complex environments.
- Scalability Across Hybrid and Multi-Cloud Environments: A data fabric must seamlessly integrate with diverse infrastructures, including on-premises, hybrid, and multi-cloud setups, to provide consistent access and governance across all environments.
Promethium’s Data Fabric brings these capabilities together into a single platform that not only supports governance but also lays the foundation for AI adoption at scale. By enabling intelligent discovery through an active metadata-driven data catalog, it provides immediate query and access to distributed data, and embeds governance into every interaction. Promethium ensures that organizations can securely and effectively leverage AI to drive innovation and insights. These capabilities empower enterprises to harness the full potential of their data while maintaining the trust and compliance needed for sustainable AI adoption.
How does the Data Fabric model help manage the growing complexity of data environments, especially in hybrid and multi-clou
The Data Fabric model simplifies the management of complex, distributed data environments by providing unified access, embedded governance, and scalability across hybrid and multi-cloud setups. It virtualizes access to data, enabling real-time querying without duplication or movement, while leveraging active metadata to offer context, lineage, and intelligence. Embedded governance ensures compliance and security, even in diverse infrastructures, while scalability ensures consistent performance as environments grow.
Promethium’s Data Fabric addresses these challenges by breaking down silos, enabling secure and immediate access to data, and providing a strong foundation for AI-driven insights across hybrid and multi-cloud architectures. This allows enterprises to reduce complexity, enhance agility, and drive innovation effectively.
What vision do you have for the convergence of AI, data governance, and self-service analytics shaping the next wave of enterprise data solutions?
The convergence of AI, data governance, and self-service analytics represents a transformative shift in how enterprises interact with and derive value from their data. This evolution is driving a new era of enterprise data solutions characterized by speed, trust, and accessibility.
AI will become deeply embedded in workflows, automating routine tasks, enhancing discovery, and making analytics more intuitive for all users, regardless of technical expertise. Governance, rather than acting as a barrier, will be seamlessly integrated into these workflows, ensuring compliance, security, and trust without slowing down innovation. Self-service analytics will empower business users to access and analyze data independently, enabling faster, data-driven decisions while reducing the burden on IT teams.
Our vision is that every employee will be empowered to make data-driven decisions, regardless of their level of technical expertise. By combining AI, governance, and self-service in a seamless and intuitive way, we aim to transform data into a strategic asset that drives innovation and accelerates business success.
What advice would you give to organizations looking to adopt Gen AI and Co-pilot solutions for data discovery while ensuring they maintain data governance and compliance standards?
Adopting Gen AI and Co-pilot solutions for data analytics requires a balanced approach that prioritizes innovation while maintaining governance and compliance. Here’s what organizations should focus on:
- Establish a Governed Data Foundation: Start with a solid foundation of organized and governed data. Tools like data catalogs and active metadata enable clear visibility into your data landscape, ensuring that analytics processes are built on trusted and relevant data.
- Embed Governance into the Analytics Workflow: Choose solutions that integrate governance at every step of the analytics process. This includes features like role-based access control, audit trails, and data lineage tracking, ensuring compliance while making analytics workflows seamless for users.
- Use AI to Enhance Accuracy and Relevance: Leverage AI to improve not only the discovery of datasets but also the relevance and quality of analytics outputs. AI-driven recommendations and explainability ensure that analytics results are transparent and aligned with business goals.
- Empower Non-Technical Users Without Compromising Control: Focus on solutions that make advanced analytics accessible to users of all technical levels while maintaining centralized oversight. This ensures that insights are widely available without risking compliance breaches.
- Start with Specific Use Cases and Scale: Identify high-value analytics use cases to pilot Gen AI and Co-pilot solutions. Prove their value in areas like accelerating decision-making or optimizing reporting, then gradually scale across departments or functions.
Organizations that integrate governance and compliance into their Gen AI-powered analytics strategies can unlock transformative potential. By enabling broader access to analytics while ensuring trust and control, they can empower all employees to derive meaningful insights and drive better decision-making across the enterprise.
Prat Moghe,
Chief Executive Officer, Promethium
Prat Moghe is a successful founder, entrepreneur and an executive with experience scaling multiple startups and public companies. His recent roles include EVP at Cloudera, Founder & CEO at Cazena, and previously SVP at IBM Netezza. He has launched and scaled category-creating products and businesses that benefited hundreds of enterprise customers globally. Prat has been a thought leader in industries including data and analytics, AI/ML, security and networking. He is also an advisor and mentor to many entrepreneurs and startups, and was the President of TIE Boston and a Trustee of TIE Board of Trustees. Prat holds a PhD from UCLA in Electrical Engineering.
About Promethium
Backed by top investors including Insight Partners, .406 Ventures, and Zetta Venture Partners, Promethium is the leading provider of industry’s first Instant Data Fabric. Designed to revolutionize the way businesses manage, discover, and utilize their data, Promethium empowers organizations to streamline their data management processes, enhance analytical capabilities, and drive informed decision-making. With its innovative solutions, Promethium provides a single, unified, consistent view and access to all data from across multiple sources, enabling customers to find new insights and answer pressing business questions while reducing project backlogs and decreasing the total cost of ownership. To learn more visit https://www.promethium.ai or follow on LinkedIn or Twitter.
