Rohit Choudhary is the founder and CEO of Acceldata, the market leader in enterprise data observability. He founded Acceldata in 2018, when he realized that the industry needed to reimagine how to monitor, investigate, remediate, and manage the reliability of data pipelines and infrastructure in a cloud first, AI enriched world.
What inspired you to focus on data observability when you founded Acceldata in 2018, and what gaps in the data management industry did you aim to fill?
My journey to founding Acceldata in 2018 began nearly 20 years ago as a software engineer, where I was driven to identify and solve problems with software. My experience as Director of Engineering at Hortonworks exposed me to a recurring theme: companies with ambitious data strategies were struggling to find stability in their data platforms, despite significant investments in data analytics. They couldn’t reliably deliver data when the business needed it most.
This challenge resonated with my team and me, and we recognized the need for a solution that could monitor, investigate, remediate, and manage the reliability of data pipelines and infrastructure. Enterprises were trying to build and manage data products with tools that weren’t designed to meet their evolving needs—leading to data teams lacking visibility into mission-critical analytics and AI applications.
This gap in the market inspired us to start Acceldata, with the goal of developing a comprehensive and scalable data observability platform. Since then, we’ve transformed how organizations develop and operate data products. Our platform correlates events across data, processing, and pipelines, providing unparalleled insights. The impact of data observability has been immense, and we’re excited to keep pushing the industry forward.
Having coined the term “Data Observability,” how do you see this concept evolving over the next few years, especially with the increasing complexity of multi-cloud environments?
Data observability has evolved from a niche concept into a critical capability for enterprises. As multi-cloud environments become more complex, observability must adapt to handle diverse data sources and infrastructures. Over the next few years, we anticipate AI and machine learning playing a key role in advancing observability capabilities, particularly through predictive analytics and automated anomaly detection.
In addition, observability will extend beyond monitoring into broader aspects of data governance, security, and compliance. Enterprises will demand more real-time control and insight into their data operations, making observability a vital part of managing data across increasingly intricate environments.
Your background includes significant experience in engineering and product development. How has this experience shaped your approach to building and scaling Acceldata?
My engineering and product development background has been pivotal in shaping how we’ve built Acceldata. Understanding the technical challenges of scaling data systems has allowed us to design a platform that addresses the real-world needs of enterprises. This experience has also instilled the importance of agility and customer feedback in our development process. At Acceldata, we prioritize innovation, but we always ensure our solutions are practical and aligned with what customers need in dynamic, complex data environments. This approach has been essential to scaling the company and expanding our market presence globally.
With the recent $60 million Series C funding round, what are the key areas of innovation and development you plan to prioritize at Acceldata?
With the $60 million Series C funding, we’re doubling down on AI-driven innovations that will significantly differentiate our platform. Building on the success of our AI Copilot, we’re enhancing our machine learning models to deliver more precise anomaly detection, automated remediation, and cost forecasting. We’re also advancing predictive analytics, where AI not only alerts users to potential issues but also suggests optimal configurations and proactive solutions, specific to their environments.
Another key focus is context-aware automation—where our platform learns from user behavior and aligns recommendations with business goals. The expansion of our Natural Language Interfaces (NLI) will enable users to interact with complex observability workflows through simple, conversational commands.
Additionally, our AI innovations will drive even greater cost optimization, forecasting resource consumption and managing costs with unprecedented accuracy. These advancements position Acceldata as the most proactive, AI-powered observability platform, helping enterprises trust and optimize their data operations like never before.
AI and LLMs are becoming central to data management. How is Acceldata positioning itself to lead in this space, and what unique capabilities does your platform offer to enterprise customers?
Acceldata is already leading the way in AI-powered data observability. Following the successful integration of Bewgle’s advanced AI technology, our platform now offers AI-driven capabilities that significantly enhance data observability. Our AI Copilot uses machine learning to detect anomalies, predict cost consumption patterns, and deliver real-time insights, all while making these functions accessible through natural language interactions.
We’ve also integrated advanced anomaly detection and automated recommendations that help enterprises prevent costly errors, optimize data infrastructure, and improve operational efficiency. Furthermore, our AI solutions streamline policy management and automatically generate human-readable descriptions for data assets and policies, bridging the gap between technical and business stakeholders. These innovations enable organizations to unlock the full potential of their data while minimizing risks and costs.
The acquisition of Bewgle has added advanced AI capabilities to Acceldata’s platform. Now that a year has passed since the acquisition, how has Bewgle’s technology been incorporated into Acceldata’s solutions, and what impact has this integration had on the development of your AI-driven data observability features?
Over the past year, we’ve fully integrated Bewgle’s AI technologies into the Acceldata platform, and the results have been transformative. Bewgle’s experience with foundational models and natural language interfaces has accelerated our AI roadmap. These capabilities are now embedded in our AI Copilot, delivering a next-generation user experience that allows users to interact with data observability workflows through plain text commands.
This integration has also improved our machine learning models, enhancing anomaly detection, automated cost forecasting, and proactive insights. We’ve been able to deliver more granular control over AI-driven operations, which empowers our customers to ensure data reliability and performance across their ecosystems. The success of this integration has strengthened Acceldata’s position as the leading AI-powered data observability platform, providing even greater value to our enterprise customers.
As someone deeply involved in the data management industry, what trends do you foresee in the AI and data observability market in the coming years?
In the coming years, I expect a few key trends to shape the AI and data observability market. Real-time data observability will become more critical as enterprises look to make faster, more informed decisions. AI and machine learning will continue to drive advancements in predictive analytics and automated anomaly detection, helping businesses stay ahead of potential issues.
Additionally, we’ll see a tighter integration of observability with data governance and security frameworks, especially as regulatory requirements grow stricter. Managed observability services will likely rise as data environments become more complex, giving enterprises the expertise and tools needed to maintain optimal performance and compliance. These trends will elevate the role of data observability in ensuring that organizations can scale their AI initiatives while maintaining high standards for data quality and governance.
Looking ahead, how do you envision the role of data observability in supporting the deployment of AI and large language models at scale, especially in industries with stringent data quality and governance requirements?
Data observability will be pivotal in deploying AI and large language models at scale, especially in industries like finance, healthcare, and government, where data quality and governance are paramount. As organizations increasingly rely on AI to drive business decisions, the need for trustworthy, high-quality data becomes even more critical.
Data observability ensures the continuous monitoring and validation of data integrity, helping prevent errors and biases that could undermine AI models. Additionally, observability will play a vital role in compliance by providing visibility into data lineage, usage, and governance, aligning with strict regulatory requirements. Ultimately, data observability enables organizations to harness the full potential of AI, ensuring that their AI initiatives are built on a foundation of reliable, high-quality data.
Thank you for the great interview, readers who wish to learn more should visit Acceldata.
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